org.lwjgl.cuda

Class CU

java.lang.Object

org.lwjgl.cuda.CU

Direct Known Subclasses:

CU40

public class CU
extends java.lang.Object

Contains bindings to the CUDA Driver API.

This class includes functionality up to CUDA version 3.2, which is the minimum version compatible with the LWJGL bindings.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	CU.Functions Contains the function pointers loaded from the NVCUDA SharedLibrary.

Field Summary

Fields

Modifier and Type	Field and Description
static int	CU_AD_FORMAT_FLOAT Array formats.
static int	CU_AD_FORMAT_HALF Array formats.
static int	CU_AD_FORMAT_SIGNED_INT16 Array formats.
static int	CU_AD_FORMAT_SIGNED_INT32 Array formats.
static int	CU_AD_FORMAT_SIGNED_INT8 Array formats.
static int	CU_AD_FORMAT_UNSIGNED_INT16 Array formats.
static int	CU_AD_FORMAT_UNSIGNED_INT32 Array formats.
static int	CU_AD_FORMAT_UNSIGNED_INT8 Array formats.
static int	CU_COMPUTEMODE_DEFAULT Compute Modes.
static int	CU_COMPUTEMODE_EXCLUSIVE_PROCESS Compute Modes.
static int	CU_COMPUTEMODE_PROHIBITED Compute Modes.
static int	CU_CTX_BLOCKING_SYNC Context creation flags.
static int	CU_CTX_FLAGS_MASK Context creation flags.
static int	CU_CTX_LMEM_RESIZE_TO_MAX Context creation flags.
static int	CU_CTX_MAP_HOST Context creation flags.
static int	CU_CTX_SCHED_AUTO Context creation flags.
static int	CU_CTX_SCHED_BLOCKING_SYNC Context creation flags.
static int	CU_CTX_SCHED_MASK Context creation flags.
static int	CU_CTX_SCHED_SPIN Context creation flags.
static int	CU_CTX_SCHED_YIELD Context creation flags.
static int	CU_CUBEMAP_FACE_NEGATIVE_X Array indices for cube faces.
static int	CU_CUBEMAP_FACE_NEGATIVE_Y Array indices for cube faces.
static int	CU_CUBEMAP_FACE_NEGATIVE_Z Array indices for cube faces.
static int	CU_CUBEMAP_FACE_POSITIVE_X Array indices for cube faces.
static int	CU_CUBEMAP_FACE_POSITIVE_Y Array indices for cube faces.
static int	CU_CUBEMAP_FACE_POSITIVE_Z Array indices for cube faces.
static int	CU_DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT Device properties.
static int	CU_DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES Device properties.
static int	CU_DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY Device properties.
static int	CU_DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER Device properties.
static int	CU_DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS Device properties.
static int	CU_DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM Device properties.
static int	CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS Device properties.
static int	CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR Device properties.
static int	CU_DEVICE_ATTRIBUTE_CLOCK_RATE Device properties.
static int	CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR Device properties.
static int	CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR Device properties.
static int	CU_DEVICE_ATTRIBUTE_COMPUTE_MODE Device properties.
static int	CU_DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED Device properties.
static int	CU_DEVICE_ATTRIBUTE_CONCURRENT_KERNELS Device properties.
static int	CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS Device properties.
static int	CU_DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH Device properties.
static int	CU_DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH Device properties.
static int	CU_DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST Device properties.
static int	CU_DEVICE_ATTRIBUTE_ECC_ENABLED Device properties.
static int	CU_DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED Device properties.
static int	CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH Device properties.
static int	CU_DEVICE_ATTRIBUTE_GPU_OVERLAP Device properties.
static int	CU_DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED Device properties.
static int	CU_DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED Device properties.
static int	CU_DEVICE_ATTRIBUTE_INTEGRATED Device properties.
static int	CU_DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT Device properties.
static int	CU_DEVICE_ATTRIBUTE_L2_CACHE_SIZE Device properties.
static int	CU_DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED Device properties.
static int	CU_DEVICE_ATTRIBUTE_MANAGED_MEMORY Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_X Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAX_PITCH Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH Device properties.
static int	CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH Device properties.
static int	CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE Device properties.
static int	CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD Device properties.
static int	CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID Device properties.
static int	CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT Device properties.
static int	CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS Device properties.
static int	CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES Device properties.
static int	CU_DEVICE_ATTRIBUTE_PCI_BUS_ID Device properties.
static int	CU_DEVICE_ATTRIBUTE_PCI_DEVICE_ID Device properties.
static int	CU_DEVICE_ATTRIBUTE_PCI_DOMAIN_ID Device properties.
static int	CU_DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK Device properties.
static int	CU_DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK Device properties.
static int	CU_DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO Device properties.
static int	CU_DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED Device properties.
static int	CU_DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT Device properties.
static int	CU_DEVICE_ATTRIBUTE_TCC_DRIVER Device properties.
static int	CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT Device properties.
static int	CU_DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT Device properties.
static int	CU_DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY Device properties.
static int	CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING Device properties.
static int	CU_DEVICE_ATTRIBUTE_WARP_SIZE Device properties.
static int	CU_EVENT_BLOCKING_SYNC Event creation flags.
static int	CU_EVENT_DEFAULT Event creation flags.
static int	CU_EVENT_DISABLE_TIMING Event creation flags.
static int	CU_EVENT_INTERPROCESS Event creation flags.
static int	CU_FUNC_ATTRIBUTE_BINARY_VERSION Function properties.
static int	CU_FUNC_ATTRIBUTE_CACHE_MODE_CA Function properties.
static int	CU_FUNC_ATTRIBUTE_CONST_SIZE_BYTES Function properties.
static int	CU_FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES Function properties.
static int	CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES Function properties.
static int	CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK Function properties.
static int	CU_FUNC_ATTRIBUTE_NUM_REGS Function properties.
static int	CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT Function properties.
static int	CU_FUNC_ATTRIBUTE_PTX_VERSION Function properties.
static int	CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES Function properties.
static int	CU_FUNC_CACHE_PREFER_EQUAL Function cache configurations.
static int	CU_FUNC_CACHE_PREFER_L1 Function cache configurations.
static int	CU_FUNC_CACHE_PREFER_NONE Function cache configurations.
static int	CU_FUNC_CACHE_PREFER_SHARED Function cache configurations.
static int	CU_GRAPHICS_MAP_RESOURCE_FLAGS_NONE Flags for mapping and unmapping interop resources.
static int	CU_GRAPHICS_MAP_RESOURCE_FLAGS_READ_ONLY Flags for mapping and unmapping interop resources.
static int	CU_GRAPHICS_MAP_RESOURCE_FLAGS_WRITE_DISCARD Flags for mapping and unmapping interop resources.
static int	CU_GRAPHICS_REGISTER_FLAGS_NONE Flags to register a graphics resource.
static int	CU_GRAPHICS_REGISTER_FLAGS_READ_ONLY Flags to register a graphics resource.
static int	CU_GRAPHICS_REGISTER_FLAGS_SURFACE_LDST Flags to register a graphics resource.
static int	CU_GRAPHICS_REGISTER_FLAGS_TEXTURE_GATHER Flags to register a graphics resource.
static int	CU_GRAPHICS_REGISTER_FLAGS_WRITE_DISCARD Flags to register a graphics resource.
static int	CU_JIT_CACHE_MODE Online compiler and linker options.
static int	CU_JIT_CACHE_OPTION_CA Caching modes for dlcm.
static int	CU_JIT_CACHE_OPTION_CG Caching modes for dlcm.
static int	CU_JIT_CACHE_OPTION_NONE Caching modes for dlcm.
static int	CU_JIT_ERROR_LOG_BUFFER Online compiler and linker options.
static int	CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES Online compiler and linker options.
static int	CU_JIT_FALLBACK_STRATEGY Online compiler and linker options.
static int	CU_JIT_FAST_COMPILE Online compiler and linker options.
static int	CU_JIT_GENERATE_DEBUG_INFO Online compiler and linker options.
static int	CU_JIT_GENERATE_LINE_INFO Online compiler and linker options.
static int	CU_JIT_GLOBAL_SYMBOL_ADDRESSES Online compiler and linker options.
static int	CU_JIT_GLOBAL_SYMBOL_COUNT Online compiler and linker options.
static int	CU_JIT_GLOBAL_SYMBOL_NAMES Online compiler and linker options.
static int	CU_JIT_INFO_LOG_BUFFER Online compiler and linker options.
static int	CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES Online compiler and linker options.
static int	CU_JIT_LOG_VERBOSE Online compiler and linker options.
static int	CU_JIT_MAX_REGISTERS Online compiler and linker options.
static int	CU_JIT_NEW_SM3X_OPT Online compiler and linker options.
static int	CU_JIT_OPTIMIZATION_LEVEL Online compiler and linker options.
static int	CU_JIT_TARGET Online compiler and linker options.
static int	CU_JIT_TARGET_FROM_CUCONTEXT Online compiler and linker options.
static int	CU_JIT_THREADS_PER_BLOCK Online compiler and linker options.
static int	CU_JIT_WALL_TIME Online compiler and linker options.
static int	CU_LIMIT_DEV_RUNTIME_PENDING_LAUNCH_COUNT Limits.
static int	CU_LIMIT_DEV_RUNTIME_SYNC_DEPTH Limits.
static int	CU_LIMIT_MALLOC_HEAP_SIZE Limits.
static int	CU_LIMIT_MAX_L2_FETCH_GRANULARITY Limits.
static int	CU_LIMIT_PRINTF_FIFO_SIZE Limits.
static int	CU_LIMIT_STACK_SIZE Limits.
static int	CU_MEMHOSTALLOC_DEVICEMAP Flags for MemHostAlloc.
static int	CU_MEMHOSTALLOC_PORTABLE Flags for MemHostAlloc.
static int	CU_MEMHOSTALLOC_WRITECOMBINED Flags for MemHostAlloc.
static int	CU_MEMHOSTREGISTER_DEVICEMAP Flags for MemHostRegister.
static int	CU_MEMHOSTREGISTER_IOMEMORY Flags for MemHostRegister.
static int	CU_MEMHOSTREGISTER_PORTABLE Flags for MemHostRegister.
static int	CU_MEMORYTYPE_ARRAY Memory types.
static int	CU_MEMORYTYPE_DEVICE Memory types.
static int	CU_MEMORYTYPE_HOST Memory types.
static int	CU_MEMORYTYPE_UNIFIED Memory types.
static int	CU_PARAM_TR_DEFAULT For texture references loaded into the module, use default texunit from texture reference.
static int	CU_PREFER_BINARY Cubin matching fallback strategies.
static int	CU_PREFER_PTX Cubin matching fallback strategies.
static int	CU_STREAM_DEFAULT Stream creation flags.
static int	CU_STREAM_NON_BLOCKING Stream creation flags.
static int	CU_TARGET_COMPUTE_20 Online compilation targets.
static int	CU_TARGET_COMPUTE_21 Online compilation targets.
static int	CU_TARGET_COMPUTE_30 Online compilation targets.
static int	CU_TARGET_COMPUTE_32 Online compilation targets.
static int	CU_TARGET_COMPUTE_35 Online compilation targets.
static int	CU_TARGET_COMPUTE_37 Online compilation targets.
static int	CU_TARGET_COMPUTE_50 Online compilation targets.
static int	CU_TARGET_COMPUTE_52 Online compilation targets.
static int	CU_TARGET_COMPUTE_53 Online compilation targets.
static int	CU_TARGET_COMPUTE_60 Online compilation targets.
static int	CU_TARGET_COMPUTE_61 Online compilation targets.
static int	CU_TARGET_COMPUTE_62 Online compilation targets.
static int	CU_TARGET_COMPUTE_70 Online compilation targets.
static int	CU_TARGET_COMPUTE_72 Online compilation targets.
static int	CU_TARGET_COMPUTE_75 Online compilation targets.
static int	CU_TR_ADDRESS_MODE_BORDER Texture reference addressing modes.
static int	CU_TR_ADDRESS_MODE_CLAMP Texture reference addressing modes.
static int	CU_TR_ADDRESS_MODE_MIRROR Texture reference addressing modes.
static int	CU_TR_ADDRESS_MODE_WRAP Texture reference addressing modes.
static int	CU_TR_FILTER_MODE_LINEAR Texture reference filtering modes.
static int	CU_TR_FILTER_MODE_POINT Texture reference filtering modes.
static int	CU_TRSA_OVERRIDE_FORMAT Flag for TexRefSetArray.
static int	CU_TRSF_NORMALIZED_COORDINATES Flag for TexRefSetFlags.
static int	CU_TRSF_READ_AS_INTEGER Flag for TexRefSetFlags.
static int	CU_TRSF_SRGB Flag for TexRefSetFlags.
static int	CUDA_ARRAY3D_2DARRAY Enum values:
static int	CUDA_ARRAY3D_COLOR_ATTACHMENT Enum values:
static int	CUDA_ARRAY3D_CUBEMAP Enum values:
static int	CUDA_ARRAY3D_DEPTH_TEXTURE Enum values:
static int	CUDA_ARRAY3D_LAYERED Enum values:
static int	CUDA_ARRAY3D_SURFACE_LDST Enum values:
static int	CUDA_ARRAY3D_TEXTURE_GATHER Enum values:
static int	CUDA_ERROR_ALREADY_ACQUIRED Error codes.
static int	CUDA_ERROR_ALREADY_MAPPED Error codes.
static int	CUDA_ERROR_ARRAY_IS_MAPPED Error codes.
static int	CUDA_ERROR_ASSERT Error codes.
static int	CUDA_ERROR_CAPTURED_EVENT Error codes.
static int	CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE Error codes.
static int	CUDA_ERROR_CONTEXT_ALREADY_CURRENT Error codes.
static int	CUDA_ERROR_CONTEXT_ALREADY_IN_USE Error codes.
static int	CUDA_ERROR_CONTEXT_IS_DESTROYED Error codes.
static int	CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE Error codes.
static int	CUDA_ERROR_DEINITIALIZED Error codes.
static int	CUDA_ERROR_ECC_UNCORRECTABLE Error codes.
static int	CUDA_ERROR_FILE_NOT_FOUND Error codes.
static int	CUDA_ERROR_HARDWARE_STACK_ERROR Error codes.
static int	CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED Error codes.
static int	CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED Error codes.
static int	CUDA_ERROR_ILLEGAL_ADDRESS Error codes.
static int	CUDA_ERROR_ILLEGAL_INSTRUCTION Error codes.
static int	CUDA_ERROR_ILLEGAL_STATE Error codes.
static int	CUDA_ERROR_INVALID_ADDRESS_SPACE Error codes.
static int	CUDA_ERROR_INVALID_CONTEXT Error codes.
static int	CUDA_ERROR_INVALID_DEVICE Error codes.
static int	CUDA_ERROR_INVALID_GRAPHICS_CONTEXT Error codes.
static int	CUDA_ERROR_INVALID_HANDLE Error codes.
static int	CUDA_ERROR_INVALID_IMAGE Error codes.
static int	CUDA_ERROR_INVALID_PC Error codes.
static int	CUDA_ERROR_INVALID_PTX Error codes.
static int	CUDA_ERROR_INVALID_SOURCE Error codes.
static int	CUDA_ERROR_INVALID_VALUE Error codes.
static int	CUDA_ERROR_JIT_COMPILER_NOT_FOUND Error codes.
static int	CUDA_ERROR_LAUNCH_FAILED Error codes.
static int	CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING Error codes.
static int	CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES Error codes.
static int	CUDA_ERROR_LAUNCH_TIMEOUT Error codes.
static int	CUDA_ERROR_MAP_FAILED Error codes.
static int	CUDA_ERROR_MISALIGNED_ADDRESS Error codes.
static int	CUDA_ERROR_NO_BINARY_FOR_GPU Error codes.
static int	CUDA_ERROR_NO_DEVICE Error codes.
static int	CUDA_ERROR_NOT_FOUND Error codes.
static int	CUDA_ERROR_NOT_INITIALIZED Error codes.
static int	CUDA_ERROR_NOT_MAPPED Error codes.
static int	CUDA_ERROR_NOT_MAPPED_AS_ARRAY Error codes.
static int	CUDA_ERROR_NOT_MAPPED_AS_POINTER Error codes.
static int	CUDA_ERROR_NOT_PERMITTED Error codes.
static int	CUDA_ERROR_NOT_READY Error codes.
static int	CUDA_ERROR_NOT_SUPPORTED Error codes.
static int	CUDA_ERROR_NVLINK_UNCORRECTABLE Error codes.
static int	CUDA_ERROR_OPERATING_SYSTEM Error codes.
static int	CUDA_ERROR_OUT_OF_MEMORY Error codes.
static int	CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED Error codes.
static int	CUDA_ERROR_PEER_ACCESS_NOT_ENABLED Error codes.
static int	CUDA_ERROR_PEER_ACCESS_UNSUPPORTED Error codes.
static int	CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE Error codes.
static int	CUDA_ERROR_PROFILER_ALREADY_STARTED Error codes.
static int	CUDA_ERROR_PROFILER_ALREADY_STOPPED Error codes.
static int	CUDA_ERROR_PROFILER_DISABLED Error codes.
static int	CUDA_ERROR_PROFILER_NOT_INITIALIZED Error codes.
static int	CUDA_ERROR_SHARED_OBJECT_INIT_FAILED Error codes.
static int	CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND Error codes.
static int	CUDA_ERROR_STREAM_CAPTURE_IMPLICIT Error codes.
static int	CUDA_ERROR_STREAM_CAPTURE_INVALIDATED Error codes.
static int	CUDA_ERROR_STREAM_CAPTURE_ISOLATION Error codes.
static int	CUDA_ERROR_STREAM_CAPTURE_MERGE Error codes.
static int	CUDA_ERROR_STREAM_CAPTURE_UNJOINED Error codes.
static int	CUDA_ERROR_STREAM_CAPTURE_UNMATCHED Error codes.
static int	CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED Error codes.
static int	CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD Error codes.
static int	CUDA_ERROR_SYSTEM_DRIVER_MISMATCH Error codes.
static int	CUDA_ERROR_SYSTEM_NOT_READY Error codes.
static int	CUDA_ERROR_TOO_MANY_PEERS Error codes.
static int	CUDA_ERROR_UNKNOWN Error codes.
static int	CUDA_ERROR_UNMAP_FAILED Error codes.
static int	CUDA_ERROR_UNSUPPORTED_LIMIT Error codes.
static int	CUDA_SUCCESS Error codes.

Method Summary

Modifier and Type	Method and Description
static int	cuArray3DCreate(org.lwjgl.PointerBuffer pHandle, CUDA_ARRAY3D_DESCRIPTOR pAllocateArray)
static int	cuArray3DGetDescriptor(CUDA_ARRAY3D_DESCRIPTOR pArrayDescriptor, long hArray)
static int	cuArrayCreate(org.lwjgl.PointerBuffer pHandle, CUDA_ARRAY_DESCRIPTOR pAllocateArray)
static int	cuArrayDestroy(long hArray)
static int	cuArrayGetDescriptor(CUDA_ARRAY_DESCRIPTOR pArrayDescriptor, long hArray)
static int	cuCtxAttach(org.lwjgl.PointerBuffer pctx, int flags)
static int	cuCtxCreate(org.lwjgl.PointerBuffer pctx, int flags, int dev)
static int	cuCtxDetach(long ctx)
static int	cuCtxGetApiVersion(long ctx, java.nio.IntBuffer version)
static int	cuCtxGetCacheConfig(java.nio.IntBuffer pconfig)
static int	cuCtxGetDevice(java.nio.IntBuffer device)
static int	cuCtxGetLimit(org.lwjgl.PointerBuffer pvalue, int limit)
static int	cuCtxGetStreamPriorityRange(java.nio.IntBuffer leastPriority, java.nio.IntBuffer greatestPriority)
static int	cuCtxSetCacheConfig(int config)
static int	cuCtxSetLimit(int limit, long value)
static int	cuCtxSynchronize()
static int	cuDeviceComputeCapability(java.nio.IntBuffer major, java.nio.IntBuffer minor, int dev)
static int	cuDeviceGet(java.nio.IntBuffer device, int ordinal)
static int	cuDeviceGetAttribute(java.nio.IntBuffer pi, int attrib, int dev)
static int	cuDeviceGetCount(java.nio.IntBuffer count)
static int	cuDeviceGetName(java.nio.ByteBuffer name, int dev)
static int	cuDeviceGetProperties(CUdevprop prop, int dev)
static int	cuDeviceTotalMem(org.lwjgl.PointerBuffer bytes, int dev)
static int	cuDriverGetVersion(java.nio.IntBuffer driverVersion)
static int	cuEventCreate(org.lwjgl.PointerBuffer phEvent, int Flags)
static int	cuEventElapsedTime(java.nio.FloatBuffer pMilliseconds, long hStart, long hEnd)
static int	cuEventQuery(long hEvent)
static int	cuEventRecord(long hEvent, long hStream)
static int	cuEventSynchronize(long hEvent)
static int	cuFuncGetAttribute(java.nio.IntBuffer pi, int attrib, long hfunc)
static int	cuFuncSetBlockShape(long hfunc, int x, int y, int z)
static int	cuFuncSetCacheConfig(long hfunc, int config)
static int	cuFuncSetSharedSize(long hfunc, int bytes)
static int	cuGetErrorName(int error, org.lwjgl.PointerBuffer pStr)
static int	cuGetErrorString(int error, org.lwjgl.PointerBuffer pStr)
static int	cuGetExportTable(org.lwjgl.PointerBuffer ppExportTable, CUuuid pExportTableId)
static int	cuGraphicsMapResources(org.lwjgl.PointerBuffer resources, long hStream)
static int	cuGraphicsResourceGetMappedPointer(org.lwjgl.PointerBuffer pDevPtr, org.lwjgl.PointerBuffer pSize, long resource)
static int	cuGraphicsResourceSetMapFlags(long resource, int flags)
static int	cuGraphicsSubResourceGetMappedArray(org.lwjgl.PointerBuffer pArray, long resource, int arrayIndex, int mipLevel)
static int	cuGraphicsUnmapResources(org.lwjgl.PointerBuffer resources, long hStream)
static int	cuGraphicsUnregisterResource(long resource)
static int	cuInit(int Flags)
static int	cuLaunch(long f)
static int	cuLaunchGrid(long f, int grid_width, int grid_height)
static int	cuLaunchGridAsync(long f, int grid_width, int grid_height, long hStream)
static int	cuMemAlloc(org.lwjgl.PointerBuffer dptr, long bytesize)
static int	cuMemAllocHost(org.lwjgl.PointerBuffer pp, long bytesize)
static int	cuMemAllocPitch(org.lwjgl.PointerBuffer dptr, org.lwjgl.PointerBuffer pPitch, long WidthInBytes, long Height, int ElementSizeBytes)
static int	cuMemcpy2D(CUDA_MEMCPY2D pCopy)
static int	cuMemcpy2DAsync(CUDA_MEMCPY2D pCopy, long hStream)
static int	cuMemcpy2DUnaligned(CUDA_MEMCPY2D pCopy)
static int	cuMemcpy3D(CUDA_MEMCPY3D pCopy)
static int	cuMemcpy3DAsync(CUDA_MEMCPY3D pCopy, long hStream)
static int	cuMemcpyAtoA(long dstArray, long dstOffset, long srcArray, long srcOffset, long ByteCount)
static int	cuMemcpyAtoD(long dstDevice, long srcArray, long srcOffset, long ByteCount)
static int	cuMemcpyAtoH(java.nio.ByteBuffer dstHost, long srcArray, long srcOffset)
static int	cuMemcpyAtoH(java.nio.DoubleBuffer dstHost, long srcArray, long srcOffset)
static int	cuMemcpyAtoH(java.nio.FloatBuffer dstHost, long srcArray, long srcOffset)
static int	cuMemcpyAtoH(java.nio.IntBuffer dstHost, long srcArray, long srcOffset)
static int	cuMemcpyAtoH(java.nio.LongBuffer dstHost, long srcArray, long srcOffset)
static int	cuMemcpyAtoH(org.lwjgl.PointerBuffer dstHost, long srcArray, long srcOffset)
static int	cuMemcpyAtoH(java.nio.ShortBuffer dstHost, long srcArray, long srcOffset)
static int	cuMemcpyAtoHAsync(java.nio.ByteBuffer dstHost, long srcArray, long srcOffset, long hStream)
static int	cuMemcpyAtoHAsync(java.nio.DoubleBuffer dstHost, long srcArray, long srcOffset, long hStream)
static int	cuMemcpyAtoHAsync(java.nio.FloatBuffer dstHost, long srcArray, long srcOffset, long hStream)
static int	cuMemcpyAtoHAsync(java.nio.IntBuffer dstHost, long srcArray, long srcOffset, long hStream)
static int	cuMemcpyAtoHAsync(java.nio.LongBuffer dstHost, long srcArray, long srcOffset, long hStream)
static int	cuMemcpyAtoHAsync(org.lwjgl.PointerBuffer dstHost, long srcArray, long srcOffset, long hStream)
static int	cuMemcpyAtoHAsync(java.nio.ShortBuffer dstHost, long srcArray, long srcOffset, long hStream)
static int	cuMemcpyDtoA(long dstArray, long dstOffset, long srcDevice, long ByteCount)
static int	cuMemcpyDtoD(long dstDevice, long srcDevice, long ByteCount)
static int	cuMemcpyDtoDAsync(long dstDevice, long srcDevice, long ByteCount, long hStream)
static int	cuMemcpyDtoH(java.nio.ByteBuffer dstHost, long srcDevice)
static int	cuMemcpyDtoH(java.nio.DoubleBuffer dstHost, long srcDevice)
static int	cuMemcpyDtoH(java.nio.FloatBuffer dstHost, long srcDevice)
static int	cuMemcpyDtoH(java.nio.IntBuffer dstHost, long srcDevice)
static int	cuMemcpyDtoH(java.nio.LongBuffer dstHost, long srcDevice)
static int	cuMemcpyDtoH(org.lwjgl.PointerBuffer dstHost, long srcDevice)
static int	cuMemcpyDtoH(java.nio.ShortBuffer dstHost, long srcDevice)
static int	cuMemcpyDtoHAsync(java.nio.ByteBuffer dstHost, long srcDevice, long hStream)
static int	cuMemcpyDtoHAsync(java.nio.DoubleBuffer dstHost, long srcDevice, long hStream)
static int	cuMemcpyDtoHAsync(java.nio.FloatBuffer dstHost, long srcDevice, long hStream)
static int	cuMemcpyDtoHAsync(java.nio.IntBuffer dstHost, long srcDevice, long hStream)
static int	cuMemcpyDtoHAsync(java.nio.LongBuffer dstHost, long srcDevice, long hStream)
static int	cuMemcpyDtoHAsync(org.lwjgl.PointerBuffer dstHost, long srcDevice, long hStream)
static int	cuMemcpyDtoHAsync(java.nio.ShortBuffer dstHost, long srcDevice, long hStream)
static int	cuMemcpyHtoA(long dstArray, long dstOffset, java.nio.ByteBuffer srcHost)
static int	cuMemcpyHtoA(long dstArray, long dstOffset, java.nio.DoubleBuffer srcHost)
static int	cuMemcpyHtoA(long dstArray, long dstOffset, java.nio.FloatBuffer srcHost)
static int	cuMemcpyHtoA(long dstArray, long dstOffset, java.nio.IntBuffer srcHost)
static int	cuMemcpyHtoA(long dstArray, long dstOffset, java.nio.LongBuffer srcHost)
static int	cuMemcpyHtoA(long dstArray, long dstOffset, org.lwjgl.PointerBuffer srcHost)
static int	cuMemcpyHtoA(long dstArray, long dstOffset, java.nio.ShortBuffer srcHost)
static int	cuMemcpyHtoAAsync(long dstArray, long dstOffset, java.nio.ByteBuffer srcHost, long hStream)
static int	cuMemcpyHtoAAsync(long dstArray, long dstOffset, java.nio.DoubleBuffer srcHost, long hStream)
static int	cuMemcpyHtoAAsync(long dstArray, long dstOffset, java.nio.FloatBuffer srcHost, long hStream)
static int	cuMemcpyHtoAAsync(long dstArray, long dstOffset, java.nio.IntBuffer srcHost, long hStream)
static int	cuMemcpyHtoAAsync(long dstArray, long dstOffset, java.nio.LongBuffer srcHost, long hStream)
static int	cuMemcpyHtoAAsync(long dstArray, long dstOffset, org.lwjgl.PointerBuffer srcHost, long hStream)
static int	cuMemcpyHtoAAsync(long dstArray, long dstOffset, java.nio.ShortBuffer srcHost, long hStream)
static int	cuMemcpyHtoD(long dstDevice, java.nio.ByteBuffer srcHost)
static int	cuMemcpyHtoD(long dstDevice, java.nio.DoubleBuffer srcHost)
static int	cuMemcpyHtoD(long dstDevice, java.nio.FloatBuffer srcHost)
static int	cuMemcpyHtoD(long dstDevice, java.nio.IntBuffer srcHost)
static int	cuMemcpyHtoD(long dstDevice, java.nio.LongBuffer srcHost)
static int	cuMemcpyHtoD(long dstDevice, org.lwjgl.PointerBuffer srcHost)
static int	cuMemcpyHtoD(long dstDevice, java.nio.ShortBuffer srcHost)
static int	cuMemcpyHtoDAsync(long dstDevice, java.nio.ByteBuffer srcHost, long hStream)
static int	cuMemcpyHtoDAsync(long dstDevice, java.nio.DoubleBuffer srcHost, long hStream)
static int	cuMemcpyHtoDAsync(long dstDevice, java.nio.FloatBuffer srcHost, long hStream)
static int	cuMemcpyHtoDAsync(long dstDevice, java.nio.IntBuffer srcHost, long hStream)
static int	cuMemcpyHtoDAsync(long dstDevice, java.nio.LongBuffer srcHost, long hStream)
static int	cuMemcpyHtoDAsync(long dstDevice, org.lwjgl.PointerBuffer srcHost, long hStream)
static int	cuMemcpyHtoDAsync(long dstDevice, java.nio.ShortBuffer srcHost, long hStream)
static int	cuMemFree(long dptr)
static int	cuMemFreeHost(java.nio.ByteBuffer p)
static int	cuMemGetAddressRange(org.lwjgl.PointerBuffer pbase, org.lwjgl.PointerBuffer psize, long dptr)
static int	cuMemGetInfo(org.lwjgl.PointerBuffer free, org.lwjgl.PointerBuffer total)
static int	cuMemHostAlloc(org.lwjgl.PointerBuffer pp, long bytesize, int Flags)
static int	cuMemHostGetDevicePointer(org.lwjgl.PointerBuffer pdptr, java.nio.ByteBuffer p, int Flags)
static int	cuMemHostGetFlags(java.nio.IntBuffer pFlags, java.nio.ByteBuffer p)
static int	cuMemsetD16(long dstDevice, short us, long N)
static int	cuMemsetD16Async(long dstDevice, short us, long N, long hStream)
static int	cuMemsetD2D16(long dstDevice, long dstPitch, short us, long Width, long Height)
static int	cuMemsetD2D16Async(long dstDevice, long dstPitch, short us, long Width, long Height, long hStream)
static int	cuMemsetD2D32(long dstDevice, long dstPitch, int ui, long Width, long Height)
static int	cuMemsetD2D32Async(long dstDevice, long dstPitch, int ui, long Width, long Height, long hStream)
static int	cuMemsetD2D8(long dstDevice, long dstPitch, byte uc, long Width, long Height)
static int	cuMemsetD2D8Async(long dstDevice, long dstPitch, byte uc, long Width, long Height, long hStream)
static int	cuMemsetD32(long dstDevice, int ui, long N)
static int	cuMemsetD32Async(long dstDevice, int ui, long N, long hStream)
static int	cuMemsetD8(long dstDevice, byte uc, long N)
static int	cuMemsetD8Async(long dstDevice, byte uc, long N, long hStream)
static int	cuModuleGetFunction(org.lwjgl.PointerBuffer hfunc, long hmod, java.nio.ByteBuffer name)
static int	cuModuleGetFunction(org.lwjgl.PointerBuffer hfunc, long hmod, java.lang.CharSequence name)
static int	cuModuleGetGlobal(org.lwjgl.PointerBuffer dptr, org.lwjgl.PointerBuffer bytes, long hmod, java.nio.ByteBuffer name)
static int	cuModuleGetGlobal(org.lwjgl.PointerBuffer dptr, org.lwjgl.PointerBuffer bytes, long hmod, java.lang.CharSequence name)
static int	cuModuleGetSurfRef(org.lwjgl.PointerBuffer pSurfRef, long hmod, java.nio.ByteBuffer name)
static int	cuModuleGetSurfRef(org.lwjgl.PointerBuffer pSurfRef, long hmod, java.lang.CharSequence name)
static int	cuModuleGetTexRef(org.lwjgl.PointerBuffer pTexRef, long hmod, java.nio.ByteBuffer name)
static int	cuModuleGetTexRef(org.lwjgl.PointerBuffer pTexRef, long hmod, java.lang.CharSequence name)
static int	cuModuleLoad(org.lwjgl.PointerBuffer module, java.nio.ByteBuffer fname)
static int	cuModuleLoad(org.lwjgl.PointerBuffer module, java.lang.CharSequence fname)
static int	cuModuleLoadData(org.lwjgl.PointerBuffer module, java.nio.ByteBuffer image)
static int	cuModuleLoadDataEx(org.lwjgl.PointerBuffer module, java.nio.ByteBuffer image, java.nio.IntBuffer options, org.lwjgl.PointerBuffer optionValues)
static int	cuModuleLoadFatBinary(org.lwjgl.PointerBuffer module, java.nio.ByteBuffer fatCubin)
static int	cuModuleUnload(long hmod)
static int	cuParamSetf(long hfunc, int offset, float value)
static int	cuParamSeti(long hfunc, int offset, int value)
static int	cuParamSetSize(long hfunc, int numbytes)
static int	cuParamSetTexRef(long hfunc, int texunit, long hTexRef)
static int	cuParamSetv(long hfunc, int offset, java.nio.ByteBuffer ptr)
static int	cuStreamAddCallback(long hStream, CUstreamCallbackI callback, long userData, int flags)
static int	cuStreamCreate(org.lwjgl.PointerBuffer phStream, int Flags)
static int	cuStreamCreateWithPriority(org.lwjgl.PointerBuffer phStream, int flags, int priority)
static int	cuStreamGetFlags(long hStream, java.nio.IntBuffer flags)
static int	cuStreamGetPriority(long hStream, java.nio.IntBuffer priority)
static int	cuStreamQuery(long hStream)
static int	cuStreamSynchronize(long hStream)
static int	cuStreamWaitEvent(long hStream, long hEvent, int Flags)
static int	cuSurfRefGetArray(org.lwjgl.PointerBuffer phArray, long hSurfRef)
static int	cuSurfRefSetArray(long hSurfRef, long hArray, int Flags)
static int	cuTexRefCreate(org.lwjgl.PointerBuffer pTexRef)
static int	cuTexRefDestroy(long hTexRef)
static int	cuTexRefGetAddress(org.lwjgl.PointerBuffer pdptr, long hTexRef)
static int	cuTexRefGetAddressMode(java.nio.IntBuffer pam, long hTexRef, int dim)
static int	cuTexRefGetArray(org.lwjgl.PointerBuffer phArray, long hTexRef)
static int	cuTexRefGetBorderColor(java.nio.FloatBuffer pBorderColor, long hTexRef)
static int	cuTexRefGetFilterMode(java.nio.IntBuffer pfm, long hTexRef)
static int	cuTexRefGetFlags(java.nio.IntBuffer pFlags, long hTexRef)
static int	cuTexRefGetFormat(java.nio.IntBuffer pFormat, java.nio.IntBuffer pNumChannels, long hTexRef)
static int	cuTexRefGetMaxAnisotropy(java.nio.IntBuffer pmaxAniso, long hTexRef)
static int	cuTexRefGetMipmapFilterMode(java.nio.IntBuffer pfm, long hTexRef)
static int	cuTexRefGetMipmapLevelBias(java.nio.FloatBuffer pbias, long hTexRef)
static int	cuTexRefGetMipmapLevelClamp(java.nio.FloatBuffer pminMipmapLevelClamp, java.nio.FloatBuffer pmaxMipmapLevelClamp, long hTexRef)
static int	cuTexRefGetMipmappedArray(org.lwjgl.PointerBuffer phMipmappedArray, long hTexRef)
static int	cuTexRefSetAddress(org.lwjgl.PointerBuffer ByteOffset, long hTexRef, long dptr, long bytes)
static int	cuTexRefSetAddress2D(long hTexRef, CUDA_ARRAY_DESCRIPTOR desc, long dptr, long Pitch)
static int	cuTexRefSetAddressMode(long hTexRef, int dim, int am)
static int	cuTexRefSetArray(long hTexRef, long hArray, int Flags)
static int	cuTexRefSetBorderColor(long hTexRef, java.nio.FloatBuffer pBorderColor)
static int	cuTexRefSetFilterMode(long hTexRef, int fm)
static int	cuTexRefSetFlags(long hTexRef, int Flags)
static int	cuTexRefSetFormat(long hTexRef, int fmt, int NumPackedComponents)
static int	cuTexRefSetMaxAnisotropy(long hTexRef, int maxAniso)
static int	cuTexRefSetMipmapFilterMode(long hTexRef, int fm)
static int	cuTexRefSetMipmapLevelBias(long hTexRef, float bias)
static int	cuTexRefSetMipmapLevelClamp(long hTexRef, float minMipmapLevelClamp, float maxMipmapLevelClamp)
static int	cuTexRefSetMipmappedArray(long hTexRef, long hMipmappedArray, int Flags)
static org.lwjgl.system.SharedLibrary	getLibrary() Returns the NVCUDA SharedLibrary.
static int	ncuArray3DCreate(long pHandle, long pAllocateArray)
static int	ncuArray3DGetDescriptor(long pArrayDescriptor, long hArray)
static int	ncuArrayCreate(long pHandle, long pAllocateArray)
static int	ncuArrayGetDescriptor(long pArrayDescriptor, long hArray)
static int	ncuCtxAttach(long pctx, int flags)
static int	ncuCtxCreate(long pctx, int flags, int dev)
static int	ncuCtxGetApiVersion(long ctx, long version)
static int	ncuCtxGetCacheConfig(long pconfig)
static int	ncuCtxGetDevice(long device)
static int	ncuCtxGetLimit(long pvalue, int limit)
static int	ncuCtxGetStreamPriorityRange(long leastPriority, long greatestPriority)
static int	ncuDeviceComputeCapability(long major, long minor, int dev)
static int	ncuDeviceGet(long device, int ordinal)
static int	ncuDeviceGetAttribute(long pi, int attrib, int dev)
static int	ncuDeviceGetCount(long count)
static int	ncuDeviceGetName(long name, int len, int dev)
static int	ncuDeviceGetProperties(long prop, int dev)
static int	ncuDeviceTotalMem(long bytes, int dev)
static int	ncuDriverGetVersion(long driverVersion)
static int	ncuEventCreate(long phEvent, int Flags)
static int	ncuEventElapsedTime(long pMilliseconds, long hStart, long hEnd)
static int	ncuFuncGetAttribute(long pi, int attrib, long hfunc)
static int	ncuGetErrorName(int error, long pStr)
static int	ncuGetErrorString(int error, long pStr)
static int	ncuGetExportTable(long ppExportTable, long pExportTableId)
static int	ncuGraphicsMapResources(int count, long resources, long hStream)
static int	ncuGraphicsResourceGetMappedPointer(long pDevPtr, long pSize, long resource)
static int	ncuGraphicsSubResourceGetMappedArray(long pArray, long resource, int arrayIndex, int mipLevel)
static int	ncuGraphicsUnmapResources(int count, long resources, long hStream)
static int	ncuMemAlloc(long dptr, long bytesize)
static int	ncuMemAllocHost(long pp, long bytesize)
static int	ncuMemAllocPitch(long dptr, long pPitch, long WidthInBytes, long Height, int ElementSizeBytes)
static int	ncuMemcpy2D(long pCopy)
static int	ncuMemcpy2DAsync(long pCopy, long hStream)
static int	ncuMemcpy2DUnaligned(long pCopy)
static int	ncuMemcpy3D(long pCopy)
static int	ncuMemcpy3DAsync(long pCopy, long hStream)
static int	ncuMemcpyAtoH(long dstHost, long srcArray, long srcOffset, long ByteCount)
static int	ncuMemcpyAtoHAsync(long dstHost, long srcArray, long srcOffset, long ByteCount, long hStream)
static int	ncuMemcpyDtoH(long dstHost, long srcDevice, long ByteCount)
static int	ncuMemcpyDtoHAsync(long dstHost, long srcDevice, long ByteCount, long hStream)
static int	ncuMemcpyHtoA(long dstArray, long dstOffset, long srcHost, long ByteCount)
static int	ncuMemcpyHtoAAsync(long dstArray, long dstOffset, long srcHost, long ByteCount, long hStream)
static int	ncuMemcpyHtoD(long dstDevice, long srcHost, long ByteCount)
static int	ncuMemcpyHtoDAsync(long dstDevice, long srcHost, long ByteCount, long hStream)
static int	ncuMemFreeHost(long p)
static int	ncuMemGetAddressRange(long pbase, long psize, long dptr)
static int	ncuMemGetInfo(long free, long total)
static int	ncuMemHostAlloc(long pp, long bytesize, int Flags)
static int	ncuMemHostGetDevicePointer(long pdptr, long p, int Flags)
static int	ncuMemHostGetFlags(long pFlags, long p)
static int	ncuModuleGetFunction(long hfunc, long hmod, long name)
static int	ncuModuleGetGlobal(long dptr, long bytes, long hmod, long name)
static int	ncuModuleGetSurfRef(long pSurfRef, long hmod, long name)
static int	ncuModuleGetTexRef(long pTexRef, long hmod, long name)
static int	ncuModuleLoad(long module, long fname)
static int	ncuModuleLoadData(long module, long image)
static int	ncuModuleLoadDataEx(long module, long image, int numOptions, long options, long optionValues)
static int	ncuModuleLoadFatBinary(long module, long fatCubin)
static int	ncuParamSetv(long hfunc, int offset, long ptr, int numbytes)
static int	ncuStreamAddCallback(long hStream, long callback, long userData, int flags)
static int	ncuStreamCreate(long phStream, int Flags)
static int	ncuStreamCreateWithPriority(long phStream, int flags, int priority)
static int	ncuStreamGetFlags(long hStream, long flags)
static int	ncuStreamGetPriority(long hStream, long priority)
static int	ncuSurfRefGetArray(long phArray, long hSurfRef)
static int	ncuTexRefCreate(long pTexRef)
static int	ncuTexRefGetAddress(long pdptr, long hTexRef)
static int	ncuTexRefGetAddressMode(long pam, long hTexRef, int dim)
static int	ncuTexRefGetArray(long phArray, long hTexRef)
static int	ncuTexRefGetBorderColor(long pBorderColor, long hTexRef)
static int	ncuTexRefGetFilterMode(long pfm, long hTexRef)
static int	ncuTexRefGetFlags(long pFlags, long hTexRef)
static int	ncuTexRefGetFormat(long pFormat, long pNumChannels, long hTexRef)
static int	ncuTexRefGetMaxAnisotropy(long pmaxAniso, long hTexRef)
static int	ncuTexRefGetMipmapFilterMode(long pfm, long hTexRef)
static int	ncuTexRefGetMipmapLevelBias(long pbias, long hTexRef)
static int	ncuTexRefGetMipmapLevelClamp(long pminMipmapLevelClamp, long pmaxMipmapLevelClamp, long hTexRef)
static int	ncuTexRefGetMipmappedArray(long phMipmappedArray, long hTexRef)
static int	ncuTexRefSetAddress(long ByteOffset, long hTexRef, long dptr, long bytes)
static int	ncuTexRefSetAddress2D(long hTexRef, long desc, long dptr, long Pitch)
static int	ncuTexRefSetBorderColor(long hTexRef, long pBorderColor)

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

CU_MEMHOSTALLOC_PORTABLE

public static final int CU_MEMHOSTALLOC_PORTABLE

Flags for MemHostAlloc.

Enum values:

• MEMHOSTALLOC_PORTABLE - If set, host memory is portable between CUDA contexts.
• MEMHOSTALLOC_DEVICEMAP - If set, host memory is mapped into CUDA address space and MemHostGetDevicePointer may be called on the host pointer.
• MEMHOSTALLOC_WRITECOMBINED - If set, host memory is allocated as write-combined - fast to write, faster to DMA, slow to read except via SSE4 streaming load instruction (MOVNTDQA).

See Also:

Constant Field Values

CU_MEMHOSTALLOC_DEVICEMAP

public static final int CU_MEMHOSTALLOC_DEVICEMAP

Flags for MemHostAlloc.

Enum values:

• MEMHOSTALLOC_PORTABLE - If set, host memory is portable between CUDA contexts.
• MEMHOSTALLOC_DEVICEMAP - If set, host memory is mapped into CUDA address space and MemHostGetDevicePointer may be called on the host pointer.
• MEMHOSTALLOC_WRITECOMBINED - If set, host memory is allocated as write-combined - fast to write, faster to DMA, slow to read except via SSE4 streaming load instruction (MOVNTDQA).

See Also:

Constant Field Values

CU_MEMHOSTALLOC_WRITECOMBINED

public static final int CU_MEMHOSTALLOC_WRITECOMBINED

Flags for MemHostAlloc.

Enum values:

• MEMHOSTALLOC_PORTABLE - If set, host memory is portable between CUDA contexts.
• MEMHOSTALLOC_DEVICEMAP - If set, host memory is mapped into CUDA address space and MemHostGetDevicePointer may be called on the host pointer.
• MEMHOSTALLOC_WRITECOMBINED - If set, host memory is allocated as write-combined - fast to write, faster to DMA, slow to read except via SSE4 streaming load instruction (MOVNTDQA).

See Also:

Constant Field Values

CU_MEMHOSTREGISTER_PORTABLE

public static final int CU_MEMHOSTREGISTER_PORTABLE

Flags for MemHostRegister.

Enum values:

• MEMHOSTREGISTER_PORTABLE - If set, host memory is portable between CUDA contexts.
• MEMHOSTREGISTER_DEVICEMAP - If set, host memory is mapped into CUDA address space and MemHostGetDevicePointer may be called on the host pointer.
• MEMHOSTREGISTER_IOMEMORY - If set, the passed memory pointer is treated as pointing to some memory-mapped I/O space, e.g. belonging to a third-party PCIe device.

  On Windows the flag is a no-op. On Linux that memory is marked as non cache-coherent for the GPU and is expected to be physically contiguous. It may return CUDA_ERROR_NOT_PERMITTED if run as an unprivileged user, CUDA_ERROR_NOT_SUPPORTED on older Linux kernel versions. On all other platforms, it is not supported and CUDA_ERROR_NOT_SUPPORTED is returned.

See Also:

Constant Field Values

CU_MEMHOSTREGISTER_DEVICEMAP

public static final int CU_MEMHOSTREGISTER_DEVICEMAP

Flags for MemHostRegister.

Enum values:

• MEMHOSTREGISTER_PORTABLE - If set, host memory is portable between CUDA contexts.
• MEMHOSTREGISTER_DEVICEMAP - If set, host memory is mapped into CUDA address space and MemHostGetDevicePointer may be called on the host pointer.
• MEMHOSTREGISTER_IOMEMORY - If set, the passed memory pointer is treated as pointing to some memory-mapped I/O space, e.g. belonging to a third-party PCIe device.

  On Windows the flag is a no-op. On Linux that memory is marked as non cache-coherent for the GPU and is expected to be physically contiguous. It may return CUDA_ERROR_NOT_PERMITTED if run as an unprivileged user, CUDA_ERROR_NOT_SUPPORTED on older Linux kernel versions. On all other platforms, it is not supported and CUDA_ERROR_NOT_SUPPORTED is returned.

See Also:

Constant Field Values

CU_MEMHOSTREGISTER_IOMEMORY

public static final int CU_MEMHOSTREGISTER_IOMEMORY

Flags for MemHostRegister.

Enum values:

• MEMHOSTREGISTER_PORTABLE - If set, host memory is portable between CUDA contexts.
• MEMHOSTREGISTER_DEVICEMAP - If set, host memory is mapped into CUDA address space and MemHostGetDevicePointer may be called on the host pointer.
• MEMHOSTREGISTER_IOMEMORY - If set, the passed memory pointer is treated as pointing to some memory-mapped I/O space, e.g. belonging to a third-party PCIe device.

  On Windows the flag is a no-op. On Linux that memory is marked as non cache-coherent for the GPU and is expected to be physically contiguous. It may return CUDA_ERROR_NOT_PERMITTED if run as an unprivileged user, CUDA_ERROR_NOT_SUPPORTED on older Linux kernel versions. On all other platforms, it is not supported and CUDA_ERROR_NOT_SUPPORTED is returned.

See Also:

Constant Field Values

CUDA_ARRAY3D_LAYERED

public static final int CUDA_ARRAY3D_LAYERED

Enum values:

• CUDA_ARRAY3D_LAYERED - If set, the CUDA array is a collection of layers, where each layer is either a 1D or a 2D array and the Depth member of CUDA_ARRAY3D_DESCRIPTOR specifies the number of layers, not the depth of a 3D array.
• CUDA_ARRAY3D_2DARRAY - Deprecated, use CUDA_ARRAY3D_LAYERED.
• CUDA_ARRAY3D_SURFACE_LDST - This flag must be set in order to bind a surface reference to the CUDA array.
• CUDA_ARRAY3D_CUBEMAP - If set, the CUDA array is a collection of six 2D arrays, representing faces of a cube. The width of such a CUDA array must be equal to its height, and Depth must be six. If CUDA_ARRAY3D_LAYERED flag is also set, then the CUDA array is a collection of cubemaps and Depth must be a multiple of six.
• CUDA_ARRAY3D_TEXTURE_GATHER - This flag must be set in order to perform texture gather operations on a CUDA array.
• CUDA_ARRAY3D_DEPTH_TEXTURE - This flag if set indicates that the CUDA array is a DEPTH_TEXTURE.
• CUDA_ARRAY3D_COLOR_ATTACHMENT - This flag indicates that the CUDA array may be bound as a color target in an external graphics API.

See Also:

Constant Field Values

CUDA_ARRAY3D_2DARRAY

public static final int CUDA_ARRAY3D_2DARRAY

Enum values:

• CUDA_ARRAY3D_LAYERED - If set, the CUDA array is a collection of layers, where each layer is either a 1D or a 2D array and the Depth member of CUDA_ARRAY3D_DESCRIPTOR specifies the number of layers, not the depth of a 3D array.
• CUDA_ARRAY3D_2DARRAY - Deprecated, use CUDA_ARRAY3D_LAYERED.
• CUDA_ARRAY3D_SURFACE_LDST - This flag must be set in order to bind a surface reference to the CUDA array.
• CUDA_ARRAY3D_CUBEMAP - If set, the CUDA array is a collection of six 2D arrays, representing faces of a cube. The width of such a CUDA array must be equal to its height, and Depth must be six. If CUDA_ARRAY3D_LAYERED flag is also set, then the CUDA array is a collection of cubemaps and Depth must be a multiple of six.
• CUDA_ARRAY3D_TEXTURE_GATHER - This flag must be set in order to perform texture gather operations on a CUDA array.
• CUDA_ARRAY3D_DEPTH_TEXTURE - This flag if set indicates that the CUDA array is a DEPTH_TEXTURE.
• CUDA_ARRAY3D_COLOR_ATTACHMENT - This flag indicates that the CUDA array may be bound as a color target in an external graphics API.

See Also:

Constant Field Values

CUDA_ARRAY3D_SURFACE_LDST

public static final int CUDA_ARRAY3D_SURFACE_LDST

Enum values:

• CUDA_ARRAY3D_LAYERED - If set, the CUDA array is a collection of layers, where each layer is either a 1D or a 2D array and the Depth member of CUDA_ARRAY3D_DESCRIPTOR specifies the number of layers, not the depth of a 3D array.
• CUDA_ARRAY3D_2DARRAY - Deprecated, use CUDA_ARRAY3D_LAYERED.
• CUDA_ARRAY3D_SURFACE_LDST - This flag must be set in order to bind a surface reference to the CUDA array.
• CUDA_ARRAY3D_CUBEMAP - If set, the CUDA array is a collection of six 2D arrays, representing faces of a cube. The width of such a CUDA array must be equal to its height, and Depth must be six. If CUDA_ARRAY3D_LAYERED flag is also set, then the CUDA array is a collection of cubemaps and Depth must be a multiple of six.
• CUDA_ARRAY3D_TEXTURE_GATHER - This flag must be set in order to perform texture gather operations on a CUDA array.
• CUDA_ARRAY3D_DEPTH_TEXTURE - This flag if set indicates that the CUDA array is a DEPTH_TEXTURE.
• CUDA_ARRAY3D_COLOR_ATTACHMENT - This flag indicates that the CUDA array may be bound as a color target in an external graphics API.

See Also:

Constant Field Values

CUDA_ARRAY3D_CUBEMAP

public static final int CUDA_ARRAY3D_CUBEMAP

Enum values:

• CUDA_ARRAY3D_LAYERED - If set, the CUDA array is a collection of layers, where each layer is either a 1D or a 2D array and the Depth member of CUDA_ARRAY3D_DESCRIPTOR specifies the number of layers, not the depth of a 3D array.
• CUDA_ARRAY3D_2DARRAY - Deprecated, use CUDA_ARRAY3D_LAYERED.
• CUDA_ARRAY3D_SURFACE_LDST - This flag must be set in order to bind a surface reference to the CUDA array.
• CUDA_ARRAY3D_CUBEMAP - If set, the CUDA array is a collection of six 2D arrays, representing faces of a cube. The width of such a CUDA array must be equal to its height, and Depth must be six. If CUDA_ARRAY3D_LAYERED flag is also set, then the CUDA array is a collection of cubemaps and Depth must be a multiple of six.
• CUDA_ARRAY3D_TEXTURE_GATHER - This flag must be set in order to perform texture gather operations on a CUDA array.
• CUDA_ARRAY3D_DEPTH_TEXTURE - This flag if set indicates that the CUDA array is a DEPTH_TEXTURE.
• CUDA_ARRAY3D_COLOR_ATTACHMENT - This flag indicates that the CUDA array may be bound as a color target in an external graphics API.

See Also:

Constant Field Values

CUDA_ARRAY3D_TEXTURE_GATHER

public static final int CUDA_ARRAY3D_TEXTURE_GATHER

Enum values:

• CUDA_ARRAY3D_LAYERED - If set, the CUDA array is a collection of layers, where each layer is either a 1D or a 2D array and the Depth member of CUDA_ARRAY3D_DESCRIPTOR specifies the number of layers, not the depth of a 3D array.
• CUDA_ARRAY3D_2DARRAY - Deprecated, use CUDA_ARRAY3D_LAYERED.
• CUDA_ARRAY3D_SURFACE_LDST - This flag must be set in order to bind a surface reference to the CUDA array.
• CUDA_ARRAY3D_CUBEMAP - If set, the CUDA array is a collection of six 2D arrays, representing faces of a cube. The width of such a CUDA array must be equal to its height, and Depth must be six. If CUDA_ARRAY3D_LAYERED flag is also set, then the CUDA array is a collection of cubemaps and Depth must be a multiple of six.
• CUDA_ARRAY3D_TEXTURE_GATHER - This flag must be set in order to perform texture gather operations on a CUDA array.
• CUDA_ARRAY3D_DEPTH_TEXTURE - This flag if set indicates that the CUDA array is a DEPTH_TEXTURE.
• CUDA_ARRAY3D_COLOR_ATTACHMENT - This flag indicates that the CUDA array may be bound as a color target in an external graphics API.

See Also:

Constant Field Values

CUDA_ARRAY3D_DEPTH_TEXTURE

public static final int CUDA_ARRAY3D_DEPTH_TEXTURE

Enum values:

• CUDA_ARRAY3D_LAYERED - If set, the CUDA array is a collection of layers, where each layer is either a 1D or a 2D array and the Depth member of CUDA_ARRAY3D_DESCRIPTOR specifies the number of layers, not the depth of a 3D array.
• CUDA_ARRAY3D_2DARRAY - Deprecated, use CUDA_ARRAY3D_LAYERED.
• CUDA_ARRAY3D_SURFACE_LDST - This flag must be set in order to bind a surface reference to the CUDA array.
• CUDA_ARRAY3D_CUBEMAP - If set, the CUDA array is a collection of six 2D arrays, representing faces of a cube. The width of such a CUDA array must be equal to its height, and Depth must be six. If CUDA_ARRAY3D_LAYERED flag is also set, then the CUDA array is a collection of cubemaps and Depth must be a multiple of six.
• CUDA_ARRAY3D_TEXTURE_GATHER - This flag must be set in order to perform texture gather operations on a CUDA array.
• CUDA_ARRAY3D_DEPTH_TEXTURE - This flag if set indicates that the CUDA array is a DEPTH_TEXTURE.
• CUDA_ARRAY3D_COLOR_ATTACHMENT - This flag indicates that the CUDA array may be bound as a color target in an external graphics API.

See Also:

Constant Field Values

CUDA_ARRAY3D_COLOR_ATTACHMENT

public static final int CUDA_ARRAY3D_COLOR_ATTACHMENT

Enum values:

• CUDA_ARRAY3D_LAYERED - If set, the CUDA array is a collection of layers, where each layer is either a 1D or a 2D array and the Depth member of CUDA_ARRAY3D_DESCRIPTOR specifies the number of layers, not the depth of a 3D array.
• CUDA_ARRAY3D_2DARRAY - Deprecated, use CUDA_ARRAY3D_LAYERED.
• CUDA_ARRAY3D_SURFACE_LDST - This flag must be set in order to bind a surface reference to the CUDA array.
• CUDA_ARRAY3D_CUBEMAP - If set, the CUDA array is a collection of six 2D arrays, representing faces of a cube. The width of such a CUDA array must be equal to its height, and Depth must be six. If CUDA_ARRAY3D_LAYERED flag is also set, then the CUDA array is a collection of cubemaps and Depth must be a multiple of six.
• CUDA_ARRAY3D_TEXTURE_GATHER - This flag must be set in order to perform texture gather operations on a CUDA array.
• CUDA_ARRAY3D_DEPTH_TEXTURE - This flag if set indicates that the CUDA array is a DEPTH_TEXTURE.
• CUDA_ARRAY3D_COLOR_ATTACHMENT - This flag indicates that the CUDA array may be bound as a color target in an external graphics API.

See Also:

Constant Field Values

CU_TRSA_OVERRIDE_FORMAT

public static final int CU_TRSA_OVERRIDE_FORMAT

Flag for TexRefSetArray.

Enum values:

• TRSA_OVERRIDE_FORMAT - Override the texref format with a format inferred from the array.

See Also:

Constant Field Values

CU_TRSF_READ_AS_INTEGER

public static final int CU_TRSF_READ_AS_INTEGER

Flag for TexRefSetFlags.

Enum values:

• TRSF_READ_AS_INTEGER - Read the texture as integers rather than promoting the values to floats in the range [0,1].
• TRSF_NORMALIZED_COORDINATES - Use normalized texture coordinates in the range [0,1) instead of [0,dim).
• TRSF_SRGB - Perform sRGB->linear conversion during texture read.

See Also:

Constant Field Values

CU_TRSF_NORMALIZED_COORDINATES

public static final int CU_TRSF_NORMALIZED_COORDINATES

Flag for TexRefSetFlags.

Enum values:

• TRSF_READ_AS_INTEGER - Read the texture as integers rather than promoting the values to floats in the range [0,1].
• TRSF_NORMALIZED_COORDINATES - Use normalized texture coordinates in the range [0,1) instead of [0,dim).
• TRSF_SRGB - Perform sRGB->linear conversion during texture read.

See Also:

Constant Field Values

CU_TRSF_SRGB

public static final int CU_TRSF_SRGB

Flag for TexRefSetFlags.

Enum values:

• TRSF_READ_AS_INTEGER - Read the texture as integers rather than promoting the values to floats in the range [0,1].
• TRSF_NORMALIZED_COORDINATES - Use normalized texture coordinates in the range [0,1) instead of [0,dim).
• TRSF_SRGB - Perform sRGB->linear conversion during texture read.

See Also:

Constant Field Values

CU_PARAM_TR_DEFAULT

public static final int CU_PARAM_TR_DEFAULT

For texture references loaded into the module, use default texunit from texture reference.

See Also:

Constant Field Values

CU_CTX_SCHED_AUTO

public static final int CU_CTX_SCHED_AUTO

Context creation flags. (CUctx_flags)

Enum values:

• CTX_SCHED_AUTO - Automatic scheduling
• CTX_SCHED_SPIN - Set spin as default scheduling
• CTX_SCHED_YIELD - Set yield as default scheduling
• CTX_SCHED_BLOCKING_SYNC - Set blocking synchronization as default scheduling
• CTX_BLOCKING_SYNC - Set blocking synchronization as default scheduling. This flag was deprecated as of CUDA 4.0 and was replaced with CTX_SCHED_BLOCKING_SYNC.
• CTX_SCHED_MASK
• CTX_MAP_HOST - Support mapped pinned allocations
• CTX_LMEM_RESIZE_TO_MAX - Keep local memory allocation after launch
• CTX_FLAGS_MASK

See Also:

Constant Field Values

CU_CTX_SCHED_SPIN

public static final int CU_CTX_SCHED_SPIN

Context creation flags. (CUctx_flags)

Enum values:

• CTX_SCHED_AUTO - Automatic scheduling
• CTX_SCHED_SPIN - Set spin as default scheduling
• CTX_SCHED_YIELD - Set yield as default scheduling
• CTX_SCHED_BLOCKING_SYNC - Set blocking synchronization as default scheduling
• CTX_BLOCKING_SYNC - Set blocking synchronization as default scheduling. This flag was deprecated as of CUDA 4.0 and was replaced with CTX_SCHED_BLOCKING_SYNC.
• CTX_SCHED_MASK
• CTX_MAP_HOST - Support mapped pinned allocations
• CTX_LMEM_RESIZE_TO_MAX - Keep local memory allocation after launch
• CTX_FLAGS_MASK

See Also:

Constant Field Values

CU_CTX_SCHED_YIELD

public static final int CU_CTX_SCHED_YIELD

Context creation flags. (CUctx_flags)

Enum values:

• CTX_SCHED_AUTO - Automatic scheduling
• CTX_SCHED_SPIN - Set spin as default scheduling
• CTX_SCHED_YIELD - Set yield as default scheduling
• CTX_SCHED_BLOCKING_SYNC - Set blocking synchronization as default scheduling
• CTX_BLOCKING_SYNC - Set blocking synchronization as default scheduling. This flag was deprecated as of CUDA 4.0 and was replaced with CTX_SCHED_BLOCKING_SYNC.
• CTX_SCHED_MASK
• CTX_MAP_HOST - Support mapped pinned allocations
• CTX_LMEM_RESIZE_TO_MAX - Keep local memory allocation after launch
• CTX_FLAGS_MASK

See Also:

Constant Field Values

CU_CTX_SCHED_BLOCKING_SYNC

public static final int CU_CTX_SCHED_BLOCKING_SYNC

Context creation flags. (CUctx_flags)

Enum values:

• CTX_SCHED_AUTO - Automatic scheduling
• CTX_SCHED_SPIN - Set spin as default scheduling
• CTX_SCHED_YIELD - Set yield as default scheduling
• CTX_SCHED_BLOCKING_SYNC - Set blocking synchronization as default scheduling
• CTX_BLOCKING_SYNC - Set blocking synchronization as default scheduling. This flag was deprecated as of CUDA 4.0 and was replaced with CTX_SCHED_BLOCKING_SYNC.
• CTX_SCHED_MASK
• CTX_MAP_HOST - Support mapped pinned allocations
• CTX_LMEM_RESIZE_TO_MAX - Keep local memory allocation after launch
• CTX_FLAGS_MASK

See Also:

Constant Field Values

CU_CTX_BLOCKING_SYNC

public static final int CU_CTX_BLOCKING_SYNC

Context creation flags. (CUctx_flags)

Enum values:

• CTX_SCHED_AUTO - Automatic scheduling
• CTX_SCHED_SPIN - Set spin as default scheduling
• CTX_SCHED_YIELD - Set yield as default scheduling
• CTX_SCHED_BLOCKING_SYNC - Set blocking synchronization as default scheduling
• CTX_BLOCKING_SYNC - Set blocking synchronization as default scheduling. This flag was deprecated as of CUDA 4.0 and was replaced with CTX_SCHED_BLOCKING_SYNC.
• CTX_SCHED_MASK
• CTX_MAP_HOST - Support mapped pinned allocations
• CTX_LMEM_RESIZE_TO_MAX - Keep local memory allocation after launch
• CTX_FLAGS_MASK

See Also:

Constant Field Values

CU_CTX_SCHED_MASK

public static final int CU_CTX_SCHED_MASK

Context creation flags. (CUctx_flags)

Enum values:

• CTX_SCHED_AUTO - Automatic scheduling
• CTX_SCHED_SPIN - Set spin as default scheduling
• CTX_SCHED_YIELD - Set yield as default scheduling
• CTX_SCHED_BLOCKING_SYNC - Set blocking synchronization as default scheduling
• CTX_BLOCKING_SYNC - Set blocking synchronization as default scheduling. This flag was deprecated as of CUDA 4.0 and was replaced with CTX_SCHED_BLOCKING_SYNC.
• CTX_SCHED_MASK
• CTX_MAP_HOST - Support mapped pinned allocations
• CTX_LMEM_RESIZE_TO_MAX - Keep local memory allocation after launch
• CTX_FLAGS_MASK

See Also:

Constant Field Values

CU_CTX_MAP_HOST

public static final int CU_CTX_MAP_HOST

Context creation flags. (CUctx_flags)

Enum values:

• CTX_SCHED_AUTO - Automatic scheduling
• CTX_SCHED_SPIN - Set spin as default scheduling
• CTX_SCHED_YIELD - Set yield as default scheduling
• CTX_SCHED_BLOCKING_SYNC - Set blocking synchronization as default scheduling
• CTX_BLOCKING_SYNC - Set blocking synchronization as default scheduling. This flag was deprecated as of CUDA 4.0 and was replaced with CTX_SCHED_BLOCKING_SYNC.
• CTX_SCHED_MASK
• CTX_MAP_HOST - Support mapped pinned allocations
• CTX_LMEM_RESIZE_TO_MAX - Keep local memory allocation after launch
• CTX_FLAGS_MASK

See Also:

Constant Field Values

CU_CTX_LMEM_RESIZE_TO_MAX

public static final int CU_CTX_LMEM_RESIZE_TO_MAX

Context creation flags. (CUctx_flags)

Enum values:

• CTX_SCHED_AUTO - Automatic scheduling
• CTX_SCHED_SPIN - Set spin as default scheduling
• CTX_SCHED_YIELD - Set yield as default scheduling
• CTX_SCHED_BLOCKING_SYNC - Set blocking synchronization as default scheduling
• CTX_BLOCKING_SYNC - Set blocking synchronization as default scheduling. This flag was deprecated as of CUDA 4.0 and was replaced with CTX_SCHED_BLOCKING_SYNC.
• CTX_SCHED_MASK
• CTX_MAP_HOST - Support mapped pinned allocations
• CTX_LMEM_RESIZE_TO_MAX - Keep local memory allocation after launch
• CTX_FLAGS_MASK

See Also:

Constant Field Values

CU_CTX_FLAGS_MASK

public static final int CU_CTX_FLAGS_MASK

Context creation flags. (CUctx_flags)

Enum values:

• CTX_SCHED_AUTO - Automatic scheduling
• CTX_SCHED_SPIN - Set spin as default scheduling
• CTX_SCHED_YIELD - Set yield as default scheduling
• CTX_SCHED_BLOCKING_SYNC - Set blocking synchronization as default scheduling
• CTX_BLOCKING_SYNC - Set blocking synchronization as default scheduling. This flag was deprecated as of CUDA 4.0 and was replaced with CTX_SCHED_BLOCKING_SYNC.
• CTX_SCHED_MASK
• CTX_MAP_HOST - Support mapped pinned allocations
• CTX_LMEM_RESIZE_TO_MAX - Keep local memory allocation after launch
• CTX_FLAGS_MASK

See Also:

Constant Field Values

CU_STREAM_DEFAULT

public static final int CU_STREAM_DEFAULT

Stream creation flags. (CUstream_flags)

Enum values:

• STREAM_DEFAULT - Default stream flag
• STREAM_NON_BLOCKING - Stream does not synchronize with stream 0 (the NULL stream)

See Also:

Constant Field Values

CU_STREAM_NON_BLOCKING

public static final int CU_STREAM_NON_BLOCKING

Stream creation flags. (CUstream_flags)

Enum values:

• STREAM_DEFAULT - Default stream flag
• STREAM_NON_BLOCKING - Stream does not synchronize with stream 0 (the NULL stream)

See Also:

Constant Field Values

CU_EVENT_DEFAULT

public static final int CU_EVENT_DEFAULT

Event creation flags. (CUevent_flags)

Enum values:

• EVENT_DEFAULT - Default event flag
• EVENT_BLOCKING_SYNC - Event uses blocking synchronization
• EVENT_DISABLE_TIMING - Event will not record timing data
• EVENT_INTERPROCESS - Event is suitable for interprocess use. EVENT_DISABLE_TIMING must be set

See Also:

Constant Field Values

CU_EVENT_BLOCKING_SYNC

public static final int CU_EVENT_BLOCKING_SYNC

Event creation flags. (CUevent_flags)

Enum values:

• EVENT_DEFAULT - Default event flag
• EVENT_BLOCKING_SYNC - Event uses blocking synchronization
• EVENT_DISABLE_TIMING - Event will not record timing data
• EVENT_INTERPROCESS - Event is suitable for interprocess use. EVENT_DISABLE_TIMING must be set

See Also:

Constant Field Values

CU_EVENT_DISABLE_TIMING

public static final int CU_EVENT_DISABLE_TIMING

Event creation flags. (CUevent_flags)

Enum values:

• EVENT_DEFAULT - Default event flag
• EVENT_BLOCKING_SYNC - Event uses blocking synchronization
• EVENT_DISABLE_TIMING - Event will not record timing data
• EVENT_INTERPROCESS - Event is suitable for interprocess use. EVENT_DISABLE_TIMING must be set

See Also:

Constant Field Values

CU_EVENT_INTERPROCESS

public static final int CU_EVENT_INTERPROCESS

Event creation flags. (CUevent_flags)

Enum values:

• EVENT_DEFAULT - Default event flag
• EVENT_BLOCKING_SYNC - Event uses blocking synchronization
• EVENT_DISABLE_TIMING - Event will not record timing data
• EVENT_INTERPROCESS - Event is suitable for interprocess use. EVENT_DISABLE_TIMING must be set

See Also:

Constant Field Values

CU_AD_FORMAT_UNSIGNED_INT8

public static final int CU_AD_FORMAT_UNSIGNED_INT8

Array formats. (CUarray_format)

Enum values:

• AD_FORMAT_UNSIGNED_INT8 - Unsigned 8-bit integers
• AD_FORMAT_UNSIGNED_INT16 - Unsigned 16-bit integers
• AD_FORMAT_UNSIGNED_INT32 - Unsigned 32-bit integers
• AD_FORMAT_SIGNED_INT8 - Signed 8-bit integers
• AD_FORMAT_SIGNED_INT16 - Signed 16-bit integers
• AD_FORMAT_SIGNED_INT32 - Signed 32-bit integers
• AD_FORMAT_HALF - 16-bit floating point
• AD_FORMAT_FLOAT - 32-bit floating point

See Also:

Constant Field Values

CU_AD_FORMAT_UNSIGNED_INT16

public static final int CU_AD_FORMAT_UNSIGNED_INT16

Array formats. (CUarray_format)

Enum values:

• AD_FORMAT_UNSIGNED_INT8 - Unsigned 8-bit integers
• AD_FORMAT_UNSIGNED_INT16 - Unsigned 16-bit integers
• AD_FORMAT_UNSIGNED_INT32 - Unsigned 32-bit integers
• AD_FORMAT_SIGNED_INT8 - Signed 8-bit integers
• AD_FORMAT_SIGNED_INT16 - Signed 16-bit integers
• AD_FORMAT_SIGNED_INT32 - Signed 32-bit integers
• AD_FORMAT_HALF - 16-bit floating point
• AD_FORMAT_FLOAT - 32-bit floating point

See Also:

Constant Field Values

CU_AD_FORMAT_UNSIGNED_INT32

public static final int CU_AD_FORMAT_UNSIGNED_INT32

Array formats. (CUarray_format)

Enum values:

• AD_FORMAT_UNSIGNED_INT8 - Unsigned 8-bit integers
• AD_FORMAT_UNSIGNED_INT16 - Unsigned 16-bit integers
• AD_FORMAT_UNSIGNED_INT32 - Unsigned 32-bit integers
• AD_FORMAT_SIGNED_INT8 - Signed 8-bit integers
• AD_FORMAT_SIGNED_INT16 - Signed 16-bit integers
• AD_FORMAT_SIGNED_INT32 - Signed 32-bit integers
• AD_FORMAT_HALF - 16-bit floating point
• AD_FORMAT_FLOAT - 32-bit floating point

See Also:

Constant Field Values

CU_AD_FORMAT_SIGNED_INT8

public static final int CU_AD_FORMAT_SIGNED_INT8

Array formats. (CUarray_format)

Enum values:

• AD_FORMAT_UNSIGNED_INT8 - Unsigned 8-bit integers
• AD_FORMAT_UNSIGNED_INT16 - Unsigned 16-bit integers
• AD_FORMAT_UNSIGNED_INT32 - Unsigned 32-bit integers
• AD_FORMAT_SIGNED_INT8 - Signed 8-bit integers
• AD_FORMAT_SIGNED_INT16 - Signed 16-bit integers
• AD_FORMAT_SIGNED_INT32 - Signed 32-bit integers
• AD_FORMAT_HALF - 16-bit floating point
• AD_FORMAT_FLOAT - 32-bit floating point

See Also:

Constant Field Values

CU_AD_FORMAT_SIGNED_INT16

public static final int CU_AD_FORMAT_SIGNED_INT16

Array formats. (CUarray_format)

Enum values:

• AD_FORMAT_UNSIGNED_INT8 - Unsigned 8-bit integers
• AD_FORMAT_UNSIGNED_INT16 - Unsigned 16-bit integers
• AD_FORMAT_UNSIGNED_INT32 - Unsigned 32-bit integers
• AD_FORMAT_SIGNED_INT8 - Signed 8-bit integers
• AD_FORMAT_SIGNED_INT16 - Signed 16-bit integers
• AD_FORMAT_SIGNED_INT32 - Signed 32-bit integers
• AD_FORMAT_HALF - 16-bit floating point
• AD_FORMAT_FLOAT - 32-bit floating point

See Also:

Constant Field Values

CU_AD_FORMAT_SIGNED_INT32

public static final int CU_AD_FORMAT_SIGNED_INT32

Array formats. (CUarray_format)

Enum values:

• AD_FORMAT_UNSIGNED_INT8 - Unsigned 8-bit integers
• AD_FORMAT_UNSIGNED_INT16 - Unsigned 16-bit integers
• AD_FORMAT_UNSIGNED_INT32 - Unsigned 32-bit integers
• AD_FORMAT_SIGNED_INT8 - Signed 8-bit integers
• AD_FORMAT_SIGNED_INT16 - Signed 16-bit integers
• AD_FORMAT_SIGNED_INT32 - Signed 32-bit integers
• AD_FORMAT_HALF - 16-bit floating point
• AD_FORMAT_FLOAT - 32-bit floating point

See Also:

Constant Field Values

CU_AD_FORMAT_HALF

public static final int CU_AD_FORMAT_HALF

Array formats. (CUarray_format)

Enum values:

• AD_FORMAT_UNSIGNED_INT8 - Unsigned 8-bit integers
• AD_FORMAT_UNSIGNED_INT16 - Unsigned 16-bit integers
• AD_FORMAT_UNSIGNED_INT32 - Unsigned 32-bit integers
• AD_FORMAT_SIGNED_INT8 - Signed 8-bit integers
• AD_FORMAT_SIGNED_INT16 - Signed 16-bit integers
• AD_FORMAT_SIGNED_INT32 - Signed 32-bit integers
• AD_FORMAT_HALF - 16-bit floating point
• AD_FORMAT_FLOAT - 32-bit floating point

See Also:

Constant Field Values

CU_AD_FORMAT_FLOAT

public static final int CU_AD_FORMAT_FLOAT

Array formats. (CUarray_format)

Enum values:

• AD_FORMAT_UNSIGNED_INT8 - Unsigned 8-bit integers
• AD_FORMAT_UNSIGNED_INT16 - Unsigned 16-bit integers
• AD_FORMAT_UNSIGNED_INT32 - Unsigned 32-bit integers
• AD_FORMAT_SIGNED_INT8 - Signed 8-bit integers
• AD_FORMAT_SIGNED_INT16 - Signed 16-bit integers
• AD_FORMAT_SIGNED_INT32 - Signed 32-bit integers
• AD_FORMAT_HALF - 16-bit floating point
• AD_FORMAT_FLOAT - 32-bit floating point

See Also:

Constant Field Values

CU_TR_ADDRESS_MODE_WRAP

public static final int CU_TR_ADDRESS_MODE_WRAP

Texture reference addressing modes. (CUaddress_mode)

Enum values:

• TR_ADDRESS_MODE_WRAP - Wrapping address mode
• TR_ADDRESS_MODE_CLAMP - Clamp to edge address mode
• TR_ADDRESS_MODE_MIRROR - Mirror address mode
• TR_ADDRESS_MODE_BORDER - Border address mode

See Also:

Constant Field Values

CU_TR_ADDRESS_MODE_CLAMP

public static final int CU_TR_ADDRESS_MODE_CLAMP

Texture reference addressing modes. (CUaddress_mode)

Enum values:

• TR_ADDRESS_MODE_WRAP - Wrapping address mode
• TR_ADDRESS_MODE_CLAMP - Clamp to edge address mode
• TR_ADDRESS_MODE_MIRROR - Mirror address mode
• TR_ADDRESS_MODE_BORDER - Border address mode

See Also:

Constant Field Values

CU_TR_ADDRESS_MODE_MIRROR

public static final int CU_TR_ADDRESS_MODE_MIRROR

Texture reference addressing modes. (CUaddress_mode)

Enum values:

• TR_ADDRESS_MODE_WRAP - Wrapping address mode
• TR_ADDRESS_MODE_CLAMP - Clamp to edge address mode
• TR_ADDRESS_MODE_MIRROR - Mirror address mode
• TR_ADDRESS_MODE_BORDER - Border address mode

See Also:

Constant Field Values

CU_TR_ADDRESS_MODE_BORDER

public static final int CU_TR_ADDRESS_MODE_BORDER

Texture reference addressing modes. (CUaddress_mode)

Enum values:

• TR_ADDRESS_MODE_WRAP - Wrapping address mode
• TR_ADDRESS_MODE_CLAMP - Clamp to edge address mode
• TR_ADDRESS_MODE_MIRROR - Mirror address mode
• TR_ADDRESS_MODE_BORDER - Border address mode

See Also:

Constant Field Values

CU_TR_FILTER_MODE_POINT

public static final int CU_TR_FILTER_MODE_POINT

Texture reference filtering modes. (CUfilter_mode)

Enum values:

• TR_FILTER_MODE_POINT - Point filter mode
• TR_FILTER_MODE_LINEAR - Linear filter mode

See Also:

Constant Field Values

CU_TR_FILTER_MODE_LINEAR

public static final int CU_TR_FILTER_MODE_LINEAR

Texture reference filtering modes. (CUfilter_mode)

Enum values:

• TR_FILTER_MODE_POINT - Point filter mode
• TR_FILTER_MODE_LINEAR - Linear filter mode

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK

public static final int CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X

public static final int CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y

public static final int CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z

public static final int CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_X

public static final int CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_X

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y

public static final int CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z

public static final int CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK

public static final int CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK

public static final int CU_DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY

public static final int CU_DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_WARP_SIZE

public static final int CU_DEVICE_ATTRIBUTE_WARP_SIZE

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAX_PITCH

public static final int CU_DEVICE_ATTRIBUTE_MAX_PITCH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK

public static final int CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK

public static final int CU_DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_CLOCK_RATE

public static final int CU_DEVICE_ATTRIBUTE_CLOCK_RATE

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT

public static final int CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_GPU_OVERLAP

public static final int CU_DEVICE_ATTRIBUTE_GPU_OVERLAP

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT

public static final int CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT

public static final int CU_DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_INTEGRATED

public static final int CU_DEVICE_ATTRIBUTE_INTEGRATED

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY

public static final int CU_DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_COMPUTE_MODE

public static final int CU_DEVICE_ATTRIBUTE_COMPUTE_MODE

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT

public static final int CU_DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_CONCURRENT_KERNELS

public static final int CU_DEVICE_ATTRIBUTE_CONCURRENT_KERNELS

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_ECC_ENABLED

public static final int CU_DEVICE_ATTRIBUTE_ECC_ENABLED

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_PCI_BUS_ID

public static final int CU_DEVICE_ATTRIBUTE_PCI_BUS_ID

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_PCI_DEVICE_ID

public static final int CU_DEVICE_ATTRIBUTE_PCI_DEVICE_ID

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_TCC_DRIVER

public static final int CU_DEVICE_ATTRIBUTE_TCC_DRIVER

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE

public static final int CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH

public static final int CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_L2_CACHE_SIZE

public static final int CU_DEVICE_ATTRIBUTE_L2_CACHE_SIZE

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR

public static final int CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT

public static final int CU_DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING

public static final int CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER

public static final int CU_DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_PCI_DOMAIN_ID

public static final int CU_DEVICE_ATTRIBUTE_PCI_DOMAIN_ID

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT

public static final int CU_DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR

public static final int CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR

public static final int CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH

public static final int CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED

public static final int CU_DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED

public static final int CU_DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED

public static final int CU_DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR

public static final int CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR

public static final int CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MANAGED_MEMORY

public static final int CU_DEVICE_ATTRIBUTE_MANAGED_MEMORY

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD

public static final int CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID

public static final int CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED

public static final int CU_DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO

public static final int CU_DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS

public static final int CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS

public static final int CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED

public static final int CU_DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM

public static final int CU_DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS

public static final int CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS

public static final int CU_DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR

public static final int CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH

public static final int CU_DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH

public static final int CU_DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN

public static final int CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES

public static final int CU_DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED

public static final int CU_DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES

public static final int CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST

public static final int CU_DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST

Device properties. (CUdevice_attribute)

Enum values:

• DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK - Maximum number of threads per block
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X - Maximum block dimension X
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y - Maximum block dimension Y
• DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z - Maximum block dimension Z
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_X - Maximum grid dimension X
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y - Maximum grid dimension Y
• DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z - Maximum grid dimension Z
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK - Maximum shared memory available per block in bytes
• DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
• DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY - Memory available on device for __constant__ variables in a CUDA C kernel in bytes
• DEVICE_ATTRIBUTE_WARP_SIZE - Warp size in threads
• DEVICE_ATTRIBUTE_MAX_PITCH - Maximum pitch in bytes allowed by memory copies
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK - Maximum number of 32-bit registers available per block
• DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK - Deprecated, use DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
• DEVICE_ATTRIBUTE_CLOCK_RATE - Typical clock frequency in kilohertz
• DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT - Alignment requirement for textures
• DEVICE_ATTRIBUTE_GPU_OVERLAP - Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
• DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT - Number of multiprocessors on device
• DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT - Specifies whether there is a run time limit on kernels
• DEVICE_ATTRIBUTE_INTEGRATED - Device is integrated with host memory
• DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY - Device can map host memory into CUDA address space
• DEVICE_ATTRIBUTE_COMPUTE_MODE - Compute mode (See CUcomputemode for details)
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH - Maximum 1D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH - Maximum 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT - Maximum 2D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH - Maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT - Maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH - Maximum 3D texture depth
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH - Maximum 2D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT - Maximum 2D layered texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS - Maximum layers in a 2D layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES - Deprecated, use DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
• DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT - Alignment requirement for surfaces
• DEVICE_ATTRIBUTE_CONCURRENT_KERNELS - Device can possibly execute multiple kernels concurrently
• DEVICE_ATTRIBUTE_ECC_ENABLED - Device has ECC support enabled
• DEVICE_ATTRIBUTE_PCI_BUS_ID - PCI bus ID of the device
• DEVICE_ATTRIBUTE_PCI_DEVICE_ID - PCI device ID of the device
• DEVICE_ATTRIBUTE_TCC_DRIVER - Device is using TCC driver model
• DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE - Peak memory clock frequency in kilohertz
• DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH - Global memory bus width in bits
• DEVICE_ATTRIBUTE_L2_CACHE_SIZE - Size of L2 cache in bytes
• DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR - Maximum resident threads per multiprocessor
• DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT - Number of asynchronous engines
• DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING - Device shares a unified address space with the host
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH - Maximum 1D layered texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS - Maximum layers in a 1D layered texture
• DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER - Deprecated, do not use.
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH - Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT - Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE - Alternate maximum 3D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE - Alternate maximum 3D texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE - Alternate maximum 3D texture depth
• DEVICE_ATTRIBUTE_PCI_DOMAIN_ID - PCI domain ID of the device
• DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT - Pitch alignment requirement for textures
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH - Maximum cubemap texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered texture width/height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered texture
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH - Maximum 1D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH - Maximum 2D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT - Maximum 2D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH - Maximum 3D surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT - Maximum 3D surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH - Maximum 3D surface depth
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH - Maximum 1D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS - Maximum layers in a 1D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH - Maximum 2D layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT - Maximum 2D layered surface height
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS - Maximum layers in a 2D layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH - Maximum cubemap surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH - Maximum cubemap layered surface width
• DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS - Maximum layers in a cubemap layered surface
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH - Maximum 1D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH - Maximum 2D linear texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT - Maximum 2D linear texture height
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH - Maximum 2D linear texture pitch in bytes
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH - Maximum mipmapped 2D texture width
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT - Maximum mipmapped 2D texture height
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR - Major compute capability version number
• DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR - Minor compute capability version number
• DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH - Maximum mipmapped 1D texture width
• DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED - Device supports stream priorities
• DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED - Device supports caching globals in L1
• DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED - Device supports caching locals in L1
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR - Maximum shared memory available per multiprocessor in bytes
• DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR - Maximum number of 32-bit registers available per multiprocessor
• DEVICE_ATTRIBUTE_MANAGED_MEMORY - Device can allocate managed memory on this system
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD - Device is on a multi-GPU board
• DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID - Unique id for a group of devices on the same multi-GPU board
• DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED - Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
• DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO - Ratio of single precision performance (in floating-point operations per second) to double precision performance
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS - Device supports coherently accessing pageable memory without calling cudaHostRegister on it
• DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS - Device can coherently access managed memory concurrently with the CPU
• DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED - Device supports compute preemption.
• DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM - Device can access host registered memory at the same virtual address as the CPU
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS - StreamBatchMemOp and related APIs are supported.
• DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS - 64-bit operations are supported in StreamBatchMemOp and related APIs.
• DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR - STREAM_WAIT_VALUE_NOR is supported.
• DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH - Device supports launching cooperative kernels via LaunchCooperativeKernel
• DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH - Device can participate in cooperative kernels launched via LaunchCooperativeKernelMultiDevice
• DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN - Maximum optin shared memory per block
• DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES - Both the STREAM_WAIT_VALUE_FLUSH flag and the STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device.
• DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED - Device supports host memory registration via cudaHostRegister().
• DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES - Device accesses pageable memory via the host's page tables.
• DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST - The host can directly access managed memory on the device without migration.

See Also:

Constant Field Values

CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK

public static final int CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK

Function properties. (CUfunction_attribute)

Enum values:

• FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK - The maximum number of threads per block, beyond which a launch of the function would fail. This number depends on both the function and the device on which the function is currently loaded.
• FUNC_ATTRIBUTE_SHARED_SIZE_BYTES - The size in bytes of statically-allocated shared memory required by this function. This does not include dynamically-allocated shared memory requested by the user at runtime.
• FUNC_ATTRIBUTE_CONST_SIZE_BYTES - The size in bytes of user-allocated constant memory required by this function.
• FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES - The size in bytes of local memory used by each thread of this function.
• FUNC_ATTRIBUTE_NUM_REGS - The number of registers used by each thread of this function.
• FUNC_ATTRIBUTE_PTX_VERSION - The PTX virtual architecture version for which the function was compiled.

  This value is the major PTX version * 10 + the minor PTX version, so a PTX version 1.3 function would return the value 13. Note that this may return the undefined value of 0 for cubins compiled prior to CUDA 3.0.

• FUNC_ATTRIBUTE_BINARY_VERSION - The binary architecture version for which the function was compiled.

  This value is the major binary version * 10 + the minor binary version, so a binary version 1.3 function would return the value 13. Note that this will return a value of 10 for legacy cubins that do not have a properly-encoded binary architecture version.

• FUNC_ATTRIBUTE_CACHE_MODE_CA - The attribute to indicate whether the function has been compiled with user specified option "-Xptxas --dlcm=ca" set.
• FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES - The maximum size in bytes of dynamically-allocated shared memory that can be used by this function.

  If the user-specified dynamic shared memory size is larger than this value, the launch will fail.

• FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT - On devices where the L1 cache and shared memory use the same hardware resources, this sets the shared memory carveout preference, in percent of the total shared memory. Refer to DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR.

  This is only a hint, and the driver can choose a different ratio if required to execute the function.

See Also:

Constant Field Values

CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES

public static final int CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES

Function properties. (CUfunction_attribute)

Enum values:

• FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK - The maximum number of threads per block, beyond which a launch of the function would fail. This number depends on both the function and the device on which the function is currently loaded.
• FUNC_ATTRIBUTE_SHARED_SIZE_BYTES - The size in bytes of statically-allocated shared memory required by this function. This does not include dynamically-allocated shared memory requested by the user at runtime.
• FUNC_ATTRIBUTE_CONST_SIZE_BYTES - The size in bytes of user-allocated constant memory required by this function.
• FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES - The size in bytes of local memory used by each thread of this function.
• FUNC_ATTRIBUTE_NUM_REGS - The number of registers used by each thread of this function.
• FUNC_ATTRIBUTE_PTX_VERSION - The PTX virtual architecture version for which the function was compiled.

  This value is the major PTX version * 10 + the minor PTX version, so a PTX version 1.3 function would return the value 13. Note that this may return the undefined value of 0 for cubins compiled prior to CUDA 3.0.

• FUNC_ATTRIBUTE_BINARY_VERSION - The binary architecture version for which the function was compiled.

  This value is the major binary version * 10 + the minor binary version, so a binary version 1.3 function would return the value 13. Note that this will return a value of 10 for legacy cubins that do not have a properly-encoded binary architecture version.

• FUNC_ATTRIBUTE_CACHE_MODE_CA - The attribute to indicate whether the function has been compiled with user specified option "-Xptxas --dlcm=ca" set.
• FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES - The maximum size in bytes of dynamically-allocated shared memory that can be used by this function.

  If the user-specified dynamic shared memory size is larger than this value, the launch will fail.

• FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT - On devices where the L1 cache and shared memory use the same hardware resources, this sets the shared memory carveout preference, in percent of the total shared memory. Refer to DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR.

  This is only a hint, and the driver can choose a different ratio if required to execute the function.

See Also:

Constant Field Values

CU_FUNC_ATTRIBUTE_CONST_SIZE_BYTES

public static final int CU_FUNC_ATTRIBUTE_CONST_SIZE_BYTES

Function properties. (CUfunction_attribute)

Enum values:

• FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK - The maximum number of threads per block, beyond which a launch of the function would fail. This number depends on both the function and the device on which the function is currently loaded.
• FUNC_ATTRIBUTE_SHARED_SIZE_BYTES - The size in bytes of statically-allocated shared memory required by this function. This does not include dynamically-allocated shared memory requested by the user at runtime.
• FUNC_ATTRIBUTE_CONST_SIZE_BYTES - The size in bytes of user-allocated constant memory required by this function.
• FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES - The size in bytes of local memory used by each thread of this function.
• FUNC_ATTRIBUTE_NUM_REGS - The number of registers used by each thread of this function.
• FUNC_ATTRIBUTE_PTX_VERSION - The PTX virtual architecture version for which the function was compiled.

  This value is the major PTX version * 10 + the minor PTX version, so a PTX version 1.3 function would return the value 13. Note that this may return the undefined value of 0 for cubins compiled prior to CUDA 3.0.

• FUNC_ATTRIBUTE_BINARY_VERSION - The binary architecture version for which the function was compiled.

  This value is the major binary version * 10 + the minor binary version, so a binary version 1.3 function would return the value 13. Note that this will return a value of 10 for legacy cubins that do not have a properly-encoded binary architecture version.

• FUNC_ATTRIBUTE_CACHE_MODE_CA - The attribute to indicate whether the function has been compiled with user specified option "-Xptxas --dlcm=ca" set.
• FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES - The maximum size in bytes of dynamically-allocated shared memory that can be used by this function.

  If the user-specified dynamic shared memory size is larger than this value, the launch will fail.

• FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT - On devices where the L1 cache and shared memory use the same hardware resources, this sets the shared memory carveout preference, in percent of the total shared memory. Refer to DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR.

  This is only a hint, and the driver can choose a different ratio if required to execute the function.

See Also:

Constant Field Values

CU_FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES

public static final int CU_FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES

Function properties. (CUfunction_attribute)

Enum values:

• FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK - The maximum number of threads per block, beyond which a launch of the function would fail. This number depends on both the function and the device on which the function is currently loaded.
• FUNC_ATTRIBUTE_SHARED_SIZE_BYTES - The size in bytes of statically-allocated shared memory required by this function. This does not include dynamically-allocated shared memory requested by the user at runtime.
• FUNC_ATTRIBUTE_CONST_SIZE_BYTES - The size in bytes of user-allocated constant memory required by this function.
• FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES - The size in bytes of local memory used by each thread of this function.
• FUNC_ATTRIBUTE_NUM_REGS - The number of registers used by each thread of this function.
• FUNC_ATTRIBUTE_PTX_VERSION - The PTX virtual architecture version for which the function was compiled.

  This value is the major PTX version * 10 + the minor PTX version, so a PTX version 1.3 function would return the value 13. Note that this may return the undefined value of 0 for cubins compiled prior to CUDA 3.0.

• FUNC_ATTRIBUTE_BINARY_VERSION - The binary architecture version for which the function was compiled.

  This value is the major binary version * 10 + the minor binary version, so a binary version 1.3 function would return the value 13. Note that this will return a value of 10 for legacy cubins that do not have a properly-encoded binary architecture version.

• FUNC_ATTRIBUTE_CACHE_MODE_CA - The attribute to indicate whether the function has been compiled with user specified option "-Xptxas --dlcm=ca" set.
• FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES - The maximum size in bytes of dynamically-allocated shared memory that can be used by this function.

  If the user-specified dynamic shared memory size is larger than this value, the launch will fail.

• FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT - On devices where the L1 cache and shared memory use the same hardware resources, this sets the shared memory carveout preference, in percent of the total shared memory. Refer to DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR.

  This is only a hint, and the driver can choose a different ratio if required to execute the function.

See Also:

Constant Field Values

CU_FUNC_ATTRIBUTE_NUM_REGS

public static final int CU_FUNC_ATTRIBUTE_NUM_REGS

Function properties. (CUfunction_attribute)

Enum values:

• FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK - The maximum number of threads per block, beyond which a launch of the function would fail. This number depends on both the function and the device on which the function is currently loaded.
• FUNC_ATTRIBUTE_SHARED_SIZE_BYTES - The size in bytes of statically-allocated shared memory required by this function. This does not include dynamically-allocated shared memory requested by the user at runtime.
• FUNC_ATTRIBUTE_CONST_SIZE_BYTES - The size in bytes of user-allocated constant memory required by this function.
• FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES - The size in bytes of local memory used by each thread of this function.
• FUNC_ATTRIBUTE_NUM_REGS - The number of registers used by each thread of this function.
• FUNC_ATTRIBUTE_PTX_VERSION - The PTX virtual architecture version for which the function was compiled.

  This value is the major PTX version * 10 + the minor PTX version, so a PTX version 1.3 function would return the value 13. Note that this may return the undefined value of 0 for cubins compiled prior to CUDA 3.0.

• FUNC_ATTRIBUTE_BINARY_VERSION - The binary architecture version for which the function was compiled.

  This value is the major binary version * 10 + the minor binary version, so a binary version 1.3 function would return the value 13. Note that this will return a value of 10 for legacy cubins that do not have a properly-encoded binary architecture version.

• FUNC_ATTRIBUTE_CACHE_MODE_CA - The attribute to indicate whether the function has been compiled with user specified option "-Xptxas --dlcm=ca" set.
• FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES - The maximum size in bytes of dynamically-allocated shared memory that can be used by this function.

  If the user-specified dynamic shared memory size is larger than this value, the launch will fail.

• FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT - On devices where the L1 cache and shared memory use the same hardware resources, this sets the shared memory carveout preference, in percent of the total shared memory. Refer to DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR.

  This is only a hint, and the driver can choose a different ratio if required to execute the function.

See Also:

Constant Field Values

CU_FUNC_ATTRIBUTE_PTX_VERSION

public static final int CU_FUNC_ATTRIBUTE_PTX_VERSION

Function properties. (CUfunction_attribute)

Enum values:

• FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK - The maximum number of threads per block, beyond which a launch of the function would fail. This number depends on both the function and the device on which the function is currently loaded.
• FUNC_ATTRIBUTE_SHARED_SIZE_BYTES - The size in bytes of statically-allocated shared memory required by this function. This does not include dynamically-allocated shared memory requested by the user at runtime.
• FUNC_ATTRIBUTE_CONST_SIZE_BYTES - The size in bytes of user-allocated constant memory required by this function.
• FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES - The size in bytes of local memory used by each thread of this function.
• FUNC_ATTRIBUTE_NUM_REGS - The number of registers used by each thread of this function.
• FUNC_ATTRIBUTE_PTX_VERSION - The PTX virtual architecture version for which the function was compiled.

  This value is the major PTX version * 10 + the minor PTX version, so a PTX version 1.3 function would return the value 13. Note that this may return the undefined value of 0 for cubins compiled prior to CUDA 3.0.

• FUNC_ATTRIBUTE_BINARY_VERSION - The binary architecture version for which the function was compiled.

  This value is the major binary version * 10 + the minor binary version, so a binary version 1.3 function would return the value 13. Note that this will return a value of 10 for legacy cubins that do not have a properly-encoded binary architecture version.

• FUNC_ATTRIBUTE_CACHE_MODE_CA - The attribute to indicate whether the function has been compiled with user specified option "-Xptxas --dlcm=ca" set.
• FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES - The maximum size in bytes of dynamically-allocated shared memory that can be used by this function.

  If the user-specified dynamic shared memory size is larger than this value, the launch will fail.

• FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT - On devices where the L1 cache and shared memory use the same hardware resources, this sets the shared memory carveout preference, in percent of the total shared memory. Refer to DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR.

  This is only a hint, and the driver can choose a different ratio if required to execute the function.

See Also:

Constant Field Values

CU_FUNC_ATTRIBUTE_BINARY_VERSION

public static final int CU_FUNC_ATTRIBUTE_BINARY_VERSION

Function properties. (CUfunction_attribute)

Enum values:

• FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK - The maximum number of threads per block, beyond which a launch of the function would fail. This number depends on both the function and the device on which the function is currently loaded.
• FUNC_ATTRIBUTE_SHARED_SIZE_BYTES - The size in bytes of statically-allocated shared memory required by this function. This does not include dynamically-allocated shared memory requested by the user at runtime.
• FUNC_ATTRIBUTE_CONST_SIZE_BYTES - The size in bytes of user-allocated constant memory required by this function.
• FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES - The size in bytes of local memory used by each thread of this function.
• FUNC_ATTRIBUTE_NUM_REGS - The number of registers used by each thread of this function.
• FUNC_ATTRIBUTE_PTX_VERSION - The PTX virtual architecture version for which the function was compiled.

  This value is the major PTX version * 10 + the minor PTX version, so a PTX version 1.3 function would return the value 13. Note that this may return the undefined value of 0 for cubins compiled prior to CUDA 3.0.

• FUNC_ATTRIBUTE_BINARY_VERSION - The binary architecture version for which the function was compiled.

  This value is the major binary version * 10 + the minor binary version, so a binary version 1.3 function would return the value 13. Note that this will return a value of 10 for legacy cubins that do not have a properly-encoded binary architecture version.

• FUNC_ATTRIBUTE_CACHE_MODE_CA - The attribute to indicate whether the function has been compiled with user specified option "-Xptxas --dlcm=ca" set.
• FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES - The maximum size in bytes of dynamically-allocated shared memory that can be used by this function.

  If the user-specified dynamic shared memory size is larger than this value, the launch will fail.

• FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT - On devices where the L1 cache and shared memory use the same hardware resources, this sets the shared memory carveout preference, in percent of the total shared memory. Refer to DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR.

  This is only a hint, and the driver can choose a different ratio if required to execute the function.

See Also:

Constant Field Values

CU_FUNC_ATTRIBUTE_CACHE_MODE_CA

public static final int CU_FUNC_ATTRIBUTE_CACHE_MODE_CA

Function properties. (CUfunction_attribute)

Enum values:

• FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK - The maximum number of threads per block, beyond which a launch of the function would fail. This number depends on both the function and the device on which the function is currently loaded.
• FUNC_ATTRIBUTE_SHARED_SIZE_BYTES - The size in bytes of statically-allocated shared memory required by this function. This does not include dynamically-allocated shared memory requested by the user at runtime.
• FUNC_ATTRIBUTE_CONST_SIZE_BYTES - The size in bytes of user-allocated constant memory required by this function.
• FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES - The size in bytes of local memory used by each thread of this function.
• FUNC_ATTRIBUTE_NUM_REGS - The number of registers used by each thread of this function.
• FUNC_ATTRIBUTE_PTX_VERSION - The PTX virtual architecture version for which the function was compiled.

  This value is the major PTX version * 10 + the minor PTX version, so a PTX version 1.3 function would return the value 13. Note that this may return the undefined value of 0 for cubins compiled prior to CUDA 3.0.

• FUNC_ATTRIBUTE_BINARY_VERSION - The binary architecture version for which the function was compiled.

  This value is the major binary version * 10 + the minor binary version, so a binary version 1.3 function would return the value 13. Note that this will return a value of 10 for legacy cubins that do not have a properly-encoded binary architecture version.

• FUNC_ATTRIBUTE_CACHE_MODE_CA - The attribute to indicate whether the function has been compiled with user specified option "-Xptxas --dlcm=ca" set.
• FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES - The maximum size in bytes of dynamically-allocated shared memory that can be used by this function.

  If the user-specified dynamic shared memory size is larger than this value, the launch will fail.

• FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT - On devices where the L1 cache and shared memory use the same hardware resources, this sets the shared memory carveout preference, in percent of the total shared memory. Refer to DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR.

  This is only a hint, and the driver can choose a different ratio if required to execute the function.

See Also:

Constant Field Values

CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES

public static final int CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES

Function properties. (CUfunction_attribute)

Enum values:

• FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK - The maximum number of threads per block, beyond which a launch of the function would fail. This number depends on both the function and the device on which the function is currently loaded.
• FUNC_ATTRIBUTE_SHARED_SIZE_BYTES - The size in bytes of statically-allocated shared memory required by this function. This does not include dynamically-allocated shared memory requested by the user at runtime.
• FUNC_ATTRIBUTE_CONST_SIZE_BYTES - The size in bytes of user-allocated constant memory required by this function.
• FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES - The size in bytes of local memory used by each thread of this function.
• FUNC_ATTRIBUTE_NUM_REGS - The number of registers used by each thread of this function.
• FUNC_ATTRIBUTE_PTX_VERSION - The PTX virtual architecture version for which the function was compiled.

  This value is the major PTX version * 10 + the minor PTX version, so a PTX version 1.3 function would return the value 13. Note that this may return the undefined value of 0 for cubins compiled prior to CUDA 3.0.

• FUNC_ATTRIBUTE_BINARY_VERSION - The binary architecture version for which the function was compiled.

  This value is the major binary version * 10 + the minor binary version, so a binary version 1.3 function would return the value 13. Note that this will return a value of 10 for legacy cubins that do not have a properly-encoded binary architecture version.

• FUNC_ATTRIBUTE_CACHE_MODE_CA - The attribute to indicate whether the function has been compiled with user specified option "-Xptxas --dlcm=ca" set.
• FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES - The maximum size in bytes of dynamically-allocated shared memory that can be used by this function.

  If the user-specified dynamic shared memory size is larger than this value, the launch will fail.

• FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT - On devices where the L1 cache and shared memory use the same hardware resources, this sets the shared memory carveout preference, in percent of the total shared memory. Refer to DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR.

  This is only a hint, and the driver can choose a different ratio if required to execute the function.

See Also:

Constant Field Values

CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT

public static final int CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT

Function properties. (CUfunction_attribute)

Enum values:

• FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK - The maximum number of threads per block, beyond which a launch of the function would fail. This number depends on both the function and the device on which the function is currently loaded.
• FUNC_ATTRIBUTE_SHARED_SIZE_BYTES - The size in bytes of statically-allocated shared memory required by this function. This does not include dynamically-allocated shared memory requested by the user at runtime.
• FUNC_ATTRIBUTE_CONST_SIZE_BYTES - The size in bytes of user-allocated constant memory required by this function.
• FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES - The size in bytes of local memory used by each thread of this function.
• FUNC_ATTRIBUTE_NUM_REGS - The number of registers used by each thread of this function.
• FUNC_ATTRIBUTE_PTX_VERSION - The PTX virtual architecture version for which the function was compiled.

  This value is the major PTX version * 10 + the minor PTX version, so a PTX version 1.3 function would return the value 13. Note that this may return the undefined value of 0 for cubins compiled prior to CUDA 3.0.

• FUNC_ATTRIBUTE_BINARY_VERSION - The binary architecture version for which the function was compiled.

  This value is the major binary version * 10 + the minor binary version, so a binary version 1.3 function would return the value 13. Note that this will return a value of 10 for legacy cubins that do not have a properly-encoded binary architecture version.

• FUNC_ATTRIBUTE_CACHE_MODE_CA - The attribute to indicate whether the function has been compiled with user specified option "-Xptxas --dlcm=ca" set.
• FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES - The maximum size in bytes of dynamically-allocated shared memory that can be used by this function.

  If the user-specified dynamic shared memory size is larger than this value, the launch will fail.

• FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT - On devices where the L1 cache and shared memory use the same hardware resources, this sets the shared memory carveout preference, in percent of the total shared memory. Refer to DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR.

  This is only a hint, and the driver can choose a different ratio if required to execute the function.

See Also:

Constant Field Values

CU_FUNC_CACHE_PREFER_NONE

public static final int CU_FUNC_CACHE_PREFER_NONE

Function cache configurations. (CUfunc_cache)

Enum values:

• FUNC_CACHE_PREFER_NONE - no preference for shared memory or L1 (default)
• FUNC_CACHE_PREFER_SHARED - prefer larger shared memory and smaller L1 cache
• FUNC_CACHE_PREFER_L1 - prefer larger L1 cache and smaller shared memory
• FUNC_CACHE_PREFER_EQUAL - prefer equal sized L1 cache and shared memory

See Also:

Constant Field Values

CU_FUNC_CACHE_PREFER_SHARED

public static final int CU_FUNC_CACHE_PREFER_SHARED

Function cache configurations. (CUfunc_cache)

Enum values:

• FUNC_CACHE_PREFER_NONE - no preference for shared memory or L1 (default)
• FUNC_CACHE_PREFER_SHARED - prefer larger shared memory and smaller L1 cache
• FUNC_CACHE_PREFER_L1 - prefer larger L1 cache and smaller shared memory
• FUNC_CACHE_PREFER_EQUAL - prefer equal sized L1 cache and shared memory

See Also:

Constant Field Values

CU_FUNC_CACHE_PREFER_L1

public static final int CU_FUNC_CACHE_PREFER_L1

Function cache configurations. (CUfunc_cache)

Enum values:

• FUNC_CACHE_PREFER_NONE - no preference for shared memory or L1 (default)
• FUNC_CACHE_PREFER_SHARED - prefer larger shared memory and smaller L1 cache
• FUNC_CACHE_PREFER_L1 - prefer larger L1 cache and smaller shared memory
• FUNC_CACHE_PREFER_EQUAL - prefer equal sized L1 cache and shared memory

See Also:

Constant Field Values

CU_FUNC_CACHE_PREFER_EQUAL

public static final int CU_FUNC_CACHE_PREFER_EQUAL

Function cache configurations. (CUfunc_cache)

Enum values:

• FUNC_CACHE_PREFER_NONE - no preference for shared memory or L1 (default)
• FUNC_CACHE_PREFER_SHARED - prefer larger shared memory and smaller L1 cache
• FUNC_CACHE_PREFER_L1 - prefer larger L1 cache and smaller shared memory
• FUNC_CACHE_PREFER_EQUAL - prefer equal sized L1 cache and shared memory

See Also:

Constant Field Values

CU_MEMORYTYPE_HOST

public static final int CU_MEMORYTYPE_HOST

Memory types. (CUmemorytype)

Enum values:

• MEMORYTYPE_HOST - Host memory
• MEMORYTYPE_DEVICE - Device memory
• MEMORYTYPE_ARRAY - Array memory
• MEMORYTYPE_UNIFIED - Unified device or host memory

See Also:

Constant Field Values

CU_MEMORYTYPE_DEVICE

public static final int CU_MEMORYTYPE_DEVICE

Memory types. (CUmemorytype)

Enum values:

• MEMORYTYPE_HOST - Host memory
• MEMORYTYPE_DEVICE - Device memory
• MEMORYTYPE_ARRAY - Array memory
• MEMORYTYPE_UNIFIED - Unified device or host memory

See Also:

Constant Field Values

CU_MEMORYTYPE_ARRAY

public static final int CU_MEMORYTYPE_ARRAY

Memory types. (CUmemorytype)

Enum values:

• MEMORYTYPE_HOST - Host memory
• MEMORYTYPE_DEVICE - Device memory
• MEMORYTYPE_ARRAY - Array memory
• MEMORYTYPE_UNIFIED - Unified device or host memory

See Also:

Constant Field Values

CU_MEMORYTYPE_UNIFIED

public static final int CU_MEMORYTYPE_UNIFIED

Memory types. (CUmemorytype)

Enum values:

• MEMORYTYPE_HOST - Host memory
• MEMORYTYPE_DEVICE - Device memory
• MEMORYTYPE_ARRAY - Array memory
• MEMORYTYPE_UNIFIED - Unified device or host memory

See Also:

Constant Field Values

CU_COMPUTEMODE_DEFAULT

public static final int CU_COMPUTEMODE_DEFAULT

Compute Modes. (CUcomputemode)

Enum values:

• COMPUTEMODE_DEFAULT - Default compute mode (Multiple contexts allowed per device)
• COMPUTEMODE_PROHIBITED - Compute-prohibited mode (No contexts can be created on this device at this time)
• COMPUTEMODE_EXCLUSIVE_PROCESS - Compute-exclusive-process mode (Only one context used by a single process can be present on this device at a time)

See Also:

Constant Field Values

CU_COMPUTEMODE_PROHIBITED

public static final int CU_COMPUTEMODE_PROHIBITED

Compute Modes. (CUcomputemode)

Enum values:

• COMPUTEMODE_DEFAULT - Default compute mode (Multiple contexts allowed per device)
• COMPUTEMODE_PROHIBITED - Compute-prohibited mode (No contexts can be created on this device at this time)
• COMPUTEMODE_EXCLUSIVE_PROCESS - Compute-exclusive-process mode (Only one context used by a single process can be present on this device at a time)

See Also:

Constant Field Values

CU_COMPUTEMODE_EXCLUSIVE_PROCESS

public static final int CU_COMPUTEMODE_EXCLUSIVE_PROCESS

Compute Modes. (CUcomputemode)

Enum values:

• COMPUTEMODE_DEFAULT - Default compute mode (Multiple contexts allowed per device)
• COMPUTEMODE_PROHIBITED - Compute-prohibited mode (No contexts can be created on this device at this time)
• COMPUTEMODE_EXCLUSIVE_PROCESS - Compute-exclusive-process mode (Only one context used by a single process can be present on this device at a time)

See Also:

Constant Field Values

CU_JIT_MAX_REGISTERS

public static final int CU_JIT_MAX_REGISTERS

Online compiler and linker options. (CUjit_option)

Enum values:

• JIT_MAX_REGISTERS - Max number of registers that a thread may use.

  Option type: unsigned int. Applies to: compiler only

• JIT_THREADS_PER_BLOCK - IN: Specifies minimum number of threads per block to target compilation for

  OUT: Returns the number of threads the compiler actually targeted.

  This restricts the resource utilization fo the compiler (e.g. max registers) such that a block with the given number of threads should be able to launch based on register limitations. Note, this option does not currently take into account any other resource limitations, such as shared memory utilization.

  Cannot be combined with JIT_TARGET. Option type: unsigned int. Applies to: compiler only

• JIT_WALL_TIME - Overwrites the option value with the total wall clock time, in milliseconds, spent in the compiler and linker.

  Option type: float. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER - Pointer to a buffer in which to print any log messages that are informational in nature (the buffer size is specified via option JIT_INFO_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER - Pointer to a buffer in which to print any log messages that reflect errors (the buffer size is specified via option JIT_ERROR_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_OPTIMIZATION_LEVEL - Level of optimizations to apply to generated code (0 - 4), with 4 being the default and highest level of optimizations.

  Option type: unsigned int. Applies to: compiler only

• JIT_TARGET_FROM_CUCONTEXT - No option value required. Determines the target based on the current attached context (default).

  Option type: No option value needed. Applies to: compiler and linker

• JIT_TARGET - Target is chosen based on supplied CUjit_target. Cannot be combined with JIT_THREADS_PER_BLOCK.

  Option type: unsigned int for enumerated type CUjit_target. Applies to: compiler and linker

• JIT_FALLBACK_STRATEGY - Specifies choice of fallback strategy if matching cubin is not found.

  Choice is based on supplied CUjit_fallback. This option cannot be used with cuLink* APIs as the linker requires exact matches.

  Option type: unsigned int for enumerated type CUjit_fallback. Applies to: compiler only

• JIT_GENERATE_DEBUG_INFO - Specifies whether to create debug information in output (-g) (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_LOG_VERBOSE - Generate verbose log messages (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_GENERATE_LINE_INFO - Generate line number information (-lineinfo) (0: false, default).

  Option type: int. Applies to: compiler only

• JIT_CACHE_MODE - Specifies whether to enable caching explicitly (-dlcm). Choice is based on supplied CUjit_cacheMode_enum.

  Option type: unsigned int for enumerated type CUjit_cacheMode_enum. Applies to: compiler only

• JIT_NEW_SM3X_OPT - Used for internal purposes only, in this version of CUDA.
• JIT_FAST_COMPILE - Used for internal purposes only, in this version of CUDA.
• JIT_GLOBAL_SYMBOL_NAMES - Array of device symbol names that will be relocated to the corresponing host addresses stored in JIT_GLOBAL_SYMBOL_ADDRESSES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries. When loding a device module, driver will relocate all encountered unresolved symbols to the host addresses. It is only allowed to register symbols that correspond to unresolved global variables. It is illegal to register the same device symbol at multiple addresses.

  Option type: const char **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_ADDRESSES - Array of host addresses that will be used to relocate corresponding device symbols stored in JIT_GLOBAL_SYMBOL_NAMES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries.

  Option type: void **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_COUNT - Number of entries in JIT_GLOBAL_SYMBOL_NAMES and JIT_GLOBAL_SYMBOL_ADDRESSES arrays.

  Option type: unsigned int. Applies to: dynamic linker only

See Also:

Constant Field Values

CU_JIT_THREADS_PER_BLOCK

public static final int CU_JIT_THREADS_PER_BLOCK

Online compiler and linker options. (CUjit_option)

Enum values:

• JIT_MAX_REGISTERS - Max number of registers that a thread may use.

  Option type: unsigned int. Applies to: compiler only

• JIT_THREADS_PER_BLOCK - IN: Specifies minimum number of threads per block to target compilation for

  OUT: Returns the number of threads the compiler actually targeted.

  This restricts the resource utilization fo the compiler (e.g. max registers) such that a block with the given number of threads should be able to launch based on register limitations. Note, this option does not currently take into account any other resource limitations, such as shared memory utilization.

  Cannot be combined with JIT_TARGET. Option type: unsigned int. Applies to: compiler only

• JIT_WALL_TIME - Overwrites the option value with the total wall clock time, in milliseconds, spent in the compiler and linker.

  Option type: float. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER - Pointer to a buffer in which to print any log messages that are informational in nature (the buffer size is specified via option JIT_INFO_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER - Pointer to a buffer in which to print any log messages that reflect errors (the buffer size is specified via option JIT_ERROR_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_OPTIMIZATION_LEVEL - Level of optimizations to apply to generated code (0 - 4), with 4 being the default and highest level of optimizations.

  Option type: unsigned int. Applies to: compiler only

• JIT_TARGET_FROM_CUCONTEXT - No option value required. Determines the target based on the current attached context (default).

  Option type: No option value needed. Applies to: compiler and linker

• JIT_TARGET - Target is chosen based on supplied CUjit_target. Cannot be combined with JIT_THREADS_PER_BLOCK.

  Option type: unsigned int for enumerated type CUjit_target. Applies to: compiler and linker

• JIT_FALLBACK_STRATEGY - Specifies choice of fallback strategy if matching cubin is not found.

  Choice is based on supplied CUjit_fallback. This option cannot be used with cuLink* APIs as the linker requires exact matches.

  Option type: unsigned int for enumerated type CUjit_fallback. Applies to: compiler only

• JIT_GENERATE_DEBUG_INFO - Specifies whether to create debug information in output (-g) (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_LOG_VERBOSE - Generate verbose log messages (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_GENERATE_LINE_INFO - Generate line number information (-lineinfo) (0: false, default).

  Option type: int. Applies to: compiler only

• JIT_CACHE_MODE - Specifies whether to enable caching explicitly (-dlcm). Choice is based on supplied CUjit_cacheMode_enum.

  Option type: unsigned int for enumerated type CUjit_cacheMode_enum. Applies to: compiler only

• JIT_NEW_SM3X_OPT - Used for internal purposes only, in this version of CUDA.
• JIT_FAST_COMPILE - Used for internal purposes only, in this version of CUDA.
• JIT_GLOBAL_SYMBOL_NAMES - Array of device symbol names that will be relocated to the corresponing host addresses stored in JIT_GLOBAL_SYMBOL_ADDRESSES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries. When loding a device module, driver will relocate all encountered unresolved symbols to the host addresses. It is only allowed to register symbols that correspond to unresolved global variables. It is illegal to register the same device symbol at multiple addresses.

  Option type: const char **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_ADDRESSES - Array of host addresses that will be used to relocate corresponding device symbols stored in JIT_GLOBAL_SYMBOL_NAMES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries.

  Option type: void **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_COUNT - Number of entries in JIT_GLOBAL_SYMBOL_NAMES and JIT_GLOBAL_SYMBOL_ADDRESSES arrays.

  Option type: unsigned int. Applies to: dynamic linker only

See Also:

Constant Field Values

CU_JIT_WALL_TIME

public static final int CU_JIT_WALL_TIME

Online compiler and linker options. (CUjit_option)

Enum values:

• JIT_MAX_REGISTERS - Max number of registers that a thread may use.

  Option type: unsigned int. Applies to: compiler only

• JIT_THREADS_PER_BLOCK - IN: Specifies minimum number of threads per block to target compilation for

  OUT: Returns the number of threads the compiler actually targeted.

  This restricts the resource utilization fo the compiler (e.g. max registers) such that a block with the given number of threads should be able to launch based on register limitations. Note, this option does not currently take into account any other resource limitations, such as shared memory utilization.

  Cannot be combined with JIT_TARGET. Option type: unsigned int. Applies to: compiler only

• JIT_WALL_TIME - Overwrites the option value with the total wall clock time, in milliseconds, spent in the compiler and linker.

  Option type: float. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER - Pointer to a buffer in which to print any log messages that are informational in nature (the buffer size is specified via option JIT_INFO_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER - Pointer to a buffer in which to print any log messages that reflect errors (the buffer size is specified via option JIT_ERROR_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_OPTIMIZATION_LEVEL - Level of optimizations to apply to generated code (0 - 4), with 4 being the default and highest level of optimizations.

  Option type: unsigned int. Applies to: compiler only

• JIT_TARGET_FROM_CUCONTEXT - No option value required. Determines the target based on the current attached context (default).

  Option type: No option value needed. Applies to: compiler and linker

• JIT_TARGET - Target is chosen based on supplied CUjit_target. Cannot be combined with JIT_THREADS_PER_BLOCK.

  Option type: unsigned int for enumerated type CUjit_target. Applies to: compiler and linker

• JIT_FALLBACK_STRATEGY - Specifies choice of fallback strategy if matching cubin is not found.

  Choice is based on supplied CUjit_fallback. This option cannot be used with cuLink* APIs as the linker requires exact matches.

  Option type: unsigned int for enumerated type CUjit_fallback. Applies to: compiler only

• JIT_GENERATE_DEBUG_INFO - Specifies whether to create debug information in output (-g) (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_LOG_VERBOSE - Generate verbose log messages (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_GENERATE_LINE_INFO - Generate line number information (-lineinfo) (0: false, default).

  Option type: int. Applies to: compiler only

• JIT_CACHE_MODE - Specifies whether to enable caching explicitly (-dlcm). Choice is based on supplied CUjit_cacheMode_enum.

  Option type: unsigned int for enumerated type CUjit_cacheMode_enum. Applies to: compiler only

• JIT_NEW_SM3X_OPT - Used for internal purposes only, in this version of CUDA.
• JIT_FAST_COMPILE - Used for internal purposes only, in this version of CUDA.
• JIT_GLOBAL_SYMBOL_NAMES - Array of device symbol names that will be relocated to the corresponing host addresses stored in JIT_GLOBAL_SYMBOL_ADDRESSES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries. When loding a device module, driver will relocate all encountered unresolved symbols to the host addresses. It is only allowed to register symbols that correspond to unresolved global variables. It is illegal to register the same device symbol at multiple addresses.

  Option type: const char **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_ADDRESSES - Array of host addresses that will be used to relocate corresponding device symbols stored in JIT_GLOBAL_SYMBOL_NAMES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries.

  Option type: void **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_COUNT - Number of entries in JIT_GLOBAL_SYMBOL_NAMES and JIT_GLOBAL_SYMBOL_ADDRESSES arrays.

  Option type: unsigned int. Applies to: dynamic linker only

See Also:

Constant Field Values

CU_JIT_INFO_LOG_BUFFER

public static final int CU_JIT_INFO_LOG_BUFFER

Online compiler and linker options. (CUjit_option)

Enum values:

• JIT_MAX_REGISTERS - Max number of registers that a thread may use.

  Option type: unsigned int. Applies to: compiler only

• JIT_THREADS_PER_BLOCK - IN: Specifies minimum number of threads per block to target compilation for

  OUT: Returns the number of threads the compiler actually targeted.

  This restricts the resource utilization fo the compiler (e.g. max registers) such that a block with the given number of threads should be able to launch based on register limitations. Note, this option does not currently take into account any other resource limitations, such as shared memory utilization.

  Cannot be combined with JIT_TARGET. Option type: unsigned int. Applies to: compiler only

• JIT_WALL_TIME - Overwrites the option value with the total wall clock time, in milliseconds, spent in the compiler and linker.

  Option type: float. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER - Pointer to a buffer in which to print any log messages that are informational in nature (the buffer size is specified via option JIT_INFO_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER - Pointer to a buffer in which to print any log messages that reflect errors (the buffer size is specified via option JIT_ERROR_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_OPTIMIZATION_LEVEL - Level of optimizations to apply to generated code (0 - 4), with 4 being the default and highest level of optimizations.

  Option type: unsigned int. Applies to: compiler only

• JIT_TARGET_FROM_CUCONTEXT - No option value required. Determines the target based on the current attached context (default).

  Option type: No option value needed. Applies to: compiler and linker

• JIT_TARGET - Target is chosen based on supplied CUjit_target. Cannot be combined with JIT_THREADS_PER_BLOCK.

  Option type: unsigned int for enumerated type CUjit_target. Applies to: compiler and linker

• JIT_FALLBACK_STRATEGY - Specifies choice of fallback strategy if matching cubin is not found.

  Choice is based on supplied CUjit_fallback. This option cannot be used with cuLink* APIs as the linker requires exact matches.

  Option type: unsigned int for enumerated type CUjit_fallback. Applies to: compiler only

• JIT_GENERATE_DEBUG_INFO - Specifies whether to create debug information in output (-g) (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_LOG_VERBOSE - Generate verbose log messages (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_GENERATE_LINE_INFO - Generate line number information (-lineinfo) (0: false, default).

  Option type: int. Applies to: compiler only

• JIT_CACHE_MODE - Specifies whether to enable caching explicitly (-dlcm). Choice is based on supplied CUjit_cacheMode_enum.

  Option type: unsigned int for enumerated type CUjit_cacheMode_enum. Applies to: compiler only

• JIT_NEW_SM3X_OPT - Used for internal purposes only, in this version of CUDA.
• JIT_FAST_COMPILE - Used for internal purposes only, in this version of CUDA.
• JIT_GLOBAL_SYMBOL_NAMES - Array of device symbol names that will be relocated to the corresponing host addresses stored in JIT_GLOBAL_SYMBOL_ADDRESSES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries. When loding a device module, driver will relocate all encountered unresolved symbols to the host addresses. It is only allowed to register symbols that correspond to unresolved global variables. It is illegal to register the same device symbol at multiple addresses.

  Option type: const char **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_ADDRESSES - Array of host addresses that will be used to relocate corresponding device symbols stored in JIT_GLOBAL_SYMBOL_NAMES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries.

  Option type: void **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_COUNT - Number of entries in JIT_GLOBAL_SYMBOL_NAMES and JIT_GLOBAL_SYMBOL_ADDRESSES arrays.

  Option type: unsigned int. Applies to: dynamic linker only

See Also:

Constant Field Values

CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES

public static final int CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES

Online compiler and linker options. (CUjit_option)

Enum values:

• JIT_MAX_REGISTERS - Max number of registers that a thread may use.

  Option type: unsigned int. Applies to: compiler only

• JIT_THREADS_PER_BLOCK - IN: Specifies minimum number of threads per block to target compilation for

  OUT: Returns the number of threads the compiler actually targeted.

  This restricts the resource utilization fo the compiler (e.g. max registers) such that a block with the given number of threads should be able to launch based on register limitations. Note, this option does not currently take into account any other resource limitations, such as shared memory utilization.

  Cannot be combined with JIT_TARGET. Option type: unsigned int. Applies to: compiler only

• JIT_WALL_TIME - Overwrites the option value with the total wall clock time, in milliseconds, spent in the compiler and linker.

  Option type: float. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER - Pointer to a buffer in which to print any log messages that are informational in nature (the buffer size is specified via option JIT_INFO_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER - Pointer to a buffer in which to print any log messages that reflect errors (the buffer size is specified via option JIT_ERROR_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_OPTIMIZATION_LEVEL - Level of optimizations to apply to generated code (0 - 4), with 4 being the default and highest level of optimizations.

  Option type: unsigned int. Applies to: compiler only

• JIT_TARGET_FROM_CUCONTEXT - No option value required. Determines the target based on the current attached context (default).

  Option type: No option value needed. Applies to: compiler and linker

• JIT_TARGET - Target is chosen based on supplied CUjit_target. Cannot be combined with JIT_THREADS_PER_BLOCK.

  Option type: unsigned int for enumerated type CUjit_target. Applies to: compiler and linker

• JIT_FALLBACK_STRATEGY - Specifies choice of fallback strategy if matching cubin is not found.

  Choice is based on supplied CUjit_fallback. This option cannot be used with cuLink* APIs as the linker requires exact matches.

  Option type: unsigned int for enumerated type CUjit_fallback. Applies to: compiler only

• JIT_GENERATE_DEBUG_INFO - Specifies whether to create debug information in output (-g) (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_LOG_VERBOSE - Generate verbose log messages (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_GENERATE_LINE_INFO - Generate line number information (-lineinfo) (0: false, default).

  Option type: int. Applies to: compiler only

• JIT_CACHE_MODE - Specifies whether to enable caching explicitly (-dlcm). Choice is based on supplied CUjit_cacheMode_enum.

  Option type: unsigned int for enumerated type CUjit_cacheMode_enum. Applies to: compiler only

• JIT_NEW_SM3X_OPT - Used for internal purposes only, in this version of CUDA.
• JIT_FAST_COMPILE - Used for internal purposes only, in this version of CUDA.
• JIT_GLOBAL_SYMBOL_NAMES - Array of device symbol names that will be relocated to the corresponing host addresses stored in JIT_GLOBAL_SYMBOL_ADDRESSES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries. When loding a device module, driver will relocate all encountered unresolved symbols to the host addresses. It is only allowed to register symbols that correspond to unresolved global variables. It is illegal to register the same device symbol at multiple addresses.

  Option type: const char **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_ADDRESSES - Array of host addresses that will be used to relocate corresponding device symbols stored in JIT_GLOBAL_SYMBOL_NAMES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries.

  Option type: void **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_COUNT - Number of entries in JIT_GLOBAL_SYMBOL_NAMES and JIT_GLOBAL_SYMBOL_ADDRESSES arrays.

  Option type: unsigned int. Applies to: dynamic linker only

See Also:

Constant Field Values

CU_JIT_ERROR_LOG_BUFFER

public static final int CU_JIT_ERROR_LOG_BUFFER

Online compiler and linker options. (CUjit_option)

Enum values:

• JIT_MAX_REGISTERS - Max number of registers that a thread may use.

  Option type: unsigned int. Applies to: compiler only

• JIT_THREADS_PER_BLOCK - IN: Specifies minimum number of threads per block to target compilation for

  OUT: Returns the number of threads the compiler actually targeted.

  This restricts the resource utilization fo the compiler (e.g. max registers) such that a block with the given number of threads should be able to launch based on register limitations. Note, this option does not currently take into account any other resource limitations, such as shared memory utilization.

  Cannot be combined with JIT_TARGET. Option type: unsigned int. Applies to: compiler only

• JIT_WALL_TIME - Overwrites the option value with the total wall clock time, in milliseconds, spent in the compiler and linker.

  Option type: float. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER - Pointer to a buffer in which to print any log messages that are informational in nature (the buffer size is specified via option JIT_INFO_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER - Pointer to a buffer in which to print any log messages that reflect errors (the buffer size is specified via option JIT_ERROR_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_OPTIMIZATION_LEVEL - Level of optimizations to apply to generated code (0 - 4), with 4 being the default and highest level of optimizations.

  Option type: unsigned int. Applies to: compiler only

• JIT_TARGET_FROM_CUCONTEXT - No option value required. Determines the target based on the current attached context (default).

  Option type: No option value needed. Applies to: compiler and linker

• JIT_TARGET - Target is chosen based on supplied CUjit_target. Cannot be combined with JIT_THREADS_PER_BLOCK.

  Option type: unsigned int for enumerated type CUjit_target. Applies to: compiler and linker

• JIT_FALLBACK_STRATEGY - Specifies choice of fallback strategy if matching cubin is not found.

  Choice is based on supplied CUjit_fallback. This option cannot be used with cuLink* APIs as the linker requires exact matches.

  Option type: unsigned int for enumerated type CUjit_fallback. Applies to: compiler only

• JIT_GENERATE_DEBUG_INFO - Specifies whether to create debug information in output (-g) (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_LOG_VERBOSE - Generate verbose log messages (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_GENERATE_LINE_INFO - Generate line number information (-lineinfo) (0: false, default).

  Option type: int. Applies to: compiler only

• JIT_CACHE_MODE - Specifies whether to enable caching explicitly (-dlcm). Choice is based on supplied CUjit_cacheMode_enum.

  Option type: unsigned int for enumerated type CUjit_cacheMode_enum. Applies to: compiler only

• JIT_NEW_SM3X_OPT - Used for internal purposes only, in this version of CUDA.
• JIT_FAST_COMPILE - Used for internal purposes only, in this version of CUDA.
• JIT_GLOBAL_SYMBOL_NAMES - Array of device symbol names that will be relocated to the corresponing host addresses stored in JIT_GLOBAL_SYMBOL_ADDRESSES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries. When loding a device module, driver will relocate all encountered unresolved symbols to the host addresses. It is only allowed to register symbols that correspond to unresolved global variables. It is illegal to register the same device symbol at multiple addresses.

  Option type: const char **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_ADDRESSES - Array of host addresses that will be used to relocate corresponding device symbols stored in JIT_GLOBAL_SYMBOL_NAMES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries.

  Option type: void **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_COUNT - Number of entries in JIT_GLOBAL_SYMBOL_NAMES and JIT_GLOBAL_SYMBOL_ADDRESSES arrays.

  Option type: unsigned int. Applies to: dynamic linker only

See Also:

Constant Field Values

CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES

public static final int CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES

Online compiler and linker options. (CUjit_option)

Enum values:

• JIT_MAX_REGISTERS - Max number of registers that a thread may use.

  Option type: unsigned int. Applies to: compiler only

• JIT_THREADS_PER_BLOCK - IN: Specifies minimum number of threads per block to target compilation for

  OUT: Returns the number of threads the compiler actually targeted.

  This restricts the resource utilization fo the compiler (e.g. max registers) such that a block with the given number of threads should be able to launch based on register limitations. Note, this option does not currently take into account any other resource limitations, such as shared memory utilization.

  Cannot be combined with JIT_TARGET. Option type: unsigned int. Applies to: compiler only

• JIT_WALL_TIME - Overwrites the option value with the total wall clock time, in milliseconds, spent in the compiler and linker.

  Option type: float. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER - Pointer to a buffer in which to print any log messages that are informational in nature (the buffer size is specified via option JIT_INFO_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER - Pointer to a buffer in which to print any log messages that reflect errors (the buffer size is specified via option JIT_ERROR_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_OPTIMIZATION_LEVEL - Level of optimizations to apply to generated code (0 - 4), with 4 being the default and highest level of optimizations.

  Option type: unsigned int. Applies to: compiler only

• JIT_TARGET_FROM_CUCONTEXT - No option value required. Determines the target based on the current attached context (default).

  Option type: No option value needed. Applies to: compiler and linker

• JIT_TARGET - Target is chosen based on supplied CUjit_target. Cannot be combined with JIT_THREADS_PER_BLOCK.

  Option type: unsigned int for enumerated type CUjit_target. Applies to: compiler and linker

• JIT_FALLBACK_STRATEGY - Specifies choice of fallback strategy if matching cubin is not found.

  Choice is based on supplied CUjit_fallback. This option cannot be used with cuLink* APIs as the linker requires exact matches.

  Option type: unsigned int for enumerated type CUjit_fallback. Applies to: compiler only

• JIT_GENERATE_DEBUG_INFO - Specifies whether to create debug information in output (-g) (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_LOG_VERBOSE - Generate verbose log messages (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_GENERATE_LINE_INFO - Generate line number information (-lineinfo) (0: false, default).

  Option type: int. Applies to: compiler only

• JIT_CACHE_MODE - Specifies whether to enable caching explicitly (-dlcm). Choice is based on supplied CUjit_cacheMode_enum.

  Option type: unsigned int for enumerated type CUjit_cacheMode_enum. Applies to: compiler only

• JIT_NEW_SM3X_OPT - Used for internal purposes only, in this version of CUDA.
• JIT_FAST_COMPILE - Used for internal purposes only, in this version of CUDA.
• JIT_GLOBAL_SYMBOL_NAMES - Array of device symbol names that will be relocated to the corresponing host addresses stored in JIT_GLOBAL_SYMBOL_ADDRESSES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries. When loding a device module, driver will relocate all encountered unresolved symbols to the host addresses. It is only allowed to register symbols that correspond to unresolved global variables. It is illegal to register the same device symbol at multiple addresses.

  Option type: const char **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_ADDRESSES - Array of host addresses that will be used to relocate corresponding device symbols stored in JIT_GLOBAL_SYMBOL_NAMES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries.

  Option type: void **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_COUNT - Number of entries in JIT_GLOBAL_SYMBOL_NAMES and JIT_GLOBAL_SYMBOL_ADDRESSES arrays.

  Option type: unsigned int. Applies to: dynamic linker only

See Also:

Constant Field Values

CU_JIT_OPTIMIZATION_LEVEL

public static final int CU_JIT_OPTIMIZATION_LEVEL

Online compiler and linker options. (CUjit_option)

Enum values:

• JIT_MAX_REGISTERS - Max number of registers that a thread may use.

  Option type: unsigned int. Applies to: compiler only

• JIT_THREADS_PER_BLOCK - IN: Specifies minimum number of threads per block to target compilation for

  OUT: Returns the number of threads the compiler actually targeted.

  This restricts the resource utilization fo the compiler (e.g. max registers) such that a block with the given number of threads should be able to launch based on register limitations. Note, this option does not currently take into account any other resource limitations, such as shared memory utilization.

  Cannot be combined with JIT_TARGET. Option type: unsigned int. Applies to: compiler only

• JIT_WALL_TIME - Overwrites the option value with the total wall clock time, in milliseconds, spent in the compiler and linker.

  Option type: float. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER - Pointer to a buffer in which to print any log messages that are informational in nature (the buffer size is specified via option JIT_INFO_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER - Pointer to a buffer in which to print any log messages that reflect errors (the buffer size is specified via option JIT_ERROR_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_OPTIMIZATION_LEVEL - Level of optimizations to apply to generated code (0 - 4), with 4 being the default and highest level of optimizations.

  Option type: unsigned int. Applies to: compiler only

• JIT_TARGET_FROM_CUCONTEXT - No option value required. Determines the target based on the current attached context (default).

  Option type: No option value needed. Applies to: compiler and linker

• JIT_TARGET - Target is chosen based on supplied CUjit_target. Cannot be combined with JIT_THREADS_PER_BLOCK.

  Option type: unsigned int for enumerated type CUjit_target. Applies to: compiler and linker

• JIT_FALLBACK_STRATEGY - Specifies choice of fallback strategy if matching cubin is not found.

  Choice is based on supplied CUjit_fallback. This option cannot be used with cuLink* APIs as the linker requires exact matches.

  Option type: unsigned int for enumerated type CUjit_fallback. Applies to: compiler only

• JIT_GENERATE_DEBUG_INFO - Specifies whether to create debug information in output (-g) (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_LOG_VERBOSE - Generate verbose log messages (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_GENERATE_LINE_INFO - Generate line number information (-lineinfo) (0: false, default).

  Option type: int. Applies to: compiler only

• JIT_CACHE_MODE - Specifies whether to enable caching explicitly (-dlcm). Choice is based on supplied CUjit_cacheMode_enum.

  Option type: unsigned int for enumerated type CUjit_cacheMode_enum. Applies to: compiler only

• JIT_NEW_SM3X_OPT - Used for internal purposes only, in this version of CUDA.
• JIT_FAST_COMPILE - Used for internal purposes only, in this version of CUDA.
• JIT_GLOBAL_SYMBOL_NAMES - Array of device symbol names that will be relocated to the corresponing host addresses stored in JIT_GLOBAL_SYMBOL_ADDRESSES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries. When loding a device module, driver will relocate all encountered unresolved symbols to the host addresses. It is only allowed to register symbols that correspond to unresolved global variables. It is illegal to register the same device symbol at multiple addresses.

  Option type: const char **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_ADDRESSES - Array of host addresses that will be used to relocate corresponding device symbols stored in JIT_GLOBAL_SYMBOL_NAMES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries.

  Option type: void **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_COUNT - Number of entries in JIT_GLOBAL_SYMBOL_NAMES and JIT_GLOBAL_SYMBOL_ADDRESSES arrays.

  Option type: unsigned int. Applies to: dynamic linker only

See Also:

Constant Field Values

CU_JIT_TARGET_FROM_CUCONTEXT

public static final int CU_JIT_TARGET_FROM_CUCONTEXT

Online compiler and linker options. (CUjit_option)

Enum values:

• JIT_MAX_REGISTERS - Max number of registers that a thread may use.

  Option type: unsigned int. Applies to: compiler only

• JIT_THREADS_PER_BLOCK - IN: Specifies minimum number of threads per block to target compilation for

  OUT: Returns the number of threads the compiler actually targeted.

  This restricts the resource utilization fo the compiler (e.g. max registers) such that a block with the given number of threads should be able to launch based on register limitations. Note, this option does not currently take into account any other resource limitations, such as shared memory utilization.

  Cannot be combined with JIT_TARGET. Option type: unsigned int. Applies to: compiler only

• JIT_WALL_TIME - Overwrites the option value with the total wall clock time, in milliseconds, spent in the compiler and linker.

  Option type: float. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER - Pointer to a buffer in which to print any log messages that are informational in nature (the buffer size is specified via option JIT_INFO_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER - Pointer to a buffer in which to print any log messages that reflect errors (the buffer size is specified via option JIT_ERROR_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_OPTIMIZATION_LEVEL - Level of optimizations to apply to generated code (0 - 4), with 4 being the default and highest level of optimizations.

  Option type: unsigned int. Applies to: compiler only

• JIT_TARGET_FROM_CUCONTEXT - No option value required. Determines the target based on the current attached context (default).

  Option type: No option value needed. Applies to: compiler and linker

• JIT_TARGET - Target is chosen based on supplied CUjit_target. Cannot be combined with JIT_THREADS_PER_BLOCK.

  Option type: unsigned int for enumerated type CUjit_target. Applies to: compiler and linker

• JIT_FALLBACK_STRATEGY - Specifies choice of fallback strategy if matching cubin is not found.

  Choice is based on supplied CUjit_fallback. This option cannot be used with cuLink* APIs as the linker requires exact matches.

  Option type: unsigned int for enumerated type CUjit_fallback. Applies to: compiler only

• JIT_GENERATE_DEBUG_INFO - Specifies whether to create debug information in output (-g) (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_LOG_VERBOSE - Generate verbose log messages (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_GENERATE_LINE_INFO - Generate line number information (-lineinfo) (0: false, default).

  Option type: int. Applies to: compiler only

• JIT_CACHE_MODE - Specifies whether to enable caching explicitly (-dlcm). Choice is based on supplied CUjit_cacheMode_enum.

  Option type: unsigned int for enumerated type CUjit_cacheMode_enum. Applies to: compiler only

• JIT_NEW_SM3X_OPT - Used for internal purposes only, in this version of CUDA.
• JIT_FAST_COMPILE - Used for internal purposes only, in this version of CUDA.
• JIT_GLOBAL_SYMBOL_NAMES - Array of device symbol names that will be relocated to the corresponing host addresses stored in JIT_GLOBAL_SYMBOL_ADDRESSES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries. When loding a device module, driver will relocate all encountered unresolved symbols to the host addresses. It is only allowed to register symbols that correspond to unresolved global variables. It is illegal to register the same device symbol at multiple addresses.

  Option type: const char **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_ADDRESSES - Array of host addresses that will be used to relocate corresponding device symbols stored in JIT_GLOBAL_SYMBOL_NAMES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries.

  Option type: void **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_COUNT - Number of entries in JIT_GLOBAL_SYMBOL_NAMES and JIT_GLOBAL_SYMBOL_ADDRESSES arrays.

  Option type: unsigned int. Applies to: dynamic linker only

See Also:

Constant Field Values

CU_JIT_TARGET

public static final int CU_JIT_TARGET

Online compiler and linker options. (CUjit_option)

Enum values:

• JIT_MAX_REGISTERS - Max number of registers that a thread may use.

  Option type: unsigned int. Applies to: compiler only

• JIT_THREADS_PER_BLOCK - IN: Specifies minimum number of threads per block to target compilation for

  OUT: Returns the number of threads the compiler actually targeted.

  This restricts the resource utilization fo the compiler (e.g. max registers) such that a block with the given number of threads should be able to launch based on register limitations. Note, this option does not currently take into account any other resource limitations, such as shared memory utilization.

  Cannot be combined with JIT_TARGET. Option type: unsigned int. Applies to: compiler only

• JIT_WALL_TIME - Overwrites the option value with the total wall clock time, in milliseconds, spent in the compiler and linker.

  Option type: float. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER - Pointer to a buffer in which to print any log messages that are informational in nature (the buffer size is specified via option JIT_INFO_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER - Pointer to a buffer in which to print any log messages that reflect errors (the buffer size is specified via option JIT_ERROR_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_OPTIMIZATION_LEVEL - Level of optimizations to apply to generated code (0 - 4), with 4 being the default and highest level of optimizations.

  Option type: unsigned int. Applies to: compiler only

• JIT_TARGET_FROM_CUCONTEXT - No option value required. Determines the target based on the current attached context (default).

  Option type: No option value needed. Applies to: compiler and linker

• JIT_TARGET - Target is chosen based on supplied CUjit_target. Cannot be combined with JIT_THREADS_PER_BLOCK.

  Option type: unsigned int for enumerated type CUjit_target. Applies to: compiler and linker

• JIT_FALLBACK_STRATEGY - Specifies choice of fallback strategy if matching cubin is not found.

  Choice is based on supplied CUjit_fallback. This option cannot be used with cuLink* APIs as the linker requires exact matches.

  Option type: unsigned int for enumerated type CUjit_fallback. Applies to: compiler only

• JIT_GENERATE_DEBUG_INFO - Specifies whether to create debug information in output (-g) (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_LOG_VERBOSE - Generate verbose log messages (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_GENERATE_LINE_INFO - Generate line number information (-lineinfo) (0: false, default).

  Option type: int. Applies to: compiler only

• JIT_CACHE_MODE - Specifies whether to enable caching explicitly (-dlcm). Choice is based on supplied CUjit_cacheMode_enum.

  Option type: unsigned int for enumerated type CUjit_cacheMode_enum. Applies to: compiler only

• JIT_NEW_SM3X_OPT - Used for internal purposes only, in this version of CUDA.
• JIT_FAST_COMPILE - Used for internal purposes only, in this version of CUDA.
• JIT_GLOBAL_SYMBOL_NAMES - Array of device symbol names that will be relocated to the corresponing host addresses stored in JIT_GLOBAL_SYMBOL_ADDRESSES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries. When loding a device module, driver will relocate all encountered unresolved symbols to the host addresses. It is only allowed to register symbols that correspond to unresolved global variables. It is illegal to register the same device symbol at multiple addresses.

  Option type: const char **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_ADDRESSES - Array of host addresses that will be used to relocate corresponding device symbols stored in JIT_GLOBAL_SYMBOL_NAMES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries.

  Option type: void **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_COUNT - Number of entries in JIT_GLOBAL_SYMBOL_NAMES and JIT_GLOBAL_SYMBOL_ADDRESSES arrays.

  Option type: unsigned int. Applies to: dynamic linker only

See Also:

Constant Field Values

CU_JIT_FALLBACK_STRATEGY

public static final int CU_JIT_FALLBACK_STRATEGY

Online compiler and linker options. (CUjit_option)

Enum values:

• JIT_MAX_REGISTERS - Max number of registers that a thread may use.

  Option type: unsigned int. Applies to: compiler only

• JIT_THREADS_PER_BLOCK - IN: Specifies minimum number of threads per block to target compilation for

  OUT: Returns the number of threads the compiler actually targeted.

  This restricts the resource utilization fo the compiler (e.g. max registers) such that a block with the given number of threads should be able to launch based on register limitations. Note, this option does not currently take into account any other resource limitations, such as shared memory utilization.

  Cannot be combined with JIT_TARGET. Option type: unsigned int. Applies to: compiler only

• JIT_WALL_TIME - Overwrites the option value with the total wall clock time, in milliseconds, spent in the compiler and linker.

  Option type: float. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER - Pointer to a buffer in which to print any log messages that are informational in nature (the buffer size is specified via option JIT_INFO_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER - Pointer to a buffer in which to print any log messages that reflect errors (the buffer size is specified via option JIT_ERROR_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_OPTIMIZATION_LEVEL - Level of optimizations to apply to generated code (0 - 4), with 4 being the default and highest level of optimizations.

  Option type: unsigned int. Applies to: compiler only

• JIT_TARGET_FROM_CUCONTEXT - No option value required. Determines the target based on the current attached context (default).

  Option type: No option value needed. Applies to: compiler and linker

• JIT_TARGET - Target is chosen based on supplied CUjit_target. Cannot be combined with JIT_THREADS_PER_BLOCK.

  Option type: unsigned int for enumerated type CUjit_target. Applies to: compiler and linker

• JIT_FALLBACK_STRATEGY - Specifies choice of fallback strategy if matching cubin is not found.

  Choice is based on supplied CUjit_fallback. This option cannot be used with cuLink* APIs as the linker requires exact matches.

  Option type: unsigned int for enumerated type CUjit_fallback. Applies to: compiler only

• JIT_GENERATE_DEBUG_INFO - Specifies whether to create debug information in output (-g) (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_LOG_VERBOSE - Generate verbose log messages (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_GENERATE_LINE_INFO - Generate line number information (-lineinfo) (0: false, default).

  Option type: int. Applies to: compiler only

• JIT_CACHE_MODE - Specifies whether to enable caching explicitly (-dlcm). Choice is based on supplied CUjit_cacheMode_enum.

  Option type: unsigned int for enumerated type CUjit_cacheMode_enum. Applies to: compiler only

• JIT_NEW_SM3X_OPT - Used for internal purposes only, in this version of CUDA.
• JIT_FAST_COMPILE - Used for internal purposes only, in this version of CUDA.
• JIT_GLOBAL_SYMBOL_NAMES - Array of device symbol names that will be relocated to the corresponing host addresses stored in JIT_GLOBAL_SYMBOL_ADDRESSES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries. When loding a device module, driver will relocate all encountered unresolved symbols to the host addresses. It is only allowed to register symbols that correspond to unresolved global variables. It is illegal to register the same device symbol at multiple addresses.

  Option type: const char **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_ADDRESSES - Array of host addresses that will be used to relocate corresponding device symbols stored in JIT_GLOBAL_SYMBOL_NAMES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries.

  Option type: void **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_COUNT - Number of entries in JIT_GLOBAL_SYMBOL_NAMES and JIT_GLOBAL_SYMBOL_ADDRESSES arrays.

  Option type: unsigned int. Applies to: dynamic linker only

See Also:

Constant Field Values

CU_JIT_GENERATE_DEBUG_INFO

public static final int CU_JIT_GENERATE_DEBUG_INFO

Online compiler and linker options. (CUjit_option)

Enum values:

• JIT_MAX_REGISTERS - Max number of registers that a thread may use.

  Option type: unsigned int. Applies to: compiler only

• JIT_THREADS_PER_BLOCK - IN: Specifies minimum number of threads per block to target compilation for

  OUT: Returns the number of threads the compiler actually targeted.

  This restricts the resource utilization fo the compiler (e.g. max registers) such that a block with the given number of threads should be able to launch based on register limitations. Note, this option does not currently take into account any other resource limitations, such as shared memory utilization.

  Cannot be combined with JIT_TARGET. Option type: unsigned int. Applies to: compiler only

• JIT_WALL_TIME - Overwrites the option value with the total wall clock time, in milliseconds, spent in the compiler and linker.

  Option type: float. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER - Pointer to a buffer in which to print any log messages that are informational in nature (the buffer size is specified via option JIT_INFO_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER - Pointer to a buffer in which to print any log messages that reflect errors (the buffer size is specified via option JIT_ERROR_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_OPTIMIZATION_LEVEL - Level of optimizations to apply to generated code (0 - 4), with 4 being the default and highest level of optimizations.

  Option type: unsigned int. Applies to: compiler only

• JIT_TARGET_FROM_CUCONTEXT - No option value required. Determines the target based on the current attached context (default).

  Option type: No option value needed. Applies to: compiler and linker

• JIT_TARGET - Target is chosen based on supplied CUjit_target. Cannot be combined with JIT_THREADS_PER_BLOCK.

  Option type: unsigned int for enumerated type CUjit_target. Applies to: compiler and linker

• JIT_FALLBACK_STRATEGY - Specifies choice of fallback strategy if matching cubin is not found.

  Choice is based on supplied CUjit_fallback. This option cannot be used with cuLink* APIs as the linker requires exact matches.

  Option type: unsigned int for enumerated type CUjit_fallback. Applies to: compiler only

• JIT_GENERATE_DEBUG_INFO - Specifies whether to create debug information in output (-g) (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_LOG_VERBOSE - Generate verbose log messages (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_GENERATE_LINE_INFO - Generate line number information (-lineinfo) (0: false, default).

  Option type: int. Applies to: compiler only

• JIT_CACHE_MODE - Specifies whether to enable caching explicitly (-dlcm). Choice is based on supplied CUjit_cacheMode_enum.

  Option type: unsigned int for enumerated type CUjit_cacheMode_enum. Applies to: compiler only

• JIT_NEW_SM3X_OPT - Used for internal purposes only, in this version of CUDA.
• JIT_FAST_COMPILE - Used for internal purposes only, in this version of CUDA.
• JIT_GLOBAL_SYMBOL_NAMES - Array of device symbol names that will be relocated to the corresponing host addresses stored in JIT_GLOBAL_SYMBOL_ADDRESSES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries. When loding a device module, driver will relocate all encountered unresolved symbols to the host addresses. It is only allowed to register symbols that correspond to unresolved global variables. It is illegal to register the same device symbol at multiple addresses.

  Option type: const char **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_ADDRESSES - Array of host addresses that will be used to relocate corresponding device symbols stored in JIT_GLOBAL_SYMBOL_NAMES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries.

  Option type: void **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_COUNT - Number of entries in JIT_GLOBAL_SYMBOL_NAMES and JIT_GLOBAL_SYMBOL_ADDRESSES arrays.

  Option type: unsigned int. Applies to: dynamic linker only

See Also:

Constant Field Values

CU_JIT_LOG_VERBOSE

public static final int CU_JIT_LOG_VERBOSE

Online compiler and linker options. (CUjit_option)

Enum values:

• JIT_MAX_REGISTERS - Max number of registers that a thread may use.

  Option type: unsigned int. Applies to: compiler only

• JIT_THREADS_PER_BLOCK - IN: Specifies minimum number of threads per block to target compilation for

  OUT: Returns the number of threads the compiler actually targeted.

  This restricts the resource utilization fo the compiler (e.g. max registers) such that a block with the given number of threads should be able to launch based on register limitations. Note, this option does not currently take into account any other resource limitations, such as shared memory utilization.

  Cannot be combined with JIT_TARGET. Option type: unsigned int. Applies to: compiler only

• JIT_WALL_TIME - Overwrites the option value with the total wall clock time, in milliseconds, spent in the compiler and linker.

  Option type: float. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER - Pointer to a buffer in which to print any log messages that are informational in nature (the buffer size is specified via option JIT_INFO_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER - Pointer to a buffer in which to print any log messages that reflect errors (the buffer size is specified via option JIT_ERROR_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_OPTIMIZATION_LEVEL - Level of optimizations to apply to generated code (0 - 4), with 4 being the default and highest level of optimizations.

  Option type: unsigned int. Applies to: compiler only

• JIT_TARGET_FROM_CUCONTEXT - No option value required. Determines the target based on the current attached context (default).

  Option type: No option value needed. Applies to: compiler and linker

• JIT_TARGET - Target is chosen based on supplied CUjit_target. Cannot be combined with JIT_THREADS_PER_BLOCK.

  Option type: unsigned int for enumerated type CUjit_target. Applies to: compiler and linker

• JIT_FALLBACK_STRATEGY - Specifies choice of fallback strategy if matching cubin is not found.

  Choice is based on supplied CUjit_fallback. This option cannot be used with cuLink* APIs as the linker requires exact matches.

  Option type: unsigned int for enumerated type CUjit_fallback. Applies to: compiler only

• JIT_GENERATE_DEBUG_INFO - Specifies whether to create debug information in output (-g) (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_LOG_VERBOSE - Generate verbose log messages (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_GENERATE_LINE_INFO - Generate line number information (-lineinfo) (0: false, default).

  Option type: int. Applies to: compiler only

• JIT_CACHE_MODE - Specifies whether to enable caching explicitly (-dlcm). Choice is based on supplied CUjit_cacheMode_enum.

  Option type: unsigned int for enumerated type CUjit_cacheMode_enum. Applies to: compiler only

• JIT_NEW_SM3X_OPT - Used for internal purposes only, in this version of CUDA.
• JIT_FAST_COMPILE - Used for internal purposes only, in this version of CUDA.
• JIT_GLOBAL_SYMBOL_NAMES - Array of device symbol names that will be relocated to the corresponing host addresses stored in JIT_GLOBAL_SYMBOL_ADDRESSES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries. When loding a device module, driver will relocate all encountered unresolved symbols to the host addresses. It is only allowed to register symbols that correspond to unresolved global variables. It is illegal to register the same device symbol at multiple addresses.

  Option type: const char **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_ADDRESSES - Array of host addresses that will be used to relocate corresponding device symbols stored in JIT_GLOBAL_SYMBOL_NAMES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries.

  Option type: void **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_COUNT - Number of entries in JIT_GLOBAL_SYMBOL_NAMES and JIT_GLOBAL_SYMBOL_ADDRESSES arrays.

  Option type: unsigned int. Applies to: dynamic linker only

See Also:

Constant Field Values

CU_JIT_GENERATE_LINE_INFO

public static final int CU_JIT_GENERATE_LINE_INFO

Online compiler and linker options. (CUjit_option)

Enum values:

• JIT_MAX_REGISTERS - Max number of registers that a thread may use.

  Option type: unsigned int. Applies to: compiler only

• JIT_THREADS_PER_BLOCK - IN: Specifies minimum number of threads per block to target compilation for

  OUT: Returns the number of threads the compiler actually targeted.

  This restricts the resource utilization fo the compiler (e.g. max registers) such that a block with the given number of threads should be able to launch based on register limitations. Note, this option does not currently take into account any other resource limitations, such as shared memory utilization.

  Cannot be combined with JIT_TARGET. Option type: unsigned int. Applies to: compiler only

• JIT_WALL_TIME - Overwrites the option value with the total wall clock time, in milliseconds, spent in the compiler and linker.

  Option type: float. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER - Pointer to a buffer in which to print any log messages that are informational in nature (the buffer size is specified via option JIT_INFO_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER - Pointer to a buffer in which to print any log messages that reflect errors (the buffer size is specified via option JIT_ERROR_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_OPTIMIZATION_LEVEL - Level of optimizations to apply to generated code (0 - 4), with 4 being the default and highest level of optimizations.

  Option type: unsigned int. Applies to: compiler only

• JIT_TARGET_FROM_CUCONTEXT - No option value required. Determines the target based on the current attached context (default).

  Option type: No option value needed. Applies to: compiler and linker

• JIT_TARGET - Target is chosen based on supplied CUjit_target. Cannot be combined with JIT_THREADS_PER_BLOCK.

  Option type: unsigned int for enumerated type CUjit_target. Applies to: compiler and linker

• JIT_FALLBACK_STRATEGY - Specifies choice of fallback strategy if matching cubin is not found.

  Choice is based on supplied CUjit_fallback. This option cannot be used with cuLink* APIs as the linker requires exact matches.

  Option type: unsigned int for enumerated type CUjit_fallback. Applies to: compiler only

• JIT_GENERATE_DEBUG_INFO - Specifies whether to create debug information in output (-g) (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_LOG_VERBOSE - Generate verbose log messages (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_GENERATE_LINE_INFO - Generate line number information (-lineinfo) (0: false, default).

  Option type: int. Applies to: compiler only

• JIT_CACHE_MODE - Specifies whether to enable caching explicitly (-dlcm). Choice is based on supplied CUjit_cacheMode_enum.

  Option type: unsigned int for enumerated type CUjit_cacheMode_enum. Applies to: compiler only

• JIT_NEW_SM3X_OPT - Used for internal purposes only, in this version of CUDA.
• JIT_FAST_COMPILE - Used for internal purposes only, in this version of CUDA.
• JIT_GLOBAL_SYMBOL_NAMES - Array of device symbol names that will be relocated to the corresponing host addresses stored in JIT_GLOBAL_SYMBOL_ADDRESSES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries. When loding a device module, driver will relocate all encountered unresolved symbols to the host addresses. It is only allowed to register symbols that correspond to unresolved global variables. It is illegal to register the same device symbol at multiple addresses.

  Option type: const char **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_ADDRESSES - Array of host addresses that will be used to relocate corresponding device symbols stored in JIT_GLOBAL_SYMBOL_NAMES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries.

  Option type: void **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_COUNT - Number of entries in JIT_GLOBAL_SYMBOL_NAMES and JIT_GLOBAL_SYMBOL_ADDRESSES arrays.

  Option type: unsigned int. Applies to: dynamic linker only

See Also:

Constant Field Values

CU_JIT_CACHE_MODE

public static final int CU_JIT_CACHE_MODE

Online compiler and linker options. (CUjit_option)

Enum values:

• JIT_MAX_REGISTERS - Max number of registers that a thread may use.

  Option type: unsigned int. Applies to: compiler only

• JIT_THREADS_PER_BLOCK - IN: Specifies minimum number of threads per block to target compilation for

  OUT: Returns the number of threads the compiler actually targeted.

  This restricts the resource utilization fo the compiler (e.g. max registers) such that a block with the given number of threads should be able to launch based on register limitations. Note, this option does not currently take into account any other resource limitations, such as shared memory utilization.

  Cannot be combined with JIT_TARGET. Option type: unsigned int. Applies to: compiler only

• JIT_WALL_TIME - Overwrites the option value with the total wall clock time, in milliseconds, spent in the compiler and linker.

  Option type: float. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER - Pointer to a buffer in which to print any log messages that are informational in nature (the buffer size is specified via option JIT_INFO_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER - Pointer to a buffer in which to print any log messages that reflect errors (the buffer size is specified via option JIT_ERROR_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_OPTIMIZATION_LEVEL - Level of optimizations to apply to generated code (0 - 4), with 4 being the default and highest level of optimizations.

  Option type: unsigned int. Applies to: compiler only

• JIT_TARGET_FROM_CUCONTEXT - No option value required. Determines the target based on the current attached context (default).

  Option type: No option value needed. Applies to: compiler and linker

• JIT_TARGET - Target is chosen based on supplied CUjit_target. Cannot be combined with JIT_THREADS_PER_BLOCK.

  Option type: unsigned int for enumerated type CUjit_target. Applies to: compiler and linker

• JIT_FALLBACK_STRATEGY - Specifies choice of fallback strategy if matching cubin is not found.

  Choice is based on supplied CUjit_fallback. This option cannot be used with cuLink* APIs as the linker requires exact matches.

  Option type: unsigned int for enumerated type CUjit_fallback. Applies to: compiler only

• JIT_GENERATE_DEBUG_INFO - Specifies whether to create debug information in output (-g) (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_LOG_VERBOSE - Generate verbose log messages (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_GENERATE_LINE_INFO - Generate line number information (-lineinfo) (0: false, default).

  Option type: int. Applies to: compiler only

• JIT_CACHE_MODE - Specifies whether to enable caching explicitly (-dlcm). Choice is based on supplied CUjit_cacheMode_enum.

  Option type: unsigned int for enumerated type CUjit_cacheMode_enum. Applies to: compiler only

• JIT_NEW_SM3X_OPT - Used for internal purposes only, in this version of CUDA.
• JIT_FAST_COMPILE - Used for internal purposes only, in this version of CUDA.
• JIT_GLOBAL_SYMBOL_NAMES - Array of device symbol names that will be relocated to the corresponing host addresses stored in JIT_GLOBAL_SYMBOL_ADDRESSES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries. When loding a device module, driver will relocate all encountered unresolved symbols to the host addresses. It is only allowed to register symbols that correspond to unresolved global variables. It is illegal to register the same device symbol at multiple addresses.

  Option type: const char **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_ADDRESSES - Array of host addresses that will be used to relocate corresponding device symbols stored in JIT_GLOBAL_SYMBOL_NAMES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries.

  Option type: void **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_COUNT - Number of entries in JIT_GLOBAL_SYMBOL_NAMES and JIT_GLOBAL_SYMBOL_ADDRESSES arrays.

  Option type: unsigned int. Applies to: dynamic linker only

See Also:

Constant Field Values

CU_JIT_NEW_SM3X_OPT

public static final int CU_JIT_NEW_SM3X_OPT

Online compiler and linker options. (CUjit_option)

Enum values:

• JIT_MAX_REGISTERS - Max number of registers that a thread may use.

  Option type: unsigned int. Applies to: compiler only

• JIT_THREADS_PER_BLOCK - IN: Specifies minimum number of threads per block to target compilation for

  OUT: Returns the number of threads the compiler actually targeted.

  This restricts the resource utilization fo the compiler (e.g. max registers) such that a block with the given number of threads should be able to launch based on register limitations. Note, this option does not currently take into account any other resource limitations, such as shared memory utilization.

  Cannot be combined with JIT_TARGET. Option type: unsigned int. Applies to: compiler only

• JIT_WALL_TIME - Overwrites the option value with the total wall clock time, in milliseconds, spent in the compiler and linker.

  Option type: float. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER - Pointer to a buffer in which to print any log messages that are informational in nature (the buffer size is specified via option JIT_INFO_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER - Pointer to a buffer in which to print any log messages that reflect errors (the buffer size is specified via option JIT_ERROR_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_OPTIMIZATION_LEVEL - Level of optimizations to apply to generated code (0 - 4), with 4 being the default and highest level of optimizations.

  Option type: unsigned int. Applies to: compiler only

• JIT_TARGET_FROM_CUCONTEXT - No option value required. Determines the target based on the current attached context (default).

  Option type: No option value needed. Applies to: compiler and linker

• JIT_TARGET - Target is chosen based on supplied CUjit_target. Cannot be combined with JIT_THREADS_PER_BLOCK.

  Option type: unsigned int for enumerated type CUjit_target. Applies to: compiler and linker

• JIT_FALLBACK_STRATEGY - Specifies choice of fallback strategy if matching cubin is not found.

  Choice is based on supplied CUjit_fallback. This option cannot be used with cuLink* APIs as the linker requires exact matches.

  Option type: unsigned int for enumerated type CUjit_fallback. Applies to: compiler only

• JIT_GENERATE_DEBUG_INFO - Specifies whether to create debug information in output (-g) (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_LOG_VERBOSE - Generate verbose log messages (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_GENERATE_LINE_INFO - Generate line number information (-lineinfo) (0: false, default).

  Option type: int. Applies to: compiler only

• JIT_CACHE_MODE - Specifies whether to enable caching explicitly (-dlcm). Choice is based on supplied CUjit_cacheMode_enum.

  Option type: unsigned int for enumerated type CUjit_cacheMode_enum. Applies to: compiler only

• JIT_NEW_SM3X_OPT - Used for internal purposes only, in this version of CUDA.
• JIT_FAST_COMPILE - Used for internal purposes only, in this version of CUDA.
• JIT_GLOBAL_SYMBOL_NAMES - Array of device symbol names that will be relocated to the corresponing host addresses stored in JIT_GLOBAL_SYMBOL_ADDRESSES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries. When loding a device module, driver will relocate all encountered unresolved symbols to the host addresses. It is only allowed to register symbols that correspond to unresolved global variables. It is illegal to register the same device symbol at multiple addresses.

  Option type: const char **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_ADDRESSES - Array of host addresses that will be used to relocate corresponding device symbols stored in JIT_GLOBAL_SYMBOL_NAMES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries.

  Option type: void **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_COUNT - Number of entries in JIT_GLOBAL_SYMBOL_NAMES and JIT_GLOBAL_SYMBOL_ADDRESSES arrays.

  Option type: unsigned int. Applies to: dynamic linker only

See Also:

Constant Field Values

CU_JIT_FAST_COMPILE

public static final int CU_JIT_FAST_COMPILE

Online compiler and linker options. (CUjit_option)

Enum values:

• JIT_MAX_REGISTERS - Max number of registers that a thread may use.

  Option type: unsigned int. Applies to: compiler only

• JIT_THREADS_PER_BLOCK - IN: Specifies minimum number of threads per block to target compilation for

  OUT: Returns the number of threads the compiler actually targeted.

  This restricts the resource utilization fo the compiler (e.g. max registers) such that a block with the given number of threads should be able to launch based on register limitations. Note, this option does not currently take into account any other resource limitations, such as shared memory utilization.

  Cannot be combined with JIT_TARGET. Option type: unsigned int. Applies to: compiler only

• JIT_WALL_TIME - Overwrites the option value with the total wall clock time, in milliseconds, spent in the compiler and linker.

  Option type: float. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER - Pointer to a buffer in which to print any log messages that are informational in nature (the buffer size is specified via option JIT_INFO_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER - Pointer to a buffer in which to print any log messages that reflect errors (the buffer size is specified via option JIT_ERROR_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_OPTIMIZATION_LEVEL - Level of optimizations to apply to generated code (0 - 4), with 4 being the default and highest level of optimizations.

  Option type: unsigned int. Applies to: compiler only

• JIT_TARGET_FROM_CUCONTEXT - No option value required. Determines the target based on the current attached context (default).

  Option type: No option value needed. Applies to: compiler and linker

• JIT_TARGET - Target is chosen based on supplied CUjit_target. Cannot be combined with JIT_THREADS_PER_BLOCK.

  Option type: unsigned int for enumerated type CUjit_target. Applies to: compiler and linker

• JIT_FALLBACK_STRATEGY - Specifies choice of fallback strategy if matching cubin is not found.

  Choice is based on supplied CUjit_fallback. This option cannot be used with cuLink* APIs as the linker requires exact matches.

  Option type: unsigned int for enumerated type CUjit_fallback. Applies to: compiler only

• JIT_GENERATE_DEBUG_INFO - Specifies whether to create debug information in output (-g) (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_LOG_VERBOSE - Generate verbose log messages (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_GENERATE_LINE_INFO - Generate line number information (-lineinfo) (0: false, default).

  Option type: int. Applies to: compiler only

• JIT_CACHE_MODE - Specifies whether to enable caching explicitly (-dlcm). Choice is based on supplied CUjit_cacheMode_enum.

  Option type: unsigned int for enumerated type CUjit_cacheMode_enum. Applies to: compiler only

• JIT_NEW_SM3X_OPT - Used for internal purposes only, in this version of CUDA.
• JIT_FAST_COMPILE - Used for internal purposes only, in this version of CUDA.
• JIT_GLOBAL_SYMBOL_NAMES - Array of device symbol names that will be relocated to the corresponing host addresses stored in JIT_GLOBAL_SYMBOL_ADDRESSES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries. When loding a device module, driver will relocate all encountered unresolved symbols to the host addresses. It is only allowed to register symbols that correspond to unresolved global variables. It is illegal to register the same device symbol at multiple addresses.

  Option type: const char **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_ADDRESSES - Array of host addresses that will be used to relocate corresponding device symbols stored in JIT_GLOBAL_SYMBOL_NAMES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries.

  Option type: void **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_COUNT - Number of entries in JIT_GLOBAL_SYMBOL_NAMES and JIT_GLOBAL_SYMBOL_ADDRESSES arrays.

  Option type: unsigned int. Applies to: dynamic linker only

See Also:

Constant Field Values

CU_JIT_GLOBAL_SYMBOL_NAMES

public static final int CU_JIT_GLOBAL_SYMBOL_NAMES

Online compiler and linker options. (CUjit_option)

Enum values:

• JIT_MAX_REGISTERS - Max number of registers that a thread may use.

  Option type: unsigned int. Applies to: compiler only

• JIT_THREADS_PER_BLOCK - IN: Specifies minimum number of threads per block to target compilation for

  OUT: Returns the number of threads the compiler actually targeted.

  This restricts the resource utilization fo the compiler (e.g. max registers) such that a block with the given number of threads should be able to launch based on register limitations. Note, this option does not currently take into account any other resource limitations, such as shared memory utilization.

  Cannot be combined with JIT_TARGET. Option type: unsigned int. Applies to: compiler only

• JIT_WALL_TIME - Overwrites the option value with the total wall clock time, in milliseconds, spent in the compiler and linker.

  Option type: float. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER - Pointer to a buffer in which to print any log messages that are informational in nature (the buffer size is specified via option JIT_INFO_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER - Pointer to a buffer in which to print any log messages that reflect errors (the buffer size is specified via option JIT_ERROR_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_OPTIMIZATION_LEVEL - Level of optimizations to apply to generated code (0 - 4), with 4 being the default and highest level of optimizations.

  Option type: unsigned int. Applies to: compiler only

• JIT_TARGET_FROM_CUCONTEXT - No option value required. Determines the target based on the current attached context (default).

  Option type: No option value needed. Applies to: compiler and linker

• JIT_TARGET - Target is chosen based on supplied CUjit_target. Cannot be combined with JIT_THREADS_PER_BLOCK.

  Option type: unsigned int for enumerated type CUjit_target. Applies to: compiler and linker

• JIT_FALLBACK_STRATEGY - Specifies choice of fallback strategy if matching cubin is not found.

  Choice is based on supplied CUjit_fallback. This option cannot be used with cuLink* APIs as the linker requires exact matches.

  Option type: unsigned int for enumerated type CUjit_fallback. Applies to: compiler only

• JIT_GENERATE_DEBUG_INFO - Specifies whether to create debug information in output (-g) (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_LOG_VERBOSE - Generate verbose log messages (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_GENERATE_LINE_INFO - Generate line number information (-lineinfo) (0: false, default).

  Option type: int. Applies to: compiler only

• JIT_CACHE_MODE - Specifies whether to enable caching explicitly (-dlcm). Choice is based on supplied CUjit_cacheMode_enum.

  Option type: unsigned int for enumerated type CUjit_cacheMode_enum. Applies to: compiler only

• JIT_NEW_SM3X_OPT - Used for internal purposes only, in this version of CUDA.
• JIT_FAST_COMPILE - Used for internal purposes only, in this version of CUDA.
• JIT_GLOBAL_SYMBOL_NAMES - Array of device symbol names that will be relocated to the corresponing host addresses stored in JIT_GLOBAL_SYMBOL_ADDRESSES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries. When loding a device module, driver will relocate all encountered unresolved symbols to the host addresses. It is only allowed to register symbols that correspond to unresolved global variables. It is illegal to register the same device symbol at multiple addresses.

  Option type: const char **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_ADDRESSES - Array of host addresses that will be used to relocate corresponding device symbols stored in JIT_GLOBAL_SYMBOL_NAMES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries.

  Option type: void **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_COUNT - Number of entries in JIT_GLOBAL_SYMBOL_NAMES and JIT_GLOBAL_SYMBOL_ADDRESSES arrays.

  Option type: unsigned int. Applies to: dynamic linker only

See Also:

Constant Field Values

CU_JIT_GLOBAL_SYMBOL_ADDRESSES

public static final int CU_JIT_GLOBAL_SYMBOL_ADDRESSES

Online compiler and linker options. (CUjit_option)

Enum values:

• JIT_MAX_REGISTERS - Max number of registers that a thread may use.

  Option type: unsigned int. Applies to: compiler only

• JIT_THREADS_PER_BLOCK - IN: Specifies minimum number of threads per block to target compilation for

  OUT: Returns the number of threads the compiler actually targeted.

  This restricts the resource utilization fo the compiler (e.g. max registers) such that a block with the given number of threads should be able to launch based on register limitations. Note, this option does not currently take into account any other resource limitations, such as shared memory utilization.

  Cannot be combined with JIT_TARGET. Option type: unsigned int. Applies to: compiler only

• JIT_WALL_TIME - Overwrites the option value with the total wall clock time, in milliseconds, spent in the compiler and linker.

  Option type: float. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER - Pointer to a buffer in which to print any log messages that are informational in nature (the buffer size is specified via option JIT_INFO_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER - Pointer to a buffer in which to print any log messages that reflect errors (the buffer size is specified via option JIT_ERROR_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_OPTIMIZATION_LEVEL - Level of optimizations to apply to generated code (0 - 4), with 4 being the default and highest level of optimizations.

  Option type: unsigned int. Applies to: compiler only

• JIT_TARGET_FROM_CUCONTEXT - No option value required. Determines the target based on the current attached context (default).

  Option type: No option value needed. Applies to: compiler and linker

• JIT_TARGET - Target is chosen based on supplied CUjit_target. Cannot be combined with JIT_THREADS_PER_BLOCK.

  Option type: unsigned int for enumerated type CUjit_target. Applies to: compiler and linker

• JIT_FALLBACK_STRATEGY - Specifies choice of fallback strategy if matching cubin is not found.

  Choice is based on supplied CUjit_fallback. This option cannot be used with cuLink* APIs as the linker requires exact matches.

  Option type: unsigned int for enumerated type CUjit_fallback. Applies to: compiler only

• JIT_GENERATE_DEBUG_INFO - Specifies whether to create debug information in output (-g) (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_LOG_VERBOSE - Generate verbose log messages (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_GENERATE_LINE_INFO - Generate line number information (-lineinfo) (0: false, default).

  Option type: int. Applies to: compiler only

• JIT_CACHE_MODE - Specifies whether to enable caching explicitly (-dlcm). Choice is based on supplied CUjit_cacheMode_enum.

  Option type: unsigned int for enumerated type CUjit_cacheMode_enum. Applies to: compiler only

• JIT_NEW_SM3X_OPT - Used for internal purposes only, in this version of CUDA.
• JIT_FAST_COMPILE - Used for internal purposes only, in this version of CUDA.
• JIT_GLOBAL_SYMBOL_NAMES - Array of device symbol names that will be relocated to the corresponing host addresses stored in JIT_GLOBAL_SYMBOL_ADDRESSES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries. When loding a device module, driver will relocate all encountered unresolved symbols to the host addresses. It is only allowed to register symbols that correspond to unresolved global variables. It is illegal to register the same device symbol at multiple addresses.

  Option type: const char **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_ADDRESSES - Array of host addresses that will be used to relocate corresponding device symbols stored in JIT_GLOBAL_SYMBOL_NAMES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries.

  Option type: void **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_COUNT - Number of entries in JIT_GLOBAL_SYMBOL_NAMES and JIT_GLOBAL_SYMBOL_ADDRESSES arrays.

  Option type: unsigned int. Applies to: dynamic linker only

See Also:

Constant Field Values

CU_JIT_GLOBAL_SYMBOL_COUNT

public static final int CU_JIT_GLOBAL_SYMBOL_COUNT

Online compiler and linker options. (CUjit_option)

Enum values:

• JIT_MAX_REGISTERS - Max number of registers that a thread may use.

  Option type: unsigned int. Applies to: compiler only

• JIT_THREADS_PER_BLOCK - IN: Specifies minimum number of threads per block to target compilation for

  OUT: Returns the number of threads the compiler actually targeted.

  This restricts the resource utilization fo the compiler (e.g. max registers) such that a block with the given number of threads should be able to launch based on register limitations. Note, this option does not currently take into account any other resource limitations, such as shared memory utilization.

  Cannot be combined with JIT_TARGET. Option type: unsigned int. Applies to: compiler only

• JIT_WALL_TIME - Overwrites the option value with the total wall clock time, in milliseconds, spent in the compiler and linker.

  Option type: float. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER - Pointer to a buffer in which to print any log messages that are informational in nature (the buffer size is specified via option JIT_INFO_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_INFO_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER - Pointer to a buffer in which to print any log messages that reflect errors (the buffer size is specified via option JIT_ERROR_LOG_BUFFER_SIZE_BYTES).

  Option type: char *. Applies to: compiler and linker

• JIT_ERROR_LOG_BUFFER_SIZE_BYTES - IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator).

  OUT: Amount of log buffer filled with messages.

  Option type: unsigned int. Applies to: compiler and linker

• JIT_OPTIMIZATION_LEVEL - Level of optimizations to apply to generated code (0 - 4), with 4 being the default and highest level of optimizations.

  Option type: unsigned int. Applies to: compiler only

• JIT_TARGET_FROM_CUCONTEXT - No option value required. Determines the target based on the current attached context (default).

  Option type: No option value needed. Applies to: compiler and linker

• JIT_TARGET - Target is chosen based on supplied CUjit_target. Cannot be combined with JIT_THREADS_PER_BLOCK.

  Option type: unsigned int for enumerated type CUjit_target. Applies to: compiler and linker

• JIT_FALLBACK_STRATEGY - Specifies choice of fallback strategy if matching cubin is not found.

  Choice is based on supplied CUjit_fallback. This option cannot be used with cuLink* APIs as the linker requires exact matches.

  Option type: unsigned int for enumerated type CUjit_fallback. Applies to: compiler only

• JIT_GENERATE_DEBUG_INFO - Specifies whether to create debug information in output (-g) (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_LOG_VERBOSE - Generate verbose log messages (0: false, default).

  Option type: int. Applies to: compiler and linker

• JIT_GENERATE_LINE_INFO - Generate line number information (-lineinfo) (0: false, default).

  Option type: int. Applies to: compiler only

• JIT_CACHE_MODE - Specifies whether to enable caching explicitly (-dlcm). Choice is based on supplied CUjit_cacheMode_enum.

  Option type: unsigned int for enumerated type CUjit_cacheMode_enum. Applies to: compiler only

• JIT_NEW_SM3X_OPT - Used for internal purposes only, in this version of CUDA.
• JIT_FAST_COMPILE - Used for internal purposes only, in this version of CUDA.
• JIT_GLOBAL_SYMBOL_NAMES - Array of device symbol names that will be relocated to the corresponing host addresses stored in JIT_GLOBAL_SYMBOL_ADDRESSES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries. When loding a device module, driver will relocate all encountered unresolved symbols to the host addresses. It is only allowed to register symbols that correspond to unresolved global variables. It is illegal to register the same device symbol at multiple addresses.

  Option type: const char **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_ADDRESSES - Array of host addresses that will be used to relocate corresponding device symbols stored in JIT_GLOBAL_SYMBOL_NAMES.

  Must contain JIT_GLOBAL_SYMBOL_COUNT entries.

  Option type: void **. Applies to: dynamic linker only

• JIT_GLOBAL_SYMBOL_COUNT - Number of entries in JIT_GLOBAL_SYMBOL_NAMES and JIT_GLOBAL_SYMBOL_ADDRESSES arrays.

  Option type: unsigned int. Applies to: dynamic linker only

See Also:

Constant Field Values

CU_TARGET_COMPUTE_20

public static final int CU_TARGET_COMPUTE_20

Online compilation targets. (CUjit_target)

Enum values:

• TARGET_COMPUTE_20 - Compute device class 2.0
• TARGET_COMPUTE_21 - Compute device class 2.1
• TARGET_COMPUTE_30 - Compute device class 3.0
• TARGET_COMPUTE_32 - Compute device class 3.2
• TARGET_COMPUTE_35 - Compute device class 3.5
• TARGET_COMPUTE_37 - Compute device class 3.7
• TARGET_COMPUTE_50 - Compute device class 5.0
• TARGET_COMPUTE_52 - Compute device class 5.2
• TARGET_COMPUTE_53 - Compute device class 5.3
• TARGET_COMPUTE_60 - Compute device class 6.0.
• TARGET_COMPUTE_61 - Compute device class 6.1.
• TARGET_COMPUTE_62 - Compute device class 6.2.
• TARGET_COMPUTE_70 - Compute device class 7.0.
• TARGET_COMPUTE_72 - Compute device class 7.2.
• TARGET_COMPUTE_75 - Compute device class 7.5.

See Also:

Constant Field Values

CU_TARGET_COMPUTE_21

public static final int CU_TARGET_COMPUTE_21

Online compilation targets. (CUjit_target)

Enum values:

• TARGET_COMPUTE_20 - Compute device class 2.0
• TARGET_COMPUTE_21 - Compute device class 2.1
• TARGET_COMPUTE_30 - Compute device class 3.0
• TARGET_COMPUTE_32 - Compute device class 3.2
• TARGET_COMPUTE_35 - Compute device class 3.5
• TARGET_COMPUTE_37 - Compute device class 3.7
• TARGET_COMPUTE_50 - Compute device class 5.0
• TARGET_COMPUTE_52 - Compute device class 5.2
• TARGET_COMPUTE_53 - Compute device class 5.3
• TARGET_COMPUTE_60 - Compute device class 6.0.
• TARGET_COMPUTE_61 - Compute device class 6.1.
• TARGET_COMPUTE_62 - Compute device class 6.2.
• TARGET_COMPUTE_70 - Compute device class 7.0.
• TARGET_COMPUTE_72 - Compute device class 7.2.
• TARGET_COMPUTE_75 - Compute device class 7.5.

See Also:

Constant Field Values

CU_TARGET_COMPUTE_30

public static final int CU_TARGET_COMPUTE_30

Online compilation targets. (CUjit_target)

Enum values:

• TARGET_COMPUTE_20 - Compute device class 2.0
• TARGET_COMPUTE_21 - Compute device class 2.1
• TARGET_COMPUTE_30 - Compute device class 3.0
• TARGET_COMPUTE_32 - Compute device class 3.2
• TARGET_COMPUTE_35 - Compute device class 3.5
• TARGET_COMPUTE_37 - Compute device class 3.7
• TARGET_COMPUTE_50 - Compute device class 5.0
• TARGET_COMPUTE_52 - Compute device class 5.2
• TARGET_COMPUTE_53 - Compute device class 5.3
• TARGET_COMPUTE_60 - Compute device class 6.0.
• TARGET_COMPUTE_61 - Compute device class 6.1.
• TARGET_COMPUTE_62 - Compute device class 6.2.
• TARGET_COMPUTE_70 - Compute device class 7.0.
• TARGET_COMPUTE_72 - Compute device class 7.2.
• TARGET_COMPUTE_75 - Compute device class 7.5.

See Also:

Constant Field Values

CU_TARGET_COMPUTE_32

public static final int CU_TARGET_COMPUTE_32

Online compilation targets. (CUjit_target)

Enum values:

• TARGET_COMPUTE_20 - Compute device class 2.0
• TARGET_COMPUTE_21 - Compute device class 2.1
• TARGET_COMPUTE_30 - Compute device class 3.0
• TARGET_COMPUTE_32 - Compute device class 3.2
• TARGET_COMPUTE_35 - Compute device class 3.5
• TARGET_COMPUTE_37 - Compute device class 3.7
• TARGET_COMPUTE_50 - Compute device class 5.0
• TARGET_COMPUTE_52 - Compute device class 5.2
• TARGET_COMPUTE_53 - Compute device class 5.3
• TARGET_COMPUTE_60 - Compute device class 6.0.
• TARGET_COMPUTE_61 - Compute device class 6.1.
• TARGET_COMPUTE_62 - Compute device class 6.2.
• TARGET_COMPUTE_70 - Compute device class 7.0.
• TARGET_COMPUTE_72 - Compute device class 7.2.
• TARGET_COMPUTE_75 - Compute device class 7.5.

See Also:

Constant Field Values

CU_TARGET_COMPUTE_35

public static final int CU_TARGET_COMPUTE_35

Online compilation targets. (CUjit_target)

Enum values:

• TARGET_COMPUTE_20 - Compute device class 2.0
• TARGET_COMPUTE_21 - Compute device class 2.1
• TARGET_COMPUTE_30 - Compute device class 3.0
• TARGET_COMPUTE_32 - Compute device class 3.2
• TARGET_COMPUTE_35 - Compute device class 3.5
• TARGET_COMPUTE_37 - Compute device class 3.7
• TARGET_COMPUTE_50 - Compute device class 5.0
• TARGET_COMPUTE_52 - Compute device class 5.2
• TARGET_COMPUTE_53 - Compute device class 5.3
• TARGET_COMPUTE_60 - Compute device class 6.0.
• TARGET_COMPUTE_61 - Compute device class 6.1.
• TARGET_COMPUTE_62 - Compute device class 6.2.
• TARGET_COMPUTE_70 - Compute device class 7.0.
• TARGET_COMPUTE_72 - Compute device class 7.2.
• TARGET_COMPUTE_75 - Compute device class 7.5.

See Also:

Constant Field Values

CU_TARGET_COMPUTE_37

public static final int CU_TARGET_COMPUTE_37

Online compilation targets. (CUjit_target)

Enum values:

• TARGET_COMPUTE_20 - Compute device class 2.0
• TARGET_COMPUTE_21 - Compute device class 2.1
• TARGET_COMPUTE_30 - Compute device class 3.0
• TARGET_COMPUTE_32 - Compute device class 3.2
• TARGET_COMPUTE_35 - Compute device class 3.5
• TARGET_COMPUTE_37 - Compute device class 3.7
• TARGET_COMPUTE_50 - Compute device class 5.0
• TARGET_COMPUTE_52 - Compute device class 5.2
• TARGET_COMPUTE_53 - Compute device class 5.3
• TARGET_COMPUTE_60 - Compute device class 6.0.
• TARGET_COMPUTE_61 - Compute device class 6.1.
• TARGET_COMPUTE_62 - Compute device class 6.2.
• TARGET_COMPUTE_70 - Compute device class 7.0.
• TARGET_COMPUTE_72 - Compute device class 7.2.
• TARGET_COMPUTE_75 - Compute device class 7.5.

See Also:

Constant Field Values

CU_TARGET_COMPUTE_50

public static final int CU_TARGET_COMPUTE_50

Online compilation targets. (CUjit_target)

Enum values:

• TARGET_COMPUTE_20 - Compute device class 2.0
• TARGET_COMPUTE_21 - Compute device class 2.1
• TARGET_COMPUTE_30 - Compute device class 3.0
• TARGET_COMPUTE_32 - Compute device class 3.2
• TARGET_COMPUTE_35 - Compute device class 3.5
• TARGET_COMPUTE_37 - Compute device class 3.7
• TARGET_COMPUTE_50 - Compute device class 5.0
• TARGET_COMPUTE_52 - Compute device class 5.2
• TARGET_COMPUTE_53 - Compute device class 5.3
• TARGET_COMPUTE_60 - Compute device class 6.0.
• TARGET_COMPUTE_61 - Compute device class 6.1.
• TARGET_COMPUTE_62 - Compute device class 6.2.
• TARGET_COMPUTE_70 - Compute device class 7.0.
• TARGET_COMPUTE_72 - Compute device class 7.2.
• TARGET_COMPUTE_75 - Compute device class 7.5.

See Also:

Constant Field Values

CU_TARGET_COMPUTE_52

public static final int CU_TARGET_COMPUTE_52

Online compilation targets. (CUjit_target)

Enum values:

• TARGET_COMPUTE_20 - Compute device class 2.0
• TARGET_COMPUTE_21 - Compute device class 2.1
• TARGET_COMPUTE_30 - Compute device class 3.0
• TARGET_COMPUTE_32 - Compute device class 3.2
• TARGET_COMPUTE_35 - Compute device class 3.5
• TARGET_COMPUTE_37 - Compute device class 3.7
• TARGET_COMPUTE_50 - Compute device class 5.0
• TARGET_COMPUTE_52 - Compute device class 5.2
• TARGET_COMPUTE_53 - Compute device class 5.3
• TARGET_COMPUTE_60 - Compute device class 6.0.
• TARGET_COMPUTE_61 - Compute device class 6.1.
• TARGET_COMPUTE_62 - Compute device class 6.2.
• TARGET_COMPUTE_70 - Compute device class 7.0.
• TARGET_COMPUTE_72 - Compute device class 7.2.
• TARGET_COMPUTE_75 - Compute device class 7.5.

See Also:

Constant Field Values

CU_TARGET_COMPUTE_53

public static final int CU_TARGET_COMPUTE_53

Online compilation targets. (CUjit_target)

Enum values:

• TARGET_COMPUTE_20 - Compute device class 2.0
• TARGET_COMPUTE_21 - Compute device class 2.1
• TARGET_COMPUTE_30 - Compute device class 3.0
• TARGET_COMPUTE_32 - Compute device class 3.2
• TARGET_COMPUTE_35 - Compute device class 3.5
• TARGET_COMPUTE_37 - Compute device class 3.7
• TARGET_COMPUTE_50 - Compute device class 5.0
• TARGET_COMPUTE_52 - Compute device class 5.2
• TARGET_COMPUTE_53 - Compute device class 5.3
• TARGET_COMPUTE_60 - Compute device class 6.0.
• TARGET_COMPUTE_61 - Compute device class 6.1.
• TARGET_COMPUTE_62 - Compute device class 6.2.
• TARGET_COMPUTE_70 - Compute device class 7.0.
• TARGET_COMPUTE_72 - Compute device class 7.2.
• TARGET_COMPUTE_75 - Compute device class 7.5.

See Also:

Constant Field Values

CU_TARGET_COMPUTE_60

public static final int CU_TARGET_COMPUTE_60

Online compilation targets. (CUjit_target)

Enum values:

• TARGET_COMPUTE_20 - Compute device class 2.0
• TARGET_COMPUTE_21 - Compute device class 2.1
• TARGET_COMPUTE_30 - Compute device class 3.0
• TARGET_COMPUTE_32 - Compute device class 3.2
• TARGET_COMPUTE_35 - Compute device class 3.5
• TARGET_COMPUTE_37 - Compute device class 3.7
• TARGET_COMPUTE_50 - Compute device class 5.0
• TARGET_COMPUTE_52 - Compute device class 5.2
• TARGET_COMPUTE_53 - Compute device class 5.3
• TARGET_COMPUTE_60 - Compute device class 6.0.
• TARGET_COMPUTE_61 - Compute device class 6.1.
• TARGET_COMPUTE_62 - Compute device class 6.2.
• TARGET_COMPUTE_70 - Compute device class 7.0.
• TARGET_COMPUTE_72 - Compute device class 7.2.
• TARGET_COMPUTE_75 - Compute device class 7.5.

See Also:

Constant Field Values

CU_TARGET_COMPUTE_61

public static final int CU_TARGET_COMPUTE_61

Online compilation targets. (CUjit_target)

Enum values:

• TARGET_COMPUTE_20 - Compute device class 2.0
• TARGET_COMPUTE_21 - Compute device class 2.1
• TARGET_COMPUTE_30 - Compute device class 3.0
• TARGET_COMPUTE_32 - Compute device class 3.2
• TARGET_COMPUTE_35 - Compute device class 3.5
• TARGET_COMPUTE_37 - Compute device class 3.7
• TARGET_COMPUTE_50 - Compute device class 5.0
• TARGET_COMPUTE_52 - Compute device class 5.2
• TARGET_COMPUTE_53 - Compute device class 5.3
• TARGET_COMPUTE_60 - Compute device class 6.0.
• TARGET_COMPUTE_61 - Compute device class 6.1.
• TARGET_COMPUTE_62 - Compute device class 6.2.
• TARGET_COMPUTE_70 - Compute device class 7.0.
• TARGET_COMPUTE_72 - Compute device class 7.2.
• TARGET_COMPUTE_75 - Compute device class 7.5.

See Also:

Constant Field Values

CU_TARGET_COMPUTE_62

public static final int CU_TARGET_COMPUTE_62

Online compilation targets. (CUjit_target)

Enum values:

• TARGET_COMPUTE_20 - Compute device class 2.0
• TARGET_COMPUTE_21 - Compute device class 2.1
• TARGET_COMPUTE_30 - Compute device class 3.0
• TARGET_COMPUTE_32 - Compute device class 3.2
• TARGET_COMPUTE_35 - Compute device class 3.5
• TARGET_COMPUTE_37 - Compute device class 3.7
• TARGET_COMPUTE_50 - Compute device class 5.0
• TARGET_COMPUTE_52 - Compute device class 5.2
• TARGET_COMPUTE_53 - Compute device class 5.3
• TARGET_COMPUTE_60 - Compute device class 6.0.
• TARGET_COMPUTE_61 - Compute device class 6.1.
• TARGET_COMPUTE_62 - Compute device class 6.2.
• TARGET_COMPUTE_70 - Compute device class 7.0.
• TARGET_COMPUTE_72 - Compute device class 7.2.
• TARGET_COMPUTE_75 - Compute device class 7.5.

See Also:

Constant Field Values

CU_TARGET_COMPUTE_70

public static final int CU_TARGET_COMPUTE_70

Online compilation targets. (CUjit_target)

Enum values:

• TARGET_COMPUTE_20 - Compute device class 2.0
• TARGET_COMPUTE_21 - Compute device class 2.1
• TARGET_COMPUTE_30 - Compute device class 3.0
• TARGET_COMPUTE_32 - Compute device class 3.2
• TARGET_COMPUTE_35 - Compute device class 3.5
• TARGET_COMPUTE_37 - Compute device class 3.7
• TARGET_COMPUTE_50 - Compute device class 5.0
• TARGET_COMPUTE_52 - Compute device class 5.2
• TARGET_COMPUTE_53 - Compute device class 5.3
• TARGET_COMPUTE_60 - Compute device class 6.0.
• TARGET_COMPUTE_61 - Compute device class 6.1.
• TARGET_COMPUTE_62 - Compute device class 6.2.
• TARGET_COMPUTE_70 - Compute device class 7.0.
• TARGET_COMPUTE_72 - Compute device class 7.2.
• TARGET_COMPUTE_75 - Compute device class 7.5.

See Also:

Constant Field Values

CU_TARGET_COMPUTE_72

public static final int CU_TARGET_COMPUTE_72

Online compilation targets. (CUjit_target)

Enum values:

• TARGET_COMPUTE_20 - Compute device class 2.0
• TARGET_COMPUTE_21 - Compute device class 2.1
• TARGET_COMPUTE_30 - Compute device class 3.0
• TARGET_COMPUTE_32 - Compute device class 3.2
• TARGET_COMPUTE_35 - Compute device class 3.5
• TARGET_COMPUTE_37 - Compute device class 3.7
• TARGET_COMPUTE_50 - Compute device class 5.0
• TARGET_COMPUTE_52 - Compute device class 5.2
• TARGET_COMPUTE_53 - Compute device class 5.3
• TARGET_COMPUTE_60 - Compute device class 6.0.
• TARGET_COMPUTE_61 - Compute device class 6.1.
• TARGET_COMPUTE_62 - Compute device class 6.2.
• TARGET_COMPUTE_70 - Compute device class 7.0.
• TARGET_COMPUTE_72 - Compute device class 7.2.
• TARGET_COMPUTE_75 - Compute device class 7.5.

See Also:

Constant Field Values

CU_TARGET_COMPUTE_75

public static final int CU_TARGET_COMPUTE_75

Online compilation targets. (CUjit_target)

Enum values:

• TARGET_COMPUTE_20 - Compute device class 2.0
• TARGET_COMPUTE_21 - Compute device class 2.1
• TARGET_COMPUTE_30 - Compute device class 3.0
• TARGET_COMPUTE_32 - Compute device class 3.2
• TARGET_COMPUTE_35 - Compute device class 3.5
• TARGET_COMPUTE_37 - Compute device class 3.7
• TARGET_COMPUTE_50 - Compute device class 5.0
• TARGET_COMPUTE_52 - Compute device class 5.2
• TARGET_COMPUTE_53 - Compute device class 5.3
• TARGET_COMPUTE_60 - Compute device class 6.0.
• TARGET_COMPUTE_61 - Compute device class 6.1.
• TARGET_COMPUTE_62 - Compute device class 6.2.
• TARGET_COMPUTE_70 - Compute device class 7.0.
• TARGET_COMPUTE_72 - Compute device class 7.2.
• TARGET_COMPUTE_75 - Compute device class 7.5.

See Also:

Constant Field Values

CU_PREFER_PTX

public static final int CU_PREFER_PTX

Cubin matching fallback strategies. (CUjit_fallback)

Enum values:

• PREFER_PTX - Prefer to compile ptx if exact binary match not found
• PREFER_BINARY - Prefer to fall back to compatible binary code if exact match not found

See Also:

Constant Field Values

CU_PREFER_BINARY

public static final int CU_PREFER_BINARY

Cubin matching fallback strategies. (CUjit_fallback)

Enum values:

• PREFER_PTX - Prefer to compile ptx if exact binary match not found
• PREFER_BINARY - Prefer to fall back to compatible binary code if exact match not found

See Also:

Constant Field Values

CU_JIT_CACHE_OPTION_NONE

public static final int CU_JIT_CACHE_OPTION_NONE

Caching modes for dlcm. (CUjit_cacheMode)

Enum values:

• JIT_CACHE_OPTION_NONE - Compile with no -dlcm flag specified
• JIT_CACHE_OPTION_CG - Compile with L1 cache disabled
• JIT_CACHE_OPTION_CA - Compile with L1 cache enabled

See Also:

Constant Field Values

CU_JIT_CACHE_OPTION_CG

public static final int CU_JIT_CACHE_OPTION_CG

Caching modes for dlcm. (CUjit_cacheMode)

Enum values:

• JIT_CACHE_OPTION_NONE - Compile with no -dlcm flag specified
• JIT_CACHE_OPTION_CG - Compile with L1 cache disabled
• JIT_CACHE_OPTION_CA - Compile with L1 cache enabled

See Also:

Constant Field Values

CU_JIT_CACHE_OPTION_CA

public static final int CU_JIT_CACHE_OPTION_CA

Caching modes for dlcm. (CUjit_cacheMode)

Enum values:

• JIT_CACHE_OPTION_NONE - Compile with no -dlcm flag specified
• JIT_CACHE_OPTION_CG - Compile with L1 cache disabled
• JIT_CACHE_OPTION_CA - Compile with L1 cache enabled

See Also:

Constant Field Values

CU_GRAPHICS_REGISTER_FLAGS_NONE

public static final int CU_GRAPHICS_REGISTER_FLAGS_NONE

Flags to register a graphics resource. (CUgraphicsRegisterFlags)

Enum values:

• GRAPHICS_REGISTER_FLAGS_NONE
• GRAPHICS_REGISTER_FLAGS_READ_ONLY
• GRAPHICS_REGISTER_FLAGS_WRITE_DISCARD
• GRAPHICS_REGISTER_FLAGS_SURFACE_LDST
• GRAPHICS_REGISTER_FLAGS_TEXTURE_GATHER

See Also:

Constant Field Values

CU_GRAPHICS_REGISTER_FLAGS_READ_ONLY

public static final int CU_GRAPHICS_REGISTER_FLAGS_READ_ONLY

Flags to register a graphics resource. (CUgraphicsRegisterFlags)

Enum values:

• GRAPHICS_REGISTER_FLAGS_NONE
• GRAPHICS_REGISTER_FLAGS_READ_ONLY
• GRAPHICS_REGISTER_FLAGS_WRITE_DISCARD
• GRAPHICS_REGISTER_FLAGS_SURFACE_LDST
• GRAPHICS_REGISTER_FLAGS_TEXTURE_GATHER

See Also:

Constant Field Values

CU_GRAPHICS_REGISTER_FLAGS_WRITE_DISCARD

public static final int CU_GRAPHICS_REGISTER_FLAGS_WRITE_DISCARD

Flags to register a graphics resource. (CUgraphicsRegisterFlags)

Enum values:

• GRAPHICS_REGISTER_FLAGS_NONE
• GRAPHICS_REGISTER_FLAGS_READ_ONLY
• GRAPHICS_REGISTER_FLAGS_WRITE_DISCARD
• GRAPHICS_REGISTER_FLAGS_SURFACE_LDST
• GRAPHICS_REGISTER_FLAGS_TEXTURE_GATHER

See Also:

Constant Field Values

CU_GRAPHICS_REGISTER_FLAGS_SURFACE_LDST

public static final int CU_GRAPHICS_REGISTER_FLAGS_SURFACE_LDST

Flags to register a graphics resource. (CUgraphicsRegisterFlags)

Enum values:

• GRAPHICS_REGISTER_FLAGS_NONE
• GRAPHICS_REGISTER_FLAGS_READ_ONLY
• GRAPHICS_REGISTER_FLAGS_WRITE_DISCARD
• GRAPHICS_REGISTER_FLAGS_SURFACE_LDST
• GRAPHICS_REGISTER_FLAGS_TEXTURE_GATHER

See Also:

Constant Field Values

CU_GRAPHICS_REGISTER_FLAGS_TEXTURE_GATHER

public static final int CU_GRAPHICS_REGISTER_FLAGS_TEXTURE_GATHER

Flags to register a graphics resource. (CUgraphicsRegisterFlags)

Enum values:

• GRAPHICS_REGISTER_FLAGS_NONE
• GRAPHICS_REGISTER_FLAGS_READ_ONLY
• GRAPHICS_REGISTER_FLAGS_WRITE_DISCARD
• GRAPHICS_REGISTER_FLAGS_SURFACE_LDST
• GRAPHICS_REGISTER_FLAGS_TEXTURE_GATHER

See Also:

Constant Field Values

CU_GRAPHICS_MAP_RESOURCE_FLAGS_NONE

public static final int CU_GRAPHICS_MAP_RESOURCE_FLAGS_NONE

Flags for mapping and unmapping interop resources. (CUgraphicsMapResourceFlags)

Enum values:

• GRAPHICS_MAP_RESOURCE_FLAGS_NONE
• GRAPHICS_MAP_RESOURCE_FLAGS_READ_ONLY
• GRAPHICS_MAP_RESOURCE_FLAGS_WRITE_DISCARD

See Also:

Constant Field Values

CU_GRAPHICS_MAP_RESOURCE_FLAGS_READ_ONLY

public static final int CU_GRAPHICS_MAP_RESOURCE_FLAGS_READ_ONLY

Flags for mapping and unmapping interop resources. (CUgraphicsMapResourceFlags)

Enum values:

• GRAPHICS_MAP_RESOURCE_FLAGS_NONE
• GRAPHICS_MAP_RESOURCE_FLAGS_READ_ONLY
• GRAPHICS_MAP_RESOURCE_FLAGS_WRITE_DISCARD

See Also:

Constant Field Values

CU_GRAPHICS_MAP_RESOURCE_FLAGS_WRITE_DISCARD

public static final int CU_GRAPHICS_MAP_RESOURCE_FLAGS_WRITE_DISCARD

Flags for mapping and unmapping interop resources. (CUgraphicsMapResourceFlags)

Enum values:

• GRAPHICS_MAP_RESOURCE_FLAGS_NONE
• GRAPHICS_MAP_RESOURCE_FLAGS_READ_ONLY
• GRAPHICS_MAP_RESOURCE_FLAGS_WRITE_DISCARD

See Also:

Constant Field Values

CU_CUBEMAP_FACE_POSITIVE_X

public static final int CU_CUBEMAP_FACE_POSITIVE_X

Array indices for cube faces. (CUarray_cubemap_face)

Enum values:

• CUBEMAP_FACE_POSITIVE_X - Positive X face of cubemap
• CUBEMAP_FACE_NEGATIVE_X - Negative X face of cubemap
• CUBEMAP_FACE_POSITIVE_Y - Positive Y face of cubemap
• CUBEMAP_FACE_NEGATIVE_Y - Negative Y face of cubemap
• CUBEMAP_FACE_POSITIVE_Z - Positive Z face of cubemap
• CUBEMAP_FACE_NEGATIVE_Z - Negative Z face of cubemap

See Also:

Constant Field Values

CU_CUBEMAP_FACE_NEGATIVE_X

public static final int CU_CUBEMAP_FACE_NEGATIVE_X

Array indices for cube faces. (CUarray_cubemap_face)

Enum values:

• CUBEMAP_FACE_POSITIVE_X - Positive X face of cubemap
• CUBEMAP_FACE_NEGATIVE_X - Negative X face of cubemap
• CUBEMAP_FACE_POSITIVE_Y - Positive Y face of cubemap
• CUBEMAP_FACE_NEGATIVE_Y - Negative Y face of cubemap
• CUBEMAP_FACE_POSITIVE_Z - Positive Z face of cubemap
• CUBEMAP_FACE_NEGATIVE_Z - Negative Z face of cubemap

See Also:

Constant Field Values

CU_CUBEMAP_FACE_POSITIVE_Y

public static final int CU_CUBEMAP_FACE_POSITIVE_Y

Array indices for cube faces. (CUarray_cubemap_face)

Enum values:

• CUBEMAP_FACE_POSITIVE_X - Positive X face of cubemap
• CUBEMAP_FACE_NEGATIVE_X - Negative X face of cubemap
• CUBEMAP_FACE_POSITIVE_Y - Positive Y face of cubemap
• CUBEMAP_FACE_NEGATIVE_Y - Negative Y face of cubemap
• CUBEMAP_FACE_POSITIVE_Z - Positive Z face of cubemap
• CUBEMAP_FACE_NEGATIVE_Z - Negative Z face of cubemap

See Also:

Constant Field Values

CU_CUBEMAP_FACE_NEGATIVE_Y

public static final int CU_CUBEMAP_FACE_NEGATIVE_Y

Array indices for cube faces. (CUarray_cubemap_face)

Enum values:

• CUBEMAP_FACE_POSITIVE_X - Positive X face of cubemap
• CUBEMAP_FACE_NEGATIVE_X - Negative X face of cubemap
• CUBEMAP_FACE_POSITIVE_Y - Positive Y face of cubemap
• CUBEMAP_FACE_NEGATIVE_Y - Negative Y face of cubemap
• CUBEMAP_FACE_POSITIVE_Z - Positive Z face of cubemap
• CUBEMAP_FACE_NEGATIVE_Z - Negative Z face of cubemap

See Also:

Constant Field Values

CU_CUBEMAP_FACE_POSITIVE_Z

public static final int CU_CUBEMAP_FACE_POSITIVE_Z

Array indices for cube faces. (CUarray_cubemap_face)

Enum values:

• CUBEMAP_FACE_POSITIVE_X - Positive X face of cubemap
• CUBEMAP_FACE_NEGATIVE_X - Negative X face of cubemap
• CUBEMAP_FACE_POSITIVE_Y - Positive Y face of cubemap
• CUBEMAP_FACE_NEGATIVE_Y - Negative Y face of cubemap
• CUBEMAP_FACE_POSITIVE_Z - Positive Z face of cubemap
• CUBEMAP_FACE_NEGATIVE_Z - Negative Z face of cubemap

See Also:

Constant Field Values

CU_CUBEMAP_FACE_NEGATIVE_Z

public static final int CU_CUBEMAP_FACE_NEGATIVE_Z

Array indices for cube faces. (CUarray_cubemap_face)

Enum values:

• CUBEMAP_FACE_POSITIVE_X - Positive X face of cubemap
• CUBEMAP_FACE_NEGATIVE_X - Negative X face of cubemap
• CUBEMAP_FACE_POSITIVE_Y - Positive Y face of cubemap
• CUBEMAP_FACE_NEGATIVE_Y - Negative Y face of cubemap
• CUBEMAP_FACE_POSITIVE_Z - Positive Z face of cubemap
• CUBEMAP_FACE_NEGATIVE_Z - Negative Z face of cubemap

See Also:

Constant Field Values

CU_LIMIT_STACK_SIZE

public static final int CU_LIMIT_STACK_SIZE

Limits. (CUlimit)

Enum values:

• LIMIT_STACK_SIZE - GPU thread stack size
• LIMIT_PRINTF_FIFO_SIZE - GPU printf FIFO size
• LIMIT_MALLOC_HEAP_SIZE - GPU malloc heap size
• LIMIT_DEV_RUNTIME_SYNC_DEPTH - GPU device runtime launch synchronize depth
• LIMIT_DEV_RUNTIME_PENDING_LAUNCH_COUNT - GPU device runtime pending launch count
• LIMIT_MAX_L2_FETCH_GRANULARITY - A value between 0 and 128 that indicates the maximum fetch granularity of L2 (in Bytes). This is a hint

See Also:

Constant Field Values

CU_LIMIT_PRINTF_FIFO_SIZE

public static final int CU_LIMIT_PRINTF_FIFO_SIZE

Limits. (CUlimit)

Enum values:

• LIMIT_STACK_SIZE - GPU thread stack size
• LIMIT_PRINTF_FIFO_SIZE - GPU printf FIFO size
• LIMIT_MALLOC_HEAP_SIZE - GPU malloc heap size
• LIMIT_DEV_RUNTIME_SYNC_DEPTH - GPU device runtime launch synchronize depth
• LIMIT_DEV_RUNTIME_PENDING_LAUNCH_COUNT - GPU device runtime pending launch count
• LIMIT_MAX_L2_FETCH_GRANULARITY - A value between 0 and 128 that indicates the maximum fetch granularity of L2 (in Bytes). This is a hint

See Also:

Constant Field Values

CU_LIMIT_MALLOC_HEAP_SIZE

public static final int CU_LIMIT_MALLOC_HEAP_SIZE

Limits. (CUlimit)

Enum values:

• LIMIT_STACK_SIZE - GPU thread stack size
• LIMIT_PRINTF_FIFO_SIZE - GPU printf FIFO size
• LIMIT_MALLOC_HEAP_SIZE - GPU malloc heap size
• LIMIT_DEV_RUNTIME_SYNC_DEPTH - GPU device runtime launch synchronize depth
• LIMIT_DEV_RUNTIME_PENDING_LAUNCH_COUNT - GPU device runtime pending launch count
• LIMIT_MAX_L2_FETCH_GRANULARITY - A value between 0 and 128 that indicates the maximum fetch granularity of L2 (in Bytes). This is a hint

See Also:

Constant Field Values

CU_LIMIT_DEV_RUNTIME_SYNC_DEPTH

public static final int CU_LIMIT_DEV_RUNTIME_SYNC_DEPTH

Limits. (CUlimit)

Enum values:

• LIMIT_STACK_SIZE - GPU thread stack size
• LIMIT_PRINTF_FIFO_SIZE - GPU printf FIFO size
• LIMIT_MALLOC_HEAP_SIZE - GPU malloc heap size
• LIMIT_DEV_RUNTIME_SYNC_DEPTH - GPU device runtime launch synchronize depth
• LIMIT_DEV_RUNTIME_PENDING_LAUNCH_COUNT - GPU device runtime pending launch count
• LIMIT_MAX_L2_FETCH_GRANULARITY - A value between 0 and 128 that indicates the maximum fetch granularity of L2 (in Bytes). This is a hint

See Also:

Constant Field Values

CU_LIMIT_DEV_RUNTIME_PENDING_LAUNCH_COUNT

public static final int CU_LIMIT_DEV_RUNTIME_PENDING_LAUNCH_COUNT

Limits. (CUlimit)

Enum values:

• LIMIT_STACK_SIZE - GPU thread stack size
• LIMIT_PRINTF_FIFO_SIZE - GPU printf FIFO size
• LIMIT_MALLOC_HEAP_SIZE - GPU malloc heap size
• LIMIT_DEV_RUNTIME_SYNC_DEPTH - GPU device runtime launch synchronize depth
• LIMIT_DEV_RUNTIME_PENDING_LAUNCH_COUNT - GPU device runtime pending launch count
• LIMIT_MAX_L2_FETCH_GRANULARITY - A value between 0 and 128 that indicates the maximum fetch granularity of L2 (in Bytes). This is a hint

See Also:

Constant Field Values

CU_LIMIT_MAX_L2_FETCH_GRANULARITY

public static final int CU_LIMIT_MAX_L2_FETCH_GRANULARITY

Limits. (CUlimit)

Enum values:

• LIMIT_STACK_SIZE - GPU thread stack size
• LIMIT_PRINTF_FIFO_SIZE - GPU printf FIFO size
• LIMIT_MALLOC_HEAP_SIZE - GPU malloc heap size
• LIMIT_DEV_RUNTIME_SYNC_DEPTH - GPU device runtime launch synchronize depth
• LIMIT_DEV_RUNTIME_PENDING_LAUNCH_COUNT - GPU device runtime pending launch count
• LIMIT_MAX_L2_FETCH_GRANULARITY - A value between 0 and 128 that indicates the maximum fetch granularity of L2 (in Bytes). This is a hint

See Also:

Constant Field Values

CUDA_SUCCESS

public static final int CUDA_SUCCESS

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_INVALID_VALUE

public static final int CUDA_ERROR_INVALID_VALUE

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_OUT_OF_MEMORY

public static final int CUDA_ERROR_OUT_OF_MEMORY

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_NOT_INITIALIZED

public static final int CUDA_ERROR_NOT_INITIALIZED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_DEINITIALIZED

public static final int CUDA_ERROR_DEINITIALIZED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_PROFILER_DISABLED

public static final int CUDA_ERROR_PROFILER_DISABLED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_PROFILER_NOT_INITIALIZED

public static final int CUDA_ERROR_PROFILER_NOT_INITIALIZED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_PROFILER_ALREADY_STARTED

public static final int CUDA_ERROR_PROFILER_ALREADY_STARTED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_PROFILER_ALREADY_STOPPED

public static final int CUDA_ERROR_PROFILER_ALREADY_STOPPED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_NO_DEVICE

public static final int CUDA_ERROR_NO_DEVICE

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_INVALID_DEVICE

public static final int CUDA_ERROR_INVALID_DEVICE

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_INVALID_IMAGE

public static final int CUDA_ERROR_INVALID_IMAGE

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_INVALID_CONTEXT

public static final int CUDA_ERROR_INVALID_CONTEXT

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_CONTEXT_ALREADY_CURRENT

public static final int CUDA_ERROR_CONTEXT_ALREADY_CURRENT

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_MAP_FAILED

public static final int CUDA_ERROR_MAP_FAILED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_UNMAP_FAILED

public static final int CUDA_ERROR_UNMAP_FAILED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_ARRAY_IS_MAPPED

public static final int CUDA_ERROR_ARRAY_IS_MAPPED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_ALREADY_MAPPED

public static final int CUDA_ERROR_ALREADY_MAPPED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_NO_BINARY_FOR_GPU

public static final int CUDA_ERROR_NO_BINARY_FOR_GPU

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_ALREADY_ACQUIRED

public static final int CUDA_ERROR_ALREADY_ACQUIRED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_NOT_MAPPED

public static final int CUDA_ERROR_NOT_MAPPED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_NOT_MAPPED_AS_ARRAY

public static final int CUDA_ERROR_NOT_MAPPED_AS_ARRAY

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_NOT_MAPPED_AS_POINTER

public static final int CUDA_ERROR_NOT_MAPPED_AS_POINTER

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_ECC_UNCORRECTABLE

public static final int CUDA_ERROR_ECC_UNCORRECTABLE

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_UNSUPPORTED_LIMIT

public static final int CUDA_ERROR_UNSUPPORTED_LIMIT

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_CONTEXT_ALREADY_IN_USE

public static final int CUDA_ERROR_CONTEXT_ALREADY_IN_USE

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_PEER_ACCESS_UNSUPPORTED

public static final int CUDA_ERROR_PEER_ACCESS_UNSUPPORTED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_INVALID_PTX

public static final int CUDA_ERROR_INVALID_PTX

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_INVALID_GRAPHICS_CONTEXT

public static final int CUDA_ERROR_INVALID_GRAPHICS_CONTEXT

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_NVLINK_UNCORRECTABLE

public static final int CUDA_ERROR_NVLINK_UNCORRECTABLE

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_JIT_COMPILER_NOT_FOUND

public static final int CUDA_ERROR_JIT_COMPILER_NOT_FOUND

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_INVALID_SOURCE

public static final int CUDA_ERROR_INVALID_SOURCE

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_FILE_NOT_FOUND

public static final int CUDA_ERROR_FILE_NOT_FOUND

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND

public static final int CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_SHARED_OBJECT_INIT_FAILED

public static final int CUDA_ERROR_SHARED_OBJECT_INIT_FAILED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_OPERATING_SYSTEM

public static final int CUDA_ERROR_OPERATING_SYSTEM

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_INVALID_HANDLE

public static final int CUDA_ERROR_INVALID_HANDLE

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_ILLEGAL_STATE

public static final int CUDA_ERROR_ILLEGAL_STATE

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_NOT_FOUND

public static final int CUDA_ERROR_NOT_FOUND

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_NOT_READY

public static final int CUDA_ERROR_NOT_READY

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_ILLEGAL_ADDRESS

public static final int CUDA_ERROR_ILLEGAL_ADDRESS

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES

public static final int CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_LAUNCH_TIMEOUT

public static final int CUDA_ERROR_LAUNCH_TIMEOUT

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING

public static final int CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED

public static final int CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_PEER_ACCESS_NOT_ENABLED

public static final int CUDA_ERROR_PEER_ACCESS_NOT_ENABLED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE

public static final int CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_CONTEXT_IS_DESTROYED

public static final int CUDA_ERROR_CONTEXT_IS_DESTROYED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_ASSERT

public static final int CUDA_ERROR_ASSERT

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_TOO_MANY_PEERS

public static final int CUDA_ERROR_TOO_MANY_PEERS

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED

public static final int CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED

public static final int CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_HARDWARE_STACK_ERROR

public static final int CUDA_ERROR_HARDWARE_STACK_ERROR

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_ILLEGAL_INSTRUCTION

public static final int CUDA_ERROR_ILLEGAL_INSTRUCTION

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_MISALIGNED_ADDRESS

public static final int CUDA_ERROR_MISALIGNED_ADDRESS

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_INVALID_ADDRESS_SPACE

public static final int CUDA_ERROR_INVALID_ADDRESS_SPACE

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_INVALID_PC

public static final int CUDA_ERROR_INVALID_PC

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_LAUNCH_FAILED

public static final int CUDA_ERROR_LAUNCH_FAILED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE

public static final int CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_NOT_PERMITTED

public static final int CUDA_ERROR_NOT_PERMITTED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_NOT_SUPPORTED

public static final int CUDA_ERROR_NOT_SUPPORTED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_SYSTEM_NOT_READY

public static final int CUDA_ERROR_SYSTEM_NOT_READY

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_SYSTEM_DRIVER_MISMATCH

public static final int CUDA_ERROR_SYSTEM_DRIVER_MISMATCH

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE

public static final int CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED

public static final int CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_STREAM_CAPTURE_INVALIDATED

public static final int CUDA_ERROR_STREAM_CAPTURE_INVALIDATED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_STREAM_CAPTURE_MERGE

public static final int CUDA_ERROR_STREAM_CAPTURE_MERGE

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_STREAM_CAPTURE_UNMATCHED

public static final int CUDA_ERROR_STREAM_CAPTURE_UNMATCHED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_STREAM_CAPTURE_UNJOINED

public static final int CUDA_ERROR_STREAM_CAPTURE_UNJOINED

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_STREAM_CAPTURE_ISOLATION

public static final int CUDA_ERROR_STREAM_CAPTURE_ISOLATION

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_STREAM_CAPTURE_IMPLICIT

public static final int CUDA_ERROR_STREAM_CAPTURE_IMPLICIT

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_CAPTURED_EVENT

public static final int CUDA_ERROR_CAPTURED_EVENT

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD

public static final int CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

CUDA_ERROR_UNKNOWN

public static final int CUDA_ERROR_UNKNOWN

Error codes. (CUresult)

Enum values:

• CUDA_SUCCESS - The API call returned with no errors.

  In the case of query calls, this also means that the operation being queried is complete (see EventQuery and StreamQuery).

• CUDA_ERROR_INVALID_VALUE - This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
• CUDA_ERROR_OUT_OF_MEMORY - The API call failed because it was unable to allocate enough memory to perform the requested operation.
• CUDA_ERROR_NOT_INITIALIZED - This indicates that the CUDA driver has not been initialized with Init or that initialization has failed.
• CUDA_ERROR_DEINITIALIZED - This indicates that the CUDA driver is in the process of shutting down.
• CUDA_ERROR_PROFILER_DISABLED - This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
• CUDA_ERROR_PROFILER_NOT_INITIALIZED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to attempt to enable/disable the profiling via ProfilerStart or ProfilerStop without initialization.

• CUDA_ERROR_PROFILER_ALREADY_STARTED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStart when profiling is already enabled.

• CUDA_ERROR_PROFILER_ALREADY_STOPPED - This error return is deprecated as of CUDA 5.0.

  It is no longer an error to call ProfilerStop when profiling is already disabled.

• CUDA_ERROR_NO_DEVICE - This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
• CUDA_ERROR_INVALID_DEVICE - This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
• CUDA_ERROR_INVALID_IMAGE - This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
• CUDA_ERROR_INVALID_CONTEXT - This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had CtxDestroy invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

  See CtxGetApiVersion for more details.

• CUDA_ERROR_CONTEXT_ALREADY_CURRENT - This indicated that the context being supplied as a parameter to the API call was already the active context.

  This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via CtxPushCurrent.

• CUDA_ERROR_MAP_FAILED - This indicates that a map or register operation has failed.
• CUDA_ERROR_UNMAP_FAILED - This indicates that an unmap or unregister operation has failed.
• CUDA_ERROR_ARRAY_IS_MAPPED - This indicates that the specified array is currently mapped and thus cannot be destroyed.
• CUDA_ERROR_ALREADY_MAPPED - This indicates that the resource is already mapped.
• CUDA_ERROR_NO_BINARY_FOR_GPU - This indicates that there is no kernel image available that is suitable for the device.

  This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

• CUDA_ERROR_ALREADY_ACQUIRED - This indicates that a resource has already been acquired.
• CUDA_ERROR_NOT_MAPPED - This indicates that a resource is not mapped.
• CUDA_ERROR_NOT_MAPPED_AS_ARRAY - This indicates that a mapped resource is not available for access as an array.
• CUDA_ERROR_NOT_MAPPED_AS_POINTER - This indicates that a mapped resource is not available for access as a pointer.
• CUDA_ERROR_ECC_UNCORRECTABLE - This indicates that an uncorrectable ECC error was detected during execution.
• CUDA_ERROR_UNSUPPORTED_LIMIT - This indicates that the CUlimit passed to the API call is not supported by the active device.
• CUDA_ERROR_CONTEXT_ALREADY_IN_USE - This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
• CUDA_ERROR_PEER_ACCESS_UNSUPPORTED - This indicates that peer access is not supported across the given devices.
• CUDA_ERROR_INVALID_PTX - This indicates that a PTX JIT compilation failed.
• CUDA_ERROR_INVALID_GRAPHICS_CONTEXT - This indicates an error with OpenGL or DirectX context.
• CUDA_ERROR_NVLINK_UNCORRECTABLE - This indicates that an uncorrectable NVLink error was detected during the execution.
• CUDA_ERROR_JIT_COMPILER_NOT_FOUND - This indicates that the PTX JIT compiler library was not found.
• CUDA_ERROR_INVALID_SOURCE - This indicates that the device kernel source is invalid.
• CUDA_ERROR_FILE_NOT_FOUND - This indicates that the file specified was not found.
• CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND - This indicates that a link to a shared object failed to resolve.
• CUDA_ERROR_SHARED_OBJECT_INIT_FAILED - This indicates that initialization of a shared object failed.
• CUDA_ERROR_OPERATING_SYSTEM - This indicates that an OS call failed.
• CUDA_ERROR_INVALID_HANDLE - This indicates that a resource handle passed to the API call was not valid.

  Resource handles are opaque types like CUstream and CUevent.

• CUDA_ERROR_ILLEGAL_STATE - This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
• CUDA_ERROR_NOT_FOUND - This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
• CUDA_ERROR_NOT_READY - This indicates that asynchronous operations issued previously have not completed yet.

  This result is not actually an error, but must be indicated differently than CUDA_SUCCESS (which indicates completion). Calls that may return this value include EventQuery and StreamQuery.

• CUDA_ERROR_ILLEGAL_ADDRESS - While executing a kernel, the device encountered a load or store instruction on an invalid memory address.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES - This indicates that a launch did not occur because it did not have appropriate resources.

  This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

• CUDA_ERROR_LAUNCH_TIMEOUT - This indicates that the device kernel took too long to execute.

  This can only occur if timeouts are enabled - see the device attribute DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING - This error indicates a kernel launch that uses an incompatible texturing mode.
• CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED - This error indicates that a call to CtxEnablePeerAccess is trying to re-enable peer access to a context which has already had peer access to it enabled.
• CUDA_ERROR_PEER_ACCESS_NOT_ENABLED - This error indicates that CtxDisablePeerAccess is trying to disable peer access which has not been enabled yet via CtxEnablePeerAccess.
• CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE - This error indicates that the primary context for the specified device has already been initialized.
• CUDA_ERROR_CONTEXT_IS_DESTROYED - This error indicates that the context current to the calling thread has been destroyed using CtxDestroy, or is a primary context which has not yet been initialized.
• CUDA_ERROR_ASSERT - A device-side assert triggered during kernel execution.

  The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

• CUDA_ERROR_TOO_MANY_PEERS - This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to CtxEnablePeerAccess.
• CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED - This error indicates that the memory range passed to MemHostRegister has already been registered.
• CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED - This error indicates that the pointer passed to MemHostUnregister does not correspond to any currently registered memory region.
• CUDA_ERROR_HARDWARE_STACK_ERROR - While executing a kernel, the device encountered a stack error.

  This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_ILLEGAL_INSTRUCTION - While executing a kernel, the device encountered an illegal instruction.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_MISALIGNED_ADDRESS - While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_ADDRESS_SPACE - While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_INVALID_PC - While executing a kernel, the device program counter wrapped its address space.

  This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_LAUNCH_FAILED - An exception occurred on the device while executing a kernel.

  Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide.This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.

• CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE - This error indicates that the number of blocks launched per grid for a kernel that was launched via either LaunchCooperativeKernel or LaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by OccupancyMaxActiveBlocksPerMultiprocessor or OccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
• CUDA_ERROR_NOT_PERMITTED - This error indicates that the attempted operation is not permitted.
• CUDA_ERROR_NOT_SUPPORTED - This error indicates that the attempted operation is not supported on the current system or device.
• CUDA_ERROR_SYSTEM_NOT_READY - This error indicates that the system is not yet ready to start any CUDA work.

  To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.

• CUDA_ERROR_SYSTEM_DRIVER_MISMATCH - This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver.

  Refer to the compatibility documentation for supported versions.

• CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE - This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration.

  Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.

• CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED - This error indicates that the operation is not permitted when the stream is capturing.
• CUDA_ERROR_STREAM_CAPTURE_INVALIDATED - This error indicates that the current capture sequence on the stream has been invalidated due to a previous error.
• CUDA_ERROR_STREAM_CAPTURE_MERGE - This error indicates that the operation would have resulted in a merge of two independent capture sequences.
• CUDA_ERROR_STREAM_CAPTURE_UNMATCHED - This error indicates that the capture was not initiated in this stream.
• CUDA_ERROR_STREAM_CAPTURE_UNJOINED - This error indicates that the capture sequence contains a fork that was not joined to the primary stream.
• CUDA_ERROR_STREAM_CAPTURE_ISOLATION - This error indicates that a dependency would have been created which crosses the capture sequence boundary.

  Only implicit in-stream ordering dependencies are allowed to cross the boundary.

• CUDA_ERROR_STREAM_CAPTURE_IMPLICIT - This error indicates a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
• CUDA_ERROR_CAPTURED_EVENT - This error indicates that the operation is not permitted on an event which was last recorded in a capturing stream.
• CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD - A stream capture sequence not initiated with the STREAM_CAPTURE_MODE_RELAXED argument to StreamBeginCapture was passed to StreamEndCapture in a different thread.
• CUDA_ERROR_UNKNOWN - This indicates that an unknown internal error has occurred.

See Also:

Constant Field Values

Method Detail

getLibrary

public static org.lwjgl.system.SharedLibrary getLibrary()

Returns the NVCUDA SharedLibrary.

ncuGetErrorString

public static int ncuGetErrorString(int error,
                                    long pStr)

cuGetErrorString

public static int cuGetErrorString(int error,
                                   org.lwjgl.PointerBuffer pStr)

ncuGetErrorName

public static int ncuGetErrorName(int error,
                                  long pStr)

cuGetErrorName

public static int cuGetErrorName(int error,
                                 org.lwjgl.PointerBuffer pStr)

cuInit

public static int cuInit(int Flags)

ncuDriverGetVersion

public static int ncuDriverGetVersion(long driverVersion)

cuDriverGetVersion

public static int cuDriverGetVersion(java.nio.IntBuffer driverVersion)

ncuDeviceGet

public static int ncuDeviceGet(long device,
                               int ordinal)

cuDeviceGet

public static int cuDeviceGet(java.nio.IntBuffer device,
                              int ordinal)

ncuDeviceGetCount

public static int ncuDeviceGetCount(long count)

cuDeviceGetCount

public static int cuDeviceGetCount(java.nio.IntBuffer count)

ncuDeviceGetName

public static int ncuDeviceGetName(long name,
                                   int len,
                                   int dev)

cuDeviceGetName

public static int cuDeviceGetName(java.nio.ByteBuffer name,
                                  int dev)

ncuDeviceTotalMem

public static int ncuDeviceTotalMem(long bytes,
                                    int dev)

cuDeviceTotalMem

public static int cuDeviceTotalMem(org.lwjgl.PointerBuffer bytes,
                                   int dev)

ncuDeviceGetAttribute

public static int ncuDeviceGetAttribute(long pi,
                                        int attrib,
                                        int dev)

cuDeviceGetAttribute

public static int cuDeviceGetAttribute(java.nio.IntBuffer pi,
                                       int attrib,
                                       int dev)

ncuDeviceGetProperties

public static int ncuDeviceGetProperties(long prop,
                                         int dev)

cuDeviceGetProperties

public static int cuDeviceGetProperties(CUdevprop prop,
                                        int dev)

ncuDeviceComputeCapability

public static int ncuDeviceComputeCapability(long major,
                                             long minor,
                                             int dev)

cuDeviceComputeCapability

public static int cuDeviceComputeCapability(java.nio.IntBuffer major,
                                            java.nio.IntBuffer minor,
                                            int dev)

ncuCtxCreate

public static int ncuCtxCreate(long pctx,
                               int flags,
                               int dev)

cuCtxCreate

public static int cuCtxCreate(org.lwjgl.PointerBuffer pctx,
                              int flags,
                              int dev)

ncuCtxGetDevice

public static int ncuCtxGetDevice(long device)

cuCtxGetDevice

public static int cuCtxGetDevice(java.nio.IntBuffer device)

cuCtxSynchronize

public static int cuCtxSynchronize()

cuCtxSetLimit

public static int cuCtxSetLimit(int limit,
                                long value)

ncuCtxGetLimit

public static int ncuCtxGetLimit(long pvalue,
                                 int limit)

cuCtxGetLimit

public static int cuCtxGetLimit(org.lwjgl.PointerBuffer pvalue,
                                int limit)

ncuCtxGetCacheConfig

public static int ncuCtxGetCacheConfig(long pconfig)

cuCtxGetCacheConfig

public static int cuCtxGetCacheConfig(java.nio.IntBuffer pconfig)

cuCtxSetCacheConfig

public static int cuCtxSetCacheConfig(int config)

ncuCtxGetApiVersion

public static int ncuCtxGetApiVersion(long ctx,
                                      long version)

cuCtxGetApiVersion

public static int cuCtxGetApiVersion(long ctx,
                                     java.nio.IntBuffer version)

ncuCtxGetStreamPriorityRange

public static int ncuCtxGetStreamPriorityRange(long leastPriority,
                                               long greatestPriority)

cuCtxGetStreamPriorityRange

public static int cuCtxGetStreamPriorityRange(@Nullable
                                              java.nio.IntBuffer leastPriority,
                                              @Nullable
                                              java.nio.IntBuffer greatestPriority)

ncuCtxAttach

public static int ncuCtxAttach(long pctx,
                               int flags)

cuCtxAttach

public static int cuCtxAttach(org.lwjgl.PointerBuffer pctx,
                              int flags)

cuCtxDetach

public static int cuCtxDetach(long ctx)

ncuModuleLoad

public static int ncuModuleLoad(long module,
                                long fname)

cuModuleLoad

public static int cuModuleLoad(org.lwjgl.PointerBuffer module,
                               java.nio.ByteBuffer fname)

cuModuleLoad

public static int cuModuleLoad(org.lwjgl.PointerBuffer module,
                               java.lang.CharSequence fname)

ncuModuleLoadData

public static int ncuModuleLoadData(long module,
                                    long image)

cuModuleLoadData

public static int cuModuleLoadData(org.lwjgl.PointerBuffer module,
                                   java.nio.ByteBuffer image)

ncuModuleLoadDataEx

public static int ncuModuleLoadDataEx(long module,
                                      long image,
                                      int numOptions,
                                      long options,
                                      long optionValues)

cuModuleLoadDataEx

public static int cuModuleLoadDataEx(org.lwjgl.PointerBuffer module,
                                     java.nio.ByteBuffer image,
                                     @Nullable
                                     java.nio.IntBuffer options,
                                     @Nullable
                                     org.lwjgl.PointerBuffer optionValues)

ncuModuleLoadFatBinary

public static int ncuModuleLoadFatBinary(long module,
                                         long fatCubin)

cuModuleLoadFatBinary

public static int cuModuleLoadFatBinary(org.lwjgl.PointerBuffer module,
                                        java.nio.ByteBuffer fatCubin)

cuModuleUnload

public static int cuModuleUnload(long hmod)

ncuModuleGetFunction

public static int ncuModuleGetFunction(long hfunc,
                                       long hmod,
                                       long name)

cuModuleGetFunction

public static int cuModuleGetFunction(org.lwjgl.PointerBuffer hfunc,
                                      long hmod,
                                      java.nio.ByteBuffer name)

cuModuleGetFunction

public static int cuModuleGetFunction(org.lwjgl.PointerBuffer hfunc,
                                      long hmod,
                                      java.lang.CharSequence name)

ncuModuleGetGlobal

public static int ncuModuleGetGlobal(long dptr,
                                     long bytes,
                                     long hmod,
                                     long name)

cuModuleGetGlobal

public static int cuModuleGetGlobal(@Nullable
                                    org.lwjgl.PointerBuffer dptr,
                                    @Nullable
                                    org.lwjgl.PointerBuffer bytes,
                                    long hmod,
                                    java.nio.ByteBuffer name)

cuModuleGetGlobal

public static int cuModuleGetGlobal(@Nullable
                                    org.lwjgl.PointerBuffer dptr,
                                    @Nullable
                                    org.lwjgl.PointerBuffer bytes,
                                    long hmod,
                                    java.lang.CharSequence name)

ncuModuleGetTexRef

public static int ncuModuleGetTexRef(long pTexRef,
                                     long hmod,
                                     long name)

cuModuleGetTexRef

public static int cuModuleGetTexRef(org.lwjgl.PointerBuffer pTexRef,
                                    long hmod,
                                    java.nio.ByteBuffer name)

cuModuleGetTexRef

public static int cuModuleGetTexRef(org.lwjgl.PointerBuffer pTexRef,
                                    long hmod,
                                    java.lang.CharSequence name)

ncuModuleGetSurfRef

public static int ncuModuleGetSurfRef(long pSurfRef,
                                      long hmod,
                                      long name)

cuModuleGetSurfRef

public static int cuModuleGetSurfRef(org.lwjgl.PointerBuffer pSurfRef,
                                     long hmod,
                                     java.nio.ByteBuffer name)

cuModuleGetSurfRef

public static int cuModuleGetSurfRef(org.lwjgl.PointerBuffer pSurfRef,
                                     long hmod,
                                     java.lang.CharSequence name)

ncuMemGetInfo

public static int ncuMemGetInfo(long free,
                                long total)

cuMemGetInfo

public static int cuMemGetInfo(org.lwjgl.PointerBuffer free,
                               org.lwjgl.PointerBuffer total)

ncuMemAlloc

public static int ncuMemAlloc(long dptr,
                              long bytesize)

cuMemAlloc

public static int cuMemAlloc(org.lwjgl.PointerBuffer dptr,
                             long bytesize)

ncuMemAllocPitch

public static int ncuMemAllocPitch(long dptr,
                                   long pPitch,
                                   long WidthInBytes,
                                   long Height,
                                   int ElementSizeBytes)

cuMemAllocPitch

public static int cuMemAllocPitch(org.lwjgl.PointerBuffer dptr,
                                  org.lwjgl.PointerBuffer pPitch,
                                  long WidthInBytes,
                                  long Height,
                                  int ElementSizeBytes)

cuMemFree

public static int cuMemFree(long dptr)

ncuMemGetAddressRange

public static int ncuMemGetAddressRange(long pbase,
                                        long psize,
                                        long dptr)

cuMemGetAddressRange

public static int cuMemGetAddressRange(@Nullable
                                       org.lwjgl.PointerBuffer pbase,
                                       @Nullable
                                       org.lwjgl.PointerBuffer psize,
                                       long dptr)

ncuMemAllocHost

public static int ncuMemAllocHost(long pp,
                                  long bytesize)

cuMemAllocHost

public static int cuMemAllocHost(org.lwjgl.PointerBuffer pp,
                                 long bytesize)

ncuMemFreeHost

public static int ncuMemFreeHost(long p)

cuMemFreeHost

public static int cuMemFreeHost(java.nio.ByteBuffer p)

ncuMemHostAlloc

public static int ncuMemHostAlloc(long pp,
                                  long bytesize,
                                  int Flags)

cuMemHostAlloc

public static int cuMemHostAlloc(org.lwjgl.PointerBuffer pp,
                                 long bytesize,
                                 int Flags)

ncuMemHostGetDevicePointer

public static int ncuMemHostGetDevicePointer(long pdptr,
                                             long p,
                                             int Flags)

cuMemHostGetDevicePointer

public static int cuMemHostGetDevicePointer(org.lwjgl.PointerBuffer pdptr,
                                            java.nio.ByteBuffer p,
                                            int Flags)

ncuMemHostGetFlags

public static int ncuMemHostGetFlags(long pFlags,
                                     long p)

cuMemHostGetFlags

public static int cuMemHostGetFlags(java.nio.IntBuffer pFlags,
                                    java.nio.ByteBuffer p)

ncuMemcpyHtoD

public static int ncuMemcpyHtoD(long dstDevice,
                                long srcHost,
                                long ByteCount)

cuMemcpyHtoD

public static int cuMemcpyHtoD(long dstDevice,
                               java.nio.ByteBuffer srcHost)

cuMemcpyHtoD

public static int cuMemcpyHtoD(long dstDevice,
                               java.nio.ShortBuffer srcHost)

cuMemcpyHtoD

public static int cuMemcpyHtoD(long dstDevice,
                               java.nio.IntBuffer srcHost)

cuMemcpyHtoD

public static int cuMemcpyHtoD(long dstDevice,
                               java.nio.LongBuffer srcHost)

cuMemcpyHtoD

public static int cuMemcpyHtoD(long dstDevice,
                               java.nio.FloatBuffer srcHost)

cuMemcpyHtoD

public static int cuMemcpyHtoD(long dstDevice,
                               java.nio.DoubleBuffer srcHost)

cuMemcpyHtoD

public static int cuMemcpyHtoD(long dstDevice,
                               org.lwjgl.PointerBuffer srcHost)

ncuMemcpyDtoH

public static int ncuMemcpyDtoH(long dstHost,
                                long srcDevice,
                                long ByteCount)

cuMemcpyDtoH

public static int cuMemcpyDtoH(java.nio.ByteBuffer dstHost,
                               long srcDevice)

cuMemcpyDtoH

public static int cuMemcpyDtoH(java.nio.ShortBuffer dstHost,
                               long srcDevice)

cuMemcpyDtoH

public static int cuMemcpyDtoH(java.nio.IntBuffer dstHost,
                               long srcDevice)

cuMemcpyDtoH

public static int cuMemcpyDtoH(java.nio.LongBuffer dstHost,
                               long srcDevice)

cuMemcpyDtoH

public static int cuMemcpyDtoH(java.nio.FloatBuffer dstHost,
                               long srcDevice)

cuMemcpyDtoH

public static int cuMemcpyDtoH(java.nio.DoubleBuffer dstHost,
                               long srcDevice)

cuMemcpyDtoH

public static int cuMemcpyDtoH(org.lwjgl.PointerBuffer dstHost,
                               long srcDevice)

cuMemcpyDtoD

public static int cuMemcpyDtoD(long dstDevice,
                               long srcDevice,
                               long ByteCount)

cuMemcpyDtoA

public static int cuMemcpyDtoA(long dstArray,
                               long dstOffset,
                               long srcDevice,
                               long ByteCount)

cuMemcpyAtoD

public static int cuMemcpyAtoD(long dstDevice,
                               long srcArray,
                               long srcOffset,
                               long ByteCount)

ncuMemcpyHtoA

public static int ncuMemcpyHtoA(long dstArray,
                                long dstOffset,
                                long srcHost,
                                long ByteCount)

cuMemcpyHtoA

public static int cuMemcpyHtoA(long dstArray,
                               long dstOffset,
                               java.nio.ByteBuffer srcHost)

cuMemcpyHtoA

public static int cuMemcpyHtoA(long dstArray,
                               long dstOffset,
                               java.nio.ShortBuffer srcHost)

cuMemcpyHtoA

public static int cuMemcpyHtoA(long dstArray,
                               long dstOffset,
                               java.nio.IntBuffer srcHost)

cuMemcpyHtoA

public static int cuMemcpyHtoA(long dstArray,
                               long dstOffset,
                               java.nio.LongBuffer srcHost)

cuMemcpyHtoA

public static int cuMemcpyHtoA(long dstArray,
                               long dstOffset,
                               java.nio.FloatBuffer srcHost)

cuMemcpyHtoA

public static int cuMemcpyHtoA(long dstArray,
                               long dstOffset,
                               java.nio.DoubleBuffer srcHost)

cuMemcpyHtoA

public static int cuMemcpyHtoA(long dstArray,
                               long dstOffset,
                               org.lwjgl.PointerBuffer srcHost)

ncuMemcpyAtoH

public static int ncuMemcpyAtoH(long dstHost,
                                long srcArray,
                                long srcOffset,
                                long ByteCount)

cuMemcpyAtoH

public static int cuMemcpyAtoH(java.nio.ByteBuffer dstHost,
                               long srcArray,
                               long srcOffset)

cuMemcpyAtoH

public static int cuMemcpyAtoH(java.nio.ShortBuffer dstHost,
                               long srcArray,
                               long srcOffset)

cuMemcpyAtoH

public static int cuMemcpyAtoH(java.nio.IntBuffer dstHost,
                               long srcArray,
                               long srcOffset)

cuMemcpyAtoH

public static int cuMemcpyAtoH(java.nio.LongBuffer dstHost,
                               long srcArray,
                               long srcOffset)

cuMemcpyAtoH

public static int cuMemcpyAtoH(java.nio.FloatBuffer dstHost,
                               long srcArray,
                               long srcOffset)

cuMemcpyAtoH

public static int cuMemcpyAtoH(java.nio.DoubleBuffer dstHost,
                               long srcArray,
                               long srcOffset)

cuMemcpyAtoH

public static int cuMemcpyAtoH(org.lwjgl.PointerBuffer dstHost,
                               long srcArray,
                               long srcOffset)

cuMemcpyAtoA

public static int cuMemcpyAtoA(long dstArray,
                               long dstOffset,
                               long srcArray,
                               long srcOffset,
                               long ByteCount)

ncuMemcpy2D

public static int ncuMemcpy2D(long pCopy)

cuMemcpy2D

public static int cuMemcpy2D(CUDA_MEMCPY2D pCopy)

ncuMemcpy2DUnaligned

public static int ncuMemcpy2DUnaligned(long pCopy)

cuMemcpy2DUnaligned

public static int cuMemcpy2DUnaligned(CUDA_MEMCPY2D pCopy)

ncuMemcpy3D

public static int ncuMemcpy3D(long pCopy)

cuMemcpy3D

public static int cuMemcpy3D(CUDA_MEMCPY3D pCopy)

ncuMemcpyHtoDAsync

public static int ncuMemcpyHtoDAsync(long dstDevice,
                                     long srcHost,
                                     long ByteCount,
                                     long hStream)

cuMemcpyHtoDAsync

public static int cuMemcpyHtoDAsync(long dstDevice,
                                    java.nio.ByteBuffer srcHost,
                                    long hStream)

cuMemcpyHtoDAsync

public static int cuMemcpyHtoDAsync(long dstDevice,
                                    java.nio.ShortBuffer srcHost,
                                    long hStream)

cuMemcpyHtoDAsync

public static int cuMemcpyHtoDAsync(long dstDevice,
                                    java.nio.IntBuffer srcHost,
                                    long hStream)

cuMemcpyHtoDAsync

public static int cuMemcpyHtoDAsync(long dstDevice,
                                    java.nio.LongBuffer srcHost,
                                    long hStream)

cuMemcpyHtoDAsync

public static int cuMemcpyHtoDAsync(long dstDevice,
                                    java.nio.FloatBuffer srcHost,
                                    long hStream)

cuMemcpyHtoDAsync

public static int cuMemcpyHtoDAsync(long dstDevice,
                                    java.nio.DoubleBuffer srcHost,
                                    long hStream)

cuMemcpyHtoDAsync

public static int cuMemcpyHtoDAsync(long dstDevice,
                                    org.lwjgl.PointerBuffer srcHost,
                                    long hStream)

ncuMemcpyDtoHAsync

public static int ncuMemcpyDtoHAsync(long dstHost,
                                     long srcDevice,
                                     long ByteCount,
                                     long hStream)

cuMemcpyDtoHAsync

public static int cuMemcpyDtoHAsync(java.nio.ByteBuffer dstHost,
                                    long srcDevice,
                                    long hStream)

cuMemcpyDtoHAsync

public static int cuMemcpyDtoHAsync(java.nio.ShortBuffer dstHost,
                                    long srcDevice,
                                    long hStream)

cuMemcpyDtoHAsync

public static int cuMemcpyDtoHAsync(java.nio.IntBuffer dstHost,
                                    long srcDevice,
                                    long hStream)

cuMemcpyDtoHAsync

public static int cuMemcpyDtoHAsync(java.nio.LongBuffer dstHost,
                                    long srcDevice,
                                    long hStream)

cuMemcpyDtoHAsync

public static int cuMemcpyDtoHAsync(java.nio.FloatBuffer dstHost,
                                    long srcDevice,
                                    long hStream)

cuMemcpyDtoHAsync

public static int cuMemcpyDtoHAsync(java.nio.DoubleBuffer dstHost,
                                    long srcDevice,
                                    long hStream)

cuMemcpyDtoHAsync

public static int cuMemcpyDtoHAsync(org.lwjgl.PointerBuffer dstHost,
                                    long srcDevice,
                                    long hStream)

cuMemcpyDtoDAsync

public static int cuMemcpyDtoDAsync(long dstDevice,
                                    long srcDevice,
                                    long ByteCount,
                                    long hStream)

ncuMemcpyHtoAAsync

public static int ncuMemcpyHtoAAsync(long dstArray,
                                     long dstOffset,
                                     long srcHost,
                                     long ByteCount,
                                     long hStream)

cuMemcpyHtoAAsync

public static int cuMemcpyHtoAAsync(long dstArray,
                                    long dstOffset,
                                    java.nio.ByteBuffer srcHost,
                                    long hStream)

cuMemcpyHtoAAsync

public static int cuMemcpyHtoAAsync(long dstArray,
                                    long dstOffset,
                                    java.nio.ShortBuffer srcHost,
                                    long hStream)

cuMemcpyHtoAAsync

public static int cuMemcpyHtoAAsync(long dstArray,
                                    long dstOffset,
                                    java.nio.IntBuffer srcHost,
                                    long hStream)

cuMemcpyHtoAAsync

public static int cuMemcpyHtoAAsync(long dstArray,
                                    long dstOffset,
                                    java.nio.LongBuffer srcHost,
                                    long hStream)

cuMemcpyHtoAAsync

public static int cuMemcpyHtoAAsync(long dstArray,
                                    long dstOffset,
                                    java.nio.FloatBuffer srcHost,
                                    long hStream)

cuMemcpyHtoAAsync

public static int cuMemcpyHtoAAsync(long dstArray,
                                    long dstOffset,
                                    java.nio.DoubleBuffer srcHost,
                                    long hStream)

cuMemcpyHtoAAsync

public static int cuMemcpyHtoAAsync(long dstArray,
                                    long dstOffset,
                                    org.lwjgl.PointerBuffer srcHost,
                                    long hStream)

ncuMemcpyAtoHAsync

public static int ncuMemcpyAtoHAsync(long dstHost,
                                     long srcArray,
                                     long srcOffset,
                                     long ByteCount,
                                     long hStream)

cuMemcpyAtoHAsync

public static int cuMemcpyAtoHAsync(java.nio.ByteBuffer dstHost,
                                    long srcArray,
                                    long srcOffset,
                                    long hStream)

cuMemcpyAtoHAsync

public static int cuMemcpyAtoHAsync(java.nio.ShortBuffer dstHost,
                                    long srcArray,
                                    long srcOffset,
                                    long hStream)

cuMemcpyAtoHAsync

public static int cuMemcpyAtoHAsync(java.nio.IntBuffer dstHost,
                                    long srcArray,
                                    long srcOffset,
                                    long hStream)

cuMemcpyAtoHAsync

public static int cuMemcpyAtoHAsync(java.nio.LongBuffer dstHost,
                                    long srcArray,
                                    long srcOffset,
                                    long hStream)

cuMemcpyAtoHAsync

public static int cuMemcpyAtoHAsync(java.nio.FloatBuffer dstHost,
                                    long srcArray,
                                    long srcOffset,
                                    long hStream)

cuMemcpyAtoHAsync

public static int cuMemcpyAtoHAsync(java.nio.DoubleBuffer dstHost,
                                    long srcArray,
                                    long srcOffset,
                                    long hStream)

cuMemcpyAtoHAsync

public static int cuMemcpyAtoHAsync(org.lwjgl.PointerBuffer dstHost,
                                    long srcArray,
                                    long srcOffset,
                                    long hStream)

ncuMemcpy2DAsync

public static int ncuMemcpy2DAsync(long pCopy,
                                   long hStream)

cuMemcpy2DAsync

public static int cuMemcpy2DAsync(CUDA_MEMCPY2D pCopy,
                                  long hStream)

ncuMemcpy3DAsync

public static int ncuMemcpy3DAsync(long pCopy,
                                   long hStream)

cuMemcpy3DAsync

public static int cuMemcpy3DAsync(CUDA_MEMCPY3D pCopy,
                                  long hStream)

cuMemsetD8

public static int cuMemsetD8(long dstDevice,
                             byte uc,
                             long N)

cuMemsetD16

public static int cuMemsetD16(long dstDevice,
                              short us,
                              long N)

cuMemsetD32

public static int cuMemsetD32(long dstDevice,
                              int ui,
                              long N)

cuMemsetD2D8

public static int cuMemsetD2D8(long dstDevice,
                               long dstPitch,
                               byte uc,
                               long Width,
                               long Height)

cuMemsetD2D16

public static int cuMemsetD2D16(long dstDevice,
                                long dstPitch,
                                short us,
                                long Width,
                                long Height)

cuMemsetD2D32

public static int cuMemsetD2D32(long dstDevice,
                                long dstPitch,
                                int ui,
                                long Width,
                                long Height)

cuMemsetD8Async

public static int cuMemsetD8Async(long dstDevice,
                                  byte uc,
                                  long N,
                                  long hStream)

cuMemsetD16Async

public static int cuMemsetD16Async(long dstDevice,
                                   short us,
                                   long N,
                                   long hStream)

cuMemsetD32Async

public static int cuMemsetD32Async(long dstDevice,
                                   int ui,
                                   long N,
                                   long hStream)

cuMemsetD2D8Async

public static int cuMemsetD2D8Async(long dstDevice,
                                    long dstPitch,
                                    byte uc,
                                    long Width,
                                    long Height,
                                    long hStream)

cuMemsetD2D16Async

public static int cuMemsetD2D16Async(long dstDevice,
                                     long dstPitch,
                                     short us,
                                     long Width,
                                     long Height,
                                     long hStream)

cuMemsetD2D32Async

public static int cuMemsetD2D32Async(long dstDevice,
                                     long dstPitch,
                                     int ui,
                                     long Width,
                                     long Height,
                                     long hStream)

ncuArrayCreate

public static int ncuArrayCreate(long pHandle,
                                 long pAllocateArray)

cuArrayCreate

public static int cuArrayCreate(org.lwjgl.PointerBuffer pHandle,
                                CUDA_ARRAY_DESCRIPTOR pAllocateArray)

ncuArrayGetDescriptor

public static int ncuArrayGetDescriptor(long pArrayDescriptor,
                                        long hArray)

cuArrayGetDescriptor

public static int cuArrayGetDescriptor(CUDA_ARRAY_DESCRIPTOR pArrayDescriptor,
                                       long hArray)

cuArrayDestroy

public static int cuArrayDestroy(long hArray)

ncuArray3DCreate

public static int ncuArray3DCreate(long pHandle,
                                   long pAllocateArray)

cuArray3DCreate

public static int cuArray3DCreate(org.lwjgl.PointerBuffer pHandle,
                                  CUDA_ARRAY3D_DESCRIPTOR pAllocateArray)

ncuArray3DGetDescriptor

public static int ncuArray3DGetDescriptor(long pArrayDescriptor,
                                          long hArray)

cuArray3DGetDescriptor

public static int cuArray3DGetDescriptor(CUDA_ARRAY3D_DESCRIPTOR pArrayDescriptor,
                                         long hArray)

ncuStreamCreate

public static int ncuStreamCreate(long phStream,
                                  int Flags)

cuStreamCreate

public static int cuStreamCreate(org.lwjgl.PointerBuffer phStream,
                                 int Flags)

ncuStreamCreateWithPriority

public static int ncuStreamCreateWithPriority(long phStream,
                                              int flags,
                                              int priority)

cuStreamCreateWithPriority

public static int cuStreamCreateWithPriority(org.lwjgl.PointerBuffer phStream,
                                             int flags,
                                             int priority)

ncuStreamGetPriority

public static int ncuStreamGetPriority(long hStream,
                                       long priority)

cuStreamGetPriority

public static int cuStreamGetPriority(long hStream,
                                      java.nio.IntBuffer priority)

ncuStreamGetFlags

public static int ncuStreamGetFlags(long hStream,
                                    long flags)

cuStreamGetFlags

public static int cuStreamGetFlags(long hStream,
                                   java.nio.IntBuffer flags)

cuStreamWaitEvent

public static int cuStreamWaitEvent(long hStream,
                                    long hEvent,
                                    int Flags)

ncuStreamAddCallback

public static int ncuStreamAddCallback(long hStream,
                                       long callback,
                                       long userData,
                                       int flags)

cuStreamAddCallback

public static int cuStreamAddCallback(long hStream,
                                      CUstreamCallbackI callback,
                                      long userData,
                                      int flags)

cuStreamQuery

public static int cuStreamQuery(long hStream)

cuStreamSynchronize

public static int cuStreamSynchronize(long hStream)

ncuEventCreate

public static int ncuEventCreate(long phEvent,
                                 int Flags)

cuEventCreate

public static int cuEventCreate(org.lwjgl.PointerBuffer phEvent,
                                int Flags)

cuEventRecord

public static int cuEventRecord(long hEvent,
                                long hStream)

cuEventQuery

public static int cuEventQuery(long hEvent)

cuEventSynchronize

public static int cuEventSynchronize(long hEvent)

ncuEventElapsedTime

public static int ncuEventElapsedTime(long pMilliseconds,
                                      long hStart,
                                      long hEnd)

cuEventElapsedTime

public static int cuEventElapsedTime(java.nio.FloatBuffer pMilliseconds,
                                     long hStart,
                                     long hEnd)

ncuFuncGetAttribute

public static int ncuFuncGetAttribute(long pi,
                                      int attrib,
                                      long hfunc)

cuFuncGetAttribute

public static int cuFuncGetAttribute(java.nio.IntBuffer pi,
                                     int attrib,
                                     long hfunc)

cuFuncSetCacheConfig

public static int cuFuncSetCacheConfig(long hfunc,
                                       int config)

cuFuncSetBlockShape

public static int cuFuncSetBlockShape(long hfunc,
                                      int x,
                                      int y,
                                      int z)

cuFuncSetSharedSize

public static int cuFuncSetSharedSize(long hfunc,
                                      int bytes)

cuParamSetSize

public static int cuParamSetSize(long hfunc,
                                 int numbytes)

cuParamSeti

public static int cuParamSeti(long hfunc,
                              int offset,
                              int value)

cuParamSetf

public static int cuParamSetf(long hfunc,
                              int offset,
                              float value)

ncuParamSetv

public static int ncuParamSetv(long hfunc,
                               int offset,
                               long ptr,
                               int numbytes)

cuParamSetv

public static int cuParamSetv(long hfunc,
                              int offset,
                              java.nio.ByteBuffer ptr)

cuLaunch

public static int cuLaunch(long f)

cuLaunchGrid

public static int cuLaunchGrid(long f,
                               int grid_width,
                               int grid_height)

cuLaunchGridAsync

public static int cuLaunchGridAsync(long f,
                                    int grid_width,
                                    int grid_height,
                                    long hStream)

cuParamSetTexRef

public static int cuParamSetTexRef(long hfunc,
                                   int texunit,
                                   long hTexRef)

cuTexRefSetArray

public static int cuTexRefSetArray(long hTexRef,
                                   long hArray,
                                   int Flags)

cuTexRefSetMipmappedArray

public static int cuTexRefSetMipmappedArray(long hTexRef,
                                            long hMipmappedArray,
                                            int Flags)

ncuTexRefSetAddress

public static int ncuTexRefSetAddress(long ByteOffset,
                                      long hTexRef,
                                      long dptr,
                                      long bytes)

cuTexRefSetAddress

public static int cuTexRefSetAddress(@Nullable
                                     org.lwjgl.PointerBuffer ByteOffset,
                                     long hTexRef,
                                     long dptr,
                                     long bytes)

ncuTexRefSetAddress2D

public static int ncuTexRefSetAddress2D(long hTexRef,
                                        long desc,
                                        long dptr,
                                        long Pitch)

cuTexRefSetAddress2D

public static int cuTexRefSetAddress2D(long hTexRef,
                                       CUDA_ARRAY_DESCRIPTOR desc,
                                       long dptr,
                                       long Pitch)

cuTexRefSetFormat

public static int cuTexRefSetFormat(long hTexRef,
                                    int fmt,
                                    int NumPackedComponents)

cuTexRefSetAddressMode

public static int cuTexRefSetAddressMode(long hTexRef,
                                         int dim,
                                         int am)

cuTexRefSetFilterMode

public static int cuTexRefSetFilterMode(long hTexRef,
                                        int fm)

cuTexRefSetMipmapFilterMode

public static int cuTexRefSetMipmapFilterMode(long hTexRef,
                                              int fm)

cuTexRefSetMipmapLevelBias

public static int cuTexRefSetMipmapLevelBias(long hTexRef,
                                             float bias)

cuTexRefSetMipmapLevelClamp

public static int cuTexRefSetMipmapLevelClamp(long hTexRef,
                                              float minMipmapLevelClamp,
                                              float maxMipmapLevelClamp)

cuTexRefSetMaxAnisotropy

public static int cuTexRefSetMaxAnisotropy(long hTexRef,
                                           int maxAniso)

ncuTexRefSetBorderColor

public static int ncuTexRefSetBorderColor(long hTexRef,
                                          long pBorderColor)

cuTexRefSetBorderColor

public static int cuTexRefSetBorderColor(long hTexRef,
                                         java.nio.FloatBuffer pBorderColor)

cuTexRefSetFlags

public static int cuTexRefSetFlags(long hTexRef,
                                   int Flags)

ncuTexRefGetAddress

public static int ncuTexRefGetAddress(long pdptr,
                                      long hTexRef)

cuTexRefGetAddress

public static int cuTexRefGetAddress(org.lwjgl.PointerBuffer pdptr,
                                     long hTexRef)

ncuTexRefGetArray

public static int ncuTexRefGetArray(long phArray,
                                    long hTexRef)

cuTexRefGetArray

public static int cuTexRefGetArray(org.lwjgl.PointerBuffer phArray,
                                   long hTexRef)

ncuTexRefGetMipmappedArray

public static int ncuTexRefGetMipmappedArray(long phMipmappedArray,
                                             long hTexRef)

cuTexRefGetMipmappedArray

public static int cuTexRefGetMipmappedArray(org.lwjgl.PointerBuffer phMipmappedArray,
                                            long hTexRef)

ncuTexRefGetAddressMode

public static int ncuTexRefGetAddressMode(long pam,
                                          long hTexRef,
                                          int dim)

cuTexRefGetAddressMode

public static int cuTexRefGetAddressMode(java.nio.IntBuffer pam,
                                         long hTexRef,
                                         int dim)

ncuTexRefGetFilterMode

public static int ncuTexRefGetFilterMode(long pfm,
                                         long hTexRef)

cuTexRefGetFilterMode

public static int cuTexRefGetFilterMode(java.nio.IntBuffer pfm,
                                        long hTexRef)

ncuTexRefGetFormat

public static int ncuTexRefGetFormat(long pFormat,
                                     long pNumChannels,
                                     long hTexRef)

cuTexRefGetFormat

public static int cuTexRefGetFormat(@Nullable
                                    java.nio.IntBuffer pFormat,
                                    @Nullable
                                    java.nio.IntBuffer pNumChannels,
                                    long hTexRef)

ncuTexRefGetMipmapFilterMode

public static int ncuTexRefGetMipmapFilterMode(long pfm,
                                               long hTexRef)

cuTexRefGetMipmapFilterMode

public static int cuTexRefGetMipmapFilterMode(java.nio.IntBuffer pfm,
                                              long hTexRef)

ncuTexRefGetMipmapLevelBias

public static int ncuTexRefGetMipmapLevelBias(long pbias,
                                              long hTexRef)

cuTexRefGetMipmapLevelBias

public static int cuTexRefGetMipmapLevelBias(java.nio.FloatBuffer pbias,
                                             long hTexRef)

ncuTexRefGetMipmapLevelClamp

public static int ncuTexRefGetMipmapLevelClamp(long pminMipmapLevelClamp,
                                               long pmaxMipmapLevelClamp,
                                               long hTexRef)

cuTexRefGetMipmapLevelClamp

public static int cuTexRefGetMipmapLevelClamp(java.nio.FloatBuffer pminMipmapLevelClamp,
                                              java.nio.FloatBuffer pmaxMipmapLevelClamp,
                                              long hTexRef)

ncuTexRefGetMaxAnisotropy

public static int ncuTexRefGetMaxAnisotropy(long pmaxAniso,
                                            long hTexRef)

cuTexRefGetMaxAnisotropy

public static int cuTexRefGetMaxAnisotropy(java.nio.IntBuffer pmaxAniso,
                                           long hTexRef)

ncuTexRefGetBorderColor

public static int ncuTexRefGetBorderColor(long pBorderColor,
                                          long hTexRef)

cuTexRefGetBorderColor

public static int cuTexRefGetBorderColor(java.nio.FloatBuffer pBorderColor,
                                         long hTexRef)

ncuTexRefGetFlags

public static int ncuTexRefGetFlags(long pFlags,
                                    long hTexRef)

cuTexRefGetFlags

public static int cuTexRefGetFlags(java.nio.IntBuffer pFlags,
                                   long hTexRef)

ncuTexRefCreate

public static int ncuTexRefCreate(long pTexRef)

cuTexRefCreate

public static int cuTexRefCreate(org.lwjgl.PointerBuffer pTexRef)

cuTexRefDestroy

public static int cuTexRefDestroy(long hTexRef)

cuSurfRefSetArray

public static int cuSurfRefSetArray(long hSurfRef,
                                    long hArray,
                                    int Flags)

ncuSurfRefGetArray

public static int ncuSurfRefGetArray(long phArray,
                                     long hSurfRef)

cuSurfRefGetArray

public static int cuSurfRefGetArray(org.lwjgl.PointerBuffer phArray,
                                    long hSurfRef)

cuGraphicsUnregisterResource

public static int cuGraphicsUnregisterResource(long resource)

ncuGraphicsSubResourceGetMappedArray

public static int ncuGraphicsSubResourceGetMappedArray(long pArray,
                                                       long resource,
                                                       int arrayIndex,
                                                       int mipLevel)

cuGraphicsSubResourceGetMappedArray

public static int cuGraphicsSubResourceGetMappedArray(org.lwjgl.PointerBuffer pArray,
                                                      long resource,
                                                      int arrayIndex,
                                                      int mipLevel)

ncuGraphicsResourceGetMappedPointer

public static int ncuGraphicsResourceGetMappedPointer(long pDevPtr,
                                                      long pSize,
                                                      long resource)

cuGraphicsResourceGetMappedPointer

public static int cuGraphicsResourceGetMappedPointer(org.lwjgl.PointerBuffer pDevPtr,
                                                     org.lwjgl.PointerBuffer pSize,
                                                     long resource)

cuGraphicsResourceSetMapFlags

public static int cuGraphicsResourceSetMapFlags(long resource,
                                                int flags)

ncuGraphicsMapResources

public static int ncuGraphicsMapResources(int count,
                                          long resources,
                                          long hStream)

cuGraphicsMapResources

public static int cuGraphicsMapResources(org.lwjgl.PointerBuffer resources,
                                         long hStream)

ncuGraphicsUnmapResources

public static int ncuGraphicsUnmapResources(int count,
                                            long resources,
                                            long hStream)

cuGraphicsUnmapResources

public static int cuGraphicsUnmapResources(org.lwjgl.PointerBuffer resources,
                                           long hStream)

ncuGetExportTable

public static int ncuGetExportTable(long ppExportTable,
                                    long pExportTableId)

cuGetExportTable

public static int cuGetExportTable(org.lwjgl.PointerBuffer ppExportTable,
                                   CUuuid pExportTableId)