cuda¶
Data types used by CUDA driver¶
- enum cuda.cuda.CUipcMem_flags(value)¶
CUDA Ipc Mem Flags
- Member Type
int
Valid values are as follows:
- CU_IPC_MEM_LAZY_ENABLE_PEER_ACCESS¶
- enum cuda.cuda.CUmemAttach_flags(value)¶
CUDA Mem Attach Flags
- Member Type
int
Valid values are as follows:
- CU_MEM_ATTACH_GLOBAL¶
- CU_MEM_ATTACH_HOST¶
- CU_MEM_ATTACH_SINGLE¶
- enum cuda.cuda.CUctx_flags(value)¶
Context creation flags
- Member Type
int
Valid values are as follows:
- CU_CTX_SCHED_AUTO¶
- CU_CTX_SCHED_SPIN¶
- CU_CTX_SCHED_YIELD¶
- CU_CTX_SCHED_BLOCKING_SYNC¶
- CU_CTX_SCHED_MASK¶
- CU_CTX_MAP_HOST¶
- CU_CTX_LMEM_RESIZE_TO_MAX¶
- CU_CTX_FLAGS_MASK¶
- enum cuda.cuda.CUstream_flags(value)¶
Stream creation flags
- Member Type
int
Valid values are as follows:
- CU_STREAM_DEFAULT¶
- CU_STREAM_NON_BLOCKING¶
- enum cuda.cuda.CUevent_flags(value)¶
Event creation flags
- Member Type
int
Valid values are as follows:
- CU_EVENT_DEFAULT¶
- CU_EVENT_BLOCKING_SYNC¶
- CU_EVENT_DISABLE_TIMING¶
- CU_EVENT_INTERPROCESS¶
- enum cuda.cuda.CUevent_record_flags(value)¶
Event record flags
- Member Type
int
Valid values are as follows:
- CU_EVENT_RECORD_DEFAULT¶
- CU_EVENT_RECORD_EXTERNAL¶
- enum cuda.cuda.CUevent_wait_flags(value)¶
Event wait flags
- Member Type
int
Valid values are as follows:
- CU_EVENT_WAIT_DEFAULT¶
- CU_EVENT_WAIT_EXTERNAL¶
- enum cuda.cuda.CUstreamWaitValue_flags(value)¶
Flags for cuStreamWaitValue32 and cuStreamWaitValue64
- Member Type
int
Valid values are as follows:
- CU_STREAM_WAIT_VALUE_GEQ¶
- CU_STREAM_WAIT_VALUE_EQ¶
- CU_STREAM_WAIT_VALUE_AND¶
- CU_STREAM_WAIT_VALUE_NOR¶
- CU_STREAM_WAIT_VALUE_FLUSH¶
- enum cuda.cuda.CUstreamWriteValue_flags(value)¶
Flags for cuStreamWriteValue32
- Member Type
int
Valid values are as follows:
- CU_STREAM_WRITE_VALUE_DEFAULT¶
- CU_STREAM_WRITE_VALUE_NO_MEMORY_BARRIER¶
- enum cuda.cuda.CUstreamBatchMemOpType(value)¶
Operations for cuStreamBatchMemOp
- Member Type
int
Valid values are as follows:
- CU_STREAM_MEM_OP_WAIT_VALUE_32¶
- CU_STREAM_MEM_OP_WRITE_VALUE_32¶
- CU_STREAM_MEM_OP_WAIT_VALUE_64¶
- CU_STREAM_MEM_OP_WRITE_VALUE_64¶
- CU_STREAM_MEM_OP_FLUSH_REMOTE_WRITES¶
- enum cuda.cuda.CUoccupancy_flags(value)¶
Occupancy calculator flag
- Member Type
int
Valid values are as follows:
- CU_OCCUPANCY_DEFAULT¶
- CU_OCCUPANCY_DISABLE_CACHING_OVERRIDE¶
- enum cuda.cuda.CUstreamUpdateCaptureDependencies_flags(value)¶
Flags for cuStreamUpdateCaptureDependencies
- Member Type
int
Valid values are as follows:
- CU_STREAM_ADD_CAPTURE_DEPENDENCIES¶
- CU_STREAM_SET_CAPTURE_DEPENDENCIES¶
- enum cuda.cuda.CUarray_format(value)¶
Array formats
- Member Type
int
Valid values are as follows:
- CU_AD_FORMAT_UNSIGNED_INT8¶
- CU_AD_FORMAT_UNSIGNED_INT16¶
- CU_AD_FORMAT_UNSIGNED_INT32¶
- CU_AD_FORMAT_SIGNED_INT8¶
- CU_AD_FORMAT_SIGNED_INT16¶
- CU_AD_FORMAT_SIGNED_INT32¶
- CU_AD_FORMAT_HALF¶
- CU_AD_FORMAT_FLOAT¶
- CU_AD_FORMAT_NV12¶
- CU_AD_FORMAT_UNORM_INT8X1¶
- CU_AD_FORMAT_UNORM_INT8X2¶
- CU_AD_FORMAT_UNORM_INT8X4¶
- CU_AD_FORMAT_UNORM_INT16X1¶
- CU_AD_FORMAT_UNORM_INT16X2¶
- CU_AD_FORMAT_UNORM_INT16X4¶
- CU_AD_FORMAT_SNORM_INT8X1¶
- CU_AD_FORMAT_SNORM_INT8X2¶
- CU_AD_FORMAT_SNORM_INT8X4¶
- CU_AD_FORMAT_SNORM_INT16X1¶
- CU_AD_FORMAT_SNORM_INT16X2¶
- CU_AD_FORMAT_SNORM_INT16X4¶
- CU_AD_FORMAT_BC1_UNORM¶
- CU_AD_FORMAT_BC1_UNORM_SRGB¶
- CU_AD_FORMAT_BC2_UNORM¶
- CU_AD_FORMAT_BC2_UNORM_SRGB¶
- CU_AD_FORMAT_BC3_UNORM¶
- CU_AD_FORMAT_BC3_UNORM_SRGB¶
- CU_AD_FORMAT_BC4_UNORM¶
- CU_AD_FORMAT_BC4_SNORM¶
- CU_AD_FORMAT_BC5_UNORM¶
- CU_AD_FORMAT_BC5_SNORM¶
- CU_AD_FORMAT_BC6H_UF16¶
- CU_AD_FORMAT_BC6H_SF16¶
- CU_AD_FORMAT_BC7_UNORM¶
- CU_AD_FORMAT_BC7_UNORM_SRGB¶
- enum cuda.cuda.CUaddress_mode(value)¶
Texture reference addressing modes
- Member Type
int
Valid values are as follows:
- CU_TR_ADDRESS_MODE_WRAP¶
- CU_TR_ADDRESS_MODE_CLAMP¶
- CU_TR_ADDRESS_MODE_MIRROR¶
- CU_TR_ADDRESS_MODE_BORDER¶
- enum cuda.cuda.CUfilter_mode(value)¶
Texture reference filtering modes
- Member Type
int
Valid values are as follows:
- CU_TR_FILTER_MODE_POINT¶
- CU_TR_FILTER_MODE_LINEAR¶
- enum cuda.cuda.CUdevice_attribute(value)¶
Device properties
- Member Type
int
Valid values are as follows:
- CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK¶
- CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X¶
- CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y¶
- CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z¶
- CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_X¶
- CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y¶
- CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z¶
- CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK¶
- CU_DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY¶
- CU_DEVICE_ATTRIBUTE_WARP_SIZE¶
- CU_DEVICE_ATTRIBUTE_MAX_PITCH¶
- CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK¶
- CU_DEVICE_ATTRIBUTE_CLOCK_RATE¶
- CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT¶
- CU_DEVICE_ATTRIBUTE_GPU_OVERLAP¶
- CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT¶
- CU_DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT¶
- CU_DEVICE_ATTRIBUTE_INTEGRATED¶
- CU_DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY¶
- CU_DEVICE_ATTRIBUTE_COMPUTE_MODE¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS¶
- CU_DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT¶
- CU_DEVICE_ATTRIBUTE_CONCURRENT_KERNELS¶
- CU_DEVICE_ATTRIBUTE_ECC_ENABLED¶
- CU_DEVICE_ATTRIBUTE_PCI_BUS_ID¶
- CU_DEVICE_ATTRIBUTE_PCI_DEVICE_ID¶
- CU_DEVICE_ATTRIBUTE_TCC_DRIVER¶
- CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE¶
- CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH¶
- CU_DEVICE_ATTRIBUTE_L2_CACHE_SIZE¶
- CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR¶
- CU_DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT¶
- CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS¶
- CU_DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE¶
- CU_DEVICE_ATTRIBUTE_PCI_DOMAIN_ID¶
- CU_DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT¶
- CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR¶
- CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR¶
- CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH¶
- CU_DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED¶
- CU_DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED¶
- CU_DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED¶
- CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR¶
- CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR¶
- CU_DEVICE_ATTRIBUTE_MANAGED_MEMORY¶
- CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD¶
- CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID¶
- CU_DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED¶
- CU_DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO¶
- CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS¶
- CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS¶
- CU_DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED¶
- CU_DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM¶
- CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS¶
- CU_DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS¶
- CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR¶
- CU_DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH¶
- CU_DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH¶
- CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN¶
- CU_DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES¶
- CU_DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED¶
- CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES¶
- CU_DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST¶
- CU_DEVICE_ATTRIBUTE_VIRTUAL_ADDRESS_MANAGEMENT_SUPPORTED¶
- CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_POSIX_FILE_DESCRIPTOR_SUPPORTED¶
- CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_WIN32_HANDLE_SUPPORTED¶
- CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_WIN32_KMT_HANDLE_SUPPORTED¶
- CU_DEVICE_ATTRIBUTE_MAX_BLOCKS_PER_MULTIPROCESSOR¶
- CU_DEVICE_ATTRIBUTE_GENERIC_COMPRESSION_SUPPORTED¶
- CU_DEVICE_ATTRIBUTE_MAX_PERSISTING_L2_CACHE_SIZE¶
- CU_DEVICE_ATTRIBUTE_MAX_ACCESS_POLICY_WINDOW_SIZE¶
- CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_WITH_CUDA_VMM_SUPPORTED¶
- CU_DEVICE_ATTRIBUTE_RESERVED_SHARED_MEMORY_PER_BLOCK¶
- CU_DEVICE_ATTRIBUTE_SPARSE_CUDA_ARRAY_SUPPORTED¶
- CU_DEVICE_ATTRIBUTE_READ_ONLY_HOST_REGISTER_SUPPORTED¶
- CU_DEVICE_ATTRIBUTE_TIMELINE_SEMAPHORE_INTEROP_SUPPORTED¶
- CU_DEVICE_ATTRIBUTE_MEMORY_POOLS_SUPPORTED¶
- CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_SUPPORTED¶
- CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_FLUSH_WRITES_OPTIONS¶
- CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_WRITES_ORDERING¶
- CU_DEVICE_ATTRIBUTE_MEMPOOL_SUPPORTED_HANDLE_TYPES¶
- CU_DEVICE_ATTRIBUTE_DEFERRED_MAPPING_CUDA_ARRAY_SUPPORTED¶
- CU_DEVICE_ATTRIBUTE_MAX¶
- enum cuda.cuda.CUpointer_attribute(value)¶
Pointer information
- Member Type
int
Valid values are as follows:
- CU_POINTER_ATTRIBUTE_CONTEXT¶
- CU_POINTER_ATTRIBUTE_MEMORY_TYPE¶
- CU_POINTER_ATTRIBUTE_DEVICE_POINTER¶
- CU_POINTER_ATTRIBUTE_HOST_POINTER¶
- CU_POINTER_ATTRIBUTE_P2P_TOKENS¶
- CU_POINTER_ATTRIBUTE_SYNC_MEMOPS¶
- CU_POINTER_ATTRIBUTE_BUFFER_ID¶
- CU_POINTER_ATTRIBUTE_IS_MANAGED¶
- CU_POINTER_ATTRIBUTE_DEVICE_ORDINAL¶
- CU_POINTER_ATTRIBUTE_IS_LEGACY_CUDA_IPC_CAPABLE¶
- CU_POINTER_ATTRIBUTE_RANGE_START_ADDR¶
- CU_POINTER_ATTRIBUTE_RANGE_SIZE¶
- CU_POINTER_ATTRIBUTE_MAPPED¶
- CU_POINTER_ATTRIBUTE_ALLOWED_HANDLE_TYPES¶
- CU_POINTER_ATTRIBUTE_IS_GPU_DIRECT_RDMA_CAPABLE¶
- CU_POINTER_ATTRIBUTE_ACCESS_FLAGS¶
- CU_POINTER_ATTRIBUTE_MEMPOOL_HANDLE¶
- enum cuda.cuda.CUfunction_attribute(value)¶
Function properties
- Member Type
int
Valid values are as follows:
- CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK¶
- CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES¶
- CU_FUNC_ATTRIBUTE_CONST_SIZE_BYTES¶
- CU_FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES¶
- CU_FUNC_ATTRIBUTE_NUM_REGS¶
- CU_FUNC_ATTRIBUTE_PTX_VERSION¶
- CU_FUNC_ATTRIBUTE_BINARY_VERSION¶
- CU_FUNC_ATTRIBUTE_CACHE_MODE_CA¶
- CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES¶
- CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT¶
- CU_FUNC_ATTRIBUTE_MAX¶
- enum cuda.cuda.CUfunc_cache(value)¶
Function cache configurations
- Member Type
int
Valid values are as follows:
- CU_FUNC_CACHE_PREFER_NONE¶
- CU_FUNC_CACHE_PREFER_SHARED¶
- CU_FUNC_CACHE_PREFER_L1¶
- CU_FUNC_CACHE_PREFER_EQUAL¶
Shared memory configurations
- Member Type
int
Valid values are as follows:
Shared memory carveout configurations. These may be passed to cuFuncSetAttribute
- Member Type
int
Valid values are as follows:
- enum cuda.cuda.CUmemorytype(value)¶
Memory types
- Member Type
int
Valid values are as follows:
- CU_MEMORYTYPE_HOST¶
- CU_MEMORYTYPE_DEVICE¶
- CU_MEMORYTYPE_ARRAY¶
- CU_MEMORYTYPE_UNIFIED¶
- enum cuda.cuda.CUcomputemode(value)¶
Compute Modes
- Member Type
int
Valid values are as follows:
- CU_COMPUTEMODE_DEFAULT¶
- CU_COMPUTEMODE_PROHIBITED¶
- CU_COMPUTEMODE_EXCLUSIVE_PROCESS¶
- enum cuda.cuda.CUmem_advise(value)¶
Memory advise values
- Member Type
int
Valid values are as follows:
- CU_MEM_ADVISE_SET_READ_MOSTLY¶
- CU_MEM_ADVISE_UNSET_READ_MOSTLY¶
- CU_MEM_ADVISE_SET_PREFERRED_LOCATION¶
- CU_MEM_ADVISE_UNSET_PREFERRED_LOCATION¶
- CU_MEM_ADVISE_SET_ACCESSED_BY¶
- CU_MEM_ADVISE_UNSET_ACCESSED_BY¶
- enum cuda.cuda.CUmem_range_attribute(value)¶
- Member Type
int
Valid values are as follows:
- CU_MEM_RANGE_ATTRIBUTE_READ_MOSTLY¶
- CU_MEM_RANGE_ATTRIBUTE_PREFERRED_LOCATION¶
- CU_MEM_RANGE_ATTRIBUTE_ACCESSED_BY¶
- CU_MEM_RANGE_ATTRIBUTE_LAST_PREFETCH_LOCATION¶
- enum cuda.cuda.CUjit_option(value)¶
Online compiler and linker options
- Member Type
int
Valid values are as follows:
- CU_JIT_MAX_REGISTERS¶
- CU_JIT_THREADS_PER_BLOCK¶
- CU_JIT_WALL_TIME¶
- CU_JIT_INFO_LOG_BUFFER¶
- CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES¶
- CU_JIT_ERROR_LOG_BUFFER¶
- CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES¶
- CU_JIT_OPTIMIZATION_LEVEL¶
- CU_JIT_TARGET_FROM_CUCONTEXT¶
- CU_JIT_TARGET¶
- CU_JIT_FALLBACK_STRATEGY¶
- CU_JIT_GENERATE_DEBUG_INFO¶
- CU_JIT_LOG_VERBOSE¶
- CU_JIT_GENERATE_LINE_INFO¶
- CU_JIT_CACHE_MODE¶
- CU_JIT_NEW_SM3X_OPT¶
- CU_JIT_FAST_COMPILE¶
- CU_JIT_GLOBAL_SYMBOL_NAMES¶
- CU_JIT_GLOBAL_SYMBOL_ADDRESSES¶
- CU_JIT_GLOBAL_SYMBOL_COUNT¶
- CU_JIT_LTO¶
- CU_JIT_FTZ¶
- CU_JIT_PREC_DIV¶
- CU_JIT_PREC_SQRT¶
- CU_JIT_FMA¶
- CU_JIT_NUM_OPTIONS¶
- enum cuda.cuda.CUjit_target(value)¶
Online compilation targets
- Member Type
int
Valid values are as follows:
- CU_TARGET_COMPUTE_20¶
- CU_TARGET_COMPUTE_21¶
- CU_TARGET_COMPUTE_30¶
- CU_TARGET_COMPUTE_32¶
- CU_TARGET_COMPUTE_35¶
- CU_TARGET_COMPUTE_37¶
- CU_TARGET_COMPUTE_50¶
- CU_TARGET_COMPUTE_52¶
- CU_TARGET_COMPUTE_53¶
- CU_TARGET_COMPUTE_60¶
- CU_TARGET_COMPUTE_61¶
- CU_TARGET_COMPUTE_62¶
- CU_TARGET_COMPUTE_70¶
- CU_TARGET_COMPUTE_72¶
- CU_TARGET_COMPUTE_75¶
- CU_TARGET_COMPUTE_80¶
- CU_TARGET_COMPUTE_86¶
- enum cuda.cuda.CUjit_fallback(value)¶
Cubin matching fallback strategies
- Member Type
int
Valid values are as follows:
- CU_PREFER_PTX¶
- CU_PREFER_BINARY¶
- enum cuda.cuda.CUjit_cacheMode(value)¶
Caching modes for dlcm
- Member Type
int
Valid values are as follows:
- CU_JIT_CACHE_OPTION_NONE¶
- CU_JIT_CACHE_OPTION_CG¶
- CU_JIT_CACHE_OPTION_CA¶
- enum cuda.cuda.CUjitInputType(value)¶
Device code formats
- Member Type
int
Valid values are as follows:
- CU_JIT_INPUT_CUBIN¶
- CU_JIT_INPUT_PTX¶
- CU_JIT_INPUT_FATBINARY¶
- CU_JIT_INPUT_OBJECT¶
- CU_JIT_INPUT_LIBRARY¶
- CU_JIT_INPUT_NVVM¶
- CU_JIT_NUM_INPUT_TYPES¶
- enum cuda.cuda.CUgraphicsRegisterFlags(value)¶
Flags to register a graphics resource
- Member Type
int
Valid values are as follows:
- CU_GRAPHICS_REGISTER_FLAGS_NONE¶
- CU_GRAPHICS_REGISTER_FLAGS_READ_ONLY¶
- CU_GRAPHICS_REGISTER_FLAGS_WRITE_DISCARD¶
- CU_GRAPHICS_REGISTER_FLAGS_SURFACE_LDST¶
- CU_GRAPHICS_REGISTER_FLAGS_TEXTURE_GATHER¶
- enum cuda.cuda.CUgraphicsMapResourceFlags(value)¶
Flags for mapping and unmapping interop resources
- Member Type
int
Valid values are as follows:
- CU_GRAPHICS_MAP_RESOURCE_FLAGS_NONE¶
- CU_GRAPHICS_MAP_RESOURCE_FLAGS_READ_ONLY¶
- CU_GRAPHICS_MAP_RESOURCE_FLAGS_WRITE_DISCARD¶
- enum cuda.cuda.CUarray_cubemap_face(value)¶
Array indices for cube faces
- Member Type
int
Valid values are as follows:
- CU_CUBEMAP_FACE_POSITIVE_X¶
- CU_CUBEMAP_FACE_NEGATIVE_X¶
- CU_CUBEMAP_FACE_POSITIVE_Y¶
- CU_CUBEMAP_FACE_NEGATIVE_Y¶
- CU_CUBEMAP_FACE_POSITIVE_Z¶
- CU_CUBEMAP_FACE_NEGATIVE_Z¶
- enum cuda.cuda.CUlimit(value)¶
Limits
- Member Type
int
Valid values are as follows:
- CU_LIMIT_STACK_SIZE¶
- CU_LIMIT_PRINTF_FIFO_SIZE¶
- CU_LIMIT_MALLOC_HEAP_SIZE¶
- CU_LIMIT_DEV_RUNTIME_SYNC_DEPTH¶
- CU_LIMIT_DEV_RUNTIME_PENDING_LAUNCH_COUNT¶
- CU_LIMIT_MAX_L2_FETCH_GRANULARITY¶
- CU_LIMIT_PERSISTING_L2_CACHE_SIZE¶
- CU_LIMIT_MAX¶
- enum cuda.cuda.CUresourcetype(value)¶
Resource types
- Member Type
int
Valid values are as follows:
- CU_RESOURCE_TYPE_ARRAY¶
- CU_RESOURCE_TYPE_MIPMAPPED_ARRAY¶
- CU_RESOURCE_TYPE_LINEAR¶
- CU_RESOURCE_TYPE_PITCH2D¶
- enum cuda.cuda.CUaccessProperty(value)¶
Specifies performance hint with ::CUaccessPolicyWindow for hitProp and missProp members.
- Member Type
int
Valid values are as follows:
- CU_ACCESS_PROPERTY_NORMAL¶
- CU_ACCESS_PROPERTY_STREAMING¶
- CU_ACCESS_PROPERTY_PERSISTING¶
- enum cuda.cuda.CUgraphNodeType(value)¶
Graph node types
- Member Type
int
Valid values are as follows:
- CU_GRAPH_NODE_TYPE_KERNEL¶
- CU_GRAPH_NODE_TYPE_MEMCPY¶
- CU_GRAPH_NODE_TYPE_MEMSET¶
- CU_GRAPH_NODE_TYPE_HOST¶
- CU_GRAPH_NODE_TYPE_GRAPH¶
- CU_GRAPH_NODE_TYPE_EMPTY¶
- CU_GRAPH_NODE_TYPE_WAIT_EVENT¶
- CU_GRAPH_NODE_TYPE_EVENT_RECORD¶
- CU_GRAPH_NODE_TYPE_EXT_SEMAS_SIGNAL¶
- CU_GRAPH_NODE_TYPE_EXT_SEMAS_WAIT¶
- CU_GRAPH_NODE_TYPE_MEM_ALLOC¶
- CU_GRAPH_NODE_TYPE_MEM_FREE¶
- enum cuda.cuda.CUsynchronizationPolicy(value)¶
- Member Type
int
Valid values are as follows:
- CU_SYNC_POLICY_AUTO¶
- CU_SYNC_POLICY_SPIN¶
- CU_SYNC_POLICY_YIELD¶
- CU_SYNC_POLICY_BLOCKING_SYNC¶
- enum cuda.cuda.CUkernelNodeAttrID(value)¶
Graph kernel node Attributes
- Member Type
int
Valid values are as follows:
- CU_KERNEL_NODE_ATTRIBUTE_ACCESS_POLICY_WINDOW¶
- CU_KERNEL_NODE_ATTRIBUTE_COOPERATIVE¶
- enum cuda.cuda.CUstreamCaptureStatus(value)¶
Possible stream capture statuses returned by cuStreamIsCapturing
- Member Type
int
Valid values are as follows:
- CU_STREAM_CAPTURE_STATUS_NONE¶
- CU_STREAM_CAPTURE_STATUS_ACTIVE¶
- CU_STREAM_CAPTURE_STATUS_INVALIDATED¶
- enum cuda.cuda.CUstreamCaptureMode(value)¶
Possible modes for stream capture thread interactions. For more details see cuStreamBeginCapture and cuThreadExchangeStreamCaptureMode
- Member Type
int
Valid values are as follows:
- CU_STREAM_CAPTURE_MODE_GLOBAL¶
- CU_STREAM_CAPTURE_MODE_THREAD_LOCAL¶
- CU_STREAM_CAPTURE_MODE_RELAXED¶
- enum cuda.cuda.CUstreamAttrID(value)¶
Stream Attributes
- Member Type
int
Valid values are as follows:
- CU_STREAM_ATTRIBUTE_ACCESS_POLICY_WINDOW¶
- CU_STREAM_ATTRIBUTE_SYNCHRONIZATION_POLICY¶
- enum cuda.cuda.CUdriverProcAddress_flags(value)¶
Flags to specify search options. For more details see cuGetProcAddress
- Member Type
int
Valid values are as follows:
- CU_GET_PROC_ADDRESS_DEFAULT¶
- CU_GET_PROC_ADDRESS_LEGACY_STREAM¶
- CU_GET_PROC_ADDRESS_PER_THREAD_DEFAULT_STREAM¶
- enum cuda.cuda.CUexecAffinityType(value)¶
Execution Affinity Types
- Member Type
int
Valid values are as follows:
- CU_EXEC_AFFINITY_TYPE_SM_COUNT¶
- CU_EXEC_AFFINITY_TYPE_MAX¶
- enum cuda.cuda.CUresult(value)¶
Error codes
- Member Type
int
Valid values are as follows:
- CUDA_SUCCESS¶
- CUDA_ERROR_INVALID_VALUE¶
- CUDA_ERROR_OUT_OF_MEMORY¶
- CUDA_ERROR_NOT_INITIALIZED¶
- CUDA_ERROR_DEINITIALIZED¶
- CUDA_ERROR_PROFILER_DISABLED¶
- CUDA_ERROR_PROFILER_NOT_INITIALIZED¶
- CUDA_ERROR_PROFILER_ALREADY_STARTED¶
- CUDA_ERROR_PROFILER_ALREADY_STOPPED¶
- CUDA_ERROR_STUB_LIBRARY¶
- CUDA_ERROR_NO_DEVICE¶
- CUDA_ERROR_INVALID_DEVICE¶
- CUDA_ERROR_DEVICE_NOT_LICENSED¶
- CUDA_ERROR_INVALID_IMAGE¶
- CUDA_ERROR_INVALID_CONTEXT¶
- CUDA_ERROR_CONTEXT_ALREADY_CURRENT¶
- CUDA_ERROR_MAP_FAILED¶
- CUDA_ERROR_UNMAP_FAILED¶
- CUDA_ERROR_ARRAY_IS_MAPPED¶
- CUDA_ERROR_ALREADY_MAPPED¶
- CUDA_ERROR_NO_BINARY_FOR_GPU¶
- CUDA_ERROR_ALREADY_ACQUIRED¶
- CUDA_ERROR_NOT_MAPPED¶
- CUDA_ERROR_NOT_MAPPED_AS_ARRAY¶
- CUDA_ERROR_NOT_MAPPED_AS_POINTER¶
- CUDA_ERROR_ECC_UNCORRECTABLE¶
- CUDA_ERROR_UNSUPPORTED_LIMIT¶
- CUDA_ERROR_CONTEXT_ALREADY_IN_USE¶
- CUDA_ERROR_PEER_ACCESS_UNSUPPORTED¶
- CUDA_ERROR_INVALID_PTX¶
- CUDA_ERROR_INVALID_GRAPHICS_CONTEXT¶
- CUDA_ERROR_NVLINK_UNCORRECTABLE¶
- CUDA_ERROR_JIT_COMPILER_NOT_FOUND¶
- CUDA_ERROR_UNSUPPORTED_PTX_VERSION¶
- CUDA_ERROR_JIT_COMPILATION_DISABLED¶
- CUDA_ERROR_UNSUPPORTED_EXEC_AFFINITY¶
- CUDA_ERROR_INVALID_SOURCE¶
- CUDA_ERROR_FILE_NOT_FOUND¶
- CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND¶
- CUDA_ERROR_SHARED_OBJECT_INIT_FAILED¶
- CUDA_ERROR_OPERATING_SYSTEM¶
- CUDA_ERROR_INVALID_HANDLE¶
- CUDA_ERROR_ILLEGAL_STATE¶
- CUDA_ERROR_NOT_FOUND¶
- CUDA_ERROR_NOT_READY¶
- CUDA_ERROR_ILLEGAL_ADDRESS¶
- CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES¶
- CUDA_ERROR_LAUNCH_TIMEOUT¶
- CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING¶
- CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED¶
- CUDA_ERROR_PEER_ACCESS_NOT_ENABLED¶
- CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE¶
- CUDA_ERROR_CONTEXT_IS_DESTROYED¶
- CUDA_ERROR_ASSERT¶
- CUDA_ERROR_TOO_MANY_PEERS¶
- CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED¶
- CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED¶
- CUDA_ERROR_HARDWARE_STACK_ERROR¶
- CUDA_ERROR_ILLEGAL_INSTRUCTION¶
- CUDA_ERROR_MISALIGNED_ADDRESS¶
- CUDA_ERROR_INVALID_ADDRESS_SPACE¶
- CUDA_ERROR_INVALID_PC¶
- CUDA_ERROR_LAUNCH_FAILED¶
- CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE¶
- CUDA_ERROR_NOT_PERMITTED¶
- CUDA_ERROR_NOT_SUPPORTED¶
- CUDA_ERROR_SYSTEM_NOT_READY¶
- CUDA_ERROR_SYSTEM_DRIVER_MISMATCH¶
- CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE¶
- CUDA_ERROR_MPS_CONNECTION_FAILED¶
- CUDA_ERROR_MPS_RPC_FAILURE¶
- CUDA_ERROR_MPS_SERVER_NOT_READY¶
- CUDA_ERROR_MPS_MAX_CLIENTS_REACHED¶
- CUDA_ERROR_MPS_MAX_CONNECTIONS_REACHED¶
- CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED¶
- CUDA_ERROR_STREAM_CAPTURE_INVALIDATED¶
- CUDA_ERROR_STREAM_CAPTURE_MERGE¶
- CUDA_ERROR_STREAM_CAPTURE_UNMATCHED¶
- CUDA_ERROR_STREAM_CAPTURE_UNJOINED¶
- CUDA_ERROR_STREAM_CAPTURE_ISOLATION¶
- CUDA_ERROR_STREAM_CAPTURE_IMPLICIT¶
- CUDA_ERROR_CAPTURED_EVENT¶
- CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD¶
- CUDA_ERROR_TIMEOUT¶
- CUDA_ERROR_GRAPH_EXEC_UPDATE_FAILURE¶
- CUDA_ERROR_EXTERNAL_DEVICE¶
- CUDA_ERROR_UNKNOWN¶
- enum cuda.cuda.CUdevice_P2PAttribute(value)¶
P2P Attributes
- Member Type
int
Valid values are as follows:
- CU_DEVICE_P2P_ATTRIBUTE_PERFORMANCE_RANK¶
- CU_DEVICE_P2P_ATTRIBUTE_ACCESS_SUPPORTED¶
- CU_DEVICE_P2P_ATTRIBUTE_NATIVE_ATOMIC_SUPPORTED¶
- CU_DEVICE_P2P_ATTRIBUTE_ACCESS_ACCESS_SUPPORTED¶
- enum cuda.cuda.CUresourceViewFormat(value)¶
Resource view format
- Member Type
int
Valid values are as follows:
- CU_RES_VIEW_FORMAT_NONE¶
- CU_RES_VIEW_FORMAT_UINT_1X8¶
- CU_RES_VIEW_FORMAT_UINT_2X8¶
- CU_RES_VIEW_FORMAT_UINT_4X8¶
- CU_RES_VIEW_FORMAT_SINT_1X8¶
- CU_RES_VIEW_FORMAT_SINT_2X8¶
- CU_RES_VIEW_FORMAT_SINT_4X8¶
- CU_RES_VIEW_FORMAT_UINT_1X16¶
- CU_RES_VIEW_FORMAT_UINT_2X16¶
- CU_RES_VIEW_FORMAT_UINT_4X16¶
- CU_RES_VIEW_FORMAT_SINT_1X16¶
- CU_RES_VIEW_FORMAT_SINT_2X16¶
- CU_RES_VIEW_FORMAT_SINT_4X16¶
- CU_RES_VIEW_FORMAT_UINT_1X32¶
- CU_RES_VIEW_FORMAT_UINT_2X32¶
- CU_RES_VIEW_FORMAT_UINT_4X32¶
- CU_RES_VIEW_FORMAT_SINT_1X32¶
- CU_RES_VIEW_FORMAT_SINT_2X32¶
- CU_RES_VIEW_FORMAT_SINT_4X32¶
- CU_RES_VIEW_FORMAT_FLOAT_1X16¶
- CU_RES_VIEW_FORMAT_FLOAT_2X16¶
- CU_RES_VIEW_FORMAT_FLOAT_4X16¶
- CU_RES_VIEW_FORMAT_FLOAT_1X32¶
- CU_RES_VIEW_FORMAT_FLOAT_2X32¶
- CU_RES_VIEW_FORMAT_FLOAT_4X32¶
- CU_RES_VIEW_FORMAT_UNSIGNED_BC1¶
- CU_RES_VIEW_FORMAT_UNSIGNED_BC2¶
- CU_RES_VIEW_FORMAT_UNSIGNED_BC3¶
- CU_RES_VIEW_FORMAT_UNSIGNED_BC4¶
- CU_RES_VIEW_FORMAT_SIGNED_BC4¶
- CU_RES_VIEW_FORMAT_UNSIGNED_BC5¶
- CU_RES_VIEW_FORMAT_SIGNED_BC5¶
- CU_RES_VIEW_FORMAT_UNSIGNED_BC6H¶
- CU_RES_VIEW_FORMAT_SIGNED_BC6H¶
- CU_RES_VIEW_FORMAT_UNSIGNED_BC7¶
- enum cuda.cuda.CUDA_POINTER_ATTRIBUTE_ACCESS_FLAGS(value)¶
Access flags that specify the level of access the current context’s device has on the memory referenced.
- Member Type
int
Valid values are as follows:
- CU_POINTER_ATTRIBUTE_ACCESS_FLAG_NONE¶
- CU_POINTER_ATTRIBUTE_ACCESS_FLAG_READ¶
- CU_POINTER_ATTRIBUTE_ACCESS_FLAG_READWRITE¶
- enum cuda.cuda.CUexternalMemoryHandleType(value)¶
External memory handle types
- Member Type
int
Valid values are as follows:
- CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_FD¶
- CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32¶
- CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32_KMT¶
- CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_HEAP¶
- CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_RESOURCE¶
- CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE¶
- CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE_KMT¶
- CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF¶
- enum cuda.cuda.CUexternalSemaphoreHandleType(value)¶
External semaphore handle types
- Member Type
int
Valid values are as follows:
- CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_FD¶
- CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32¶
- CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32_KMT¶
- CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D12_FENCE¶
- CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_FENCE¶
- CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC¶
- CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX¶
- CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX_KMT¶
- CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_FD¶
- CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_WIN32¶
- enum cuda.cuda.CUmemAllocationHandleType(value)¶
Flags for specifying particular handle types
- Member Type
int
Valid values are as follows:
- CU_MEM_HANDLE_TYPE_NONE¶
- CU_MEM_HANDLE_TYPE_POSIX_FILE_DESCRIPTOR¶
- CU_MEM_HANDLE_TYPE_WIN32¶
- CU_MEM_HANDLE_TYPE_WIN32_KMT¶
- CU_MEM_HANDLE_TYPE_MAX¶
- enum cuda.cuda.CUmemAccess_flags(value)¶
Specifies the memory protection flags for mapping.
- Member Type
int
Valid values are as follows:
- CU_MEM_ACCESS_FLAGS_PROT_NONE¶
- CU_MEM_ACCESS_FLAGS_PROT_READ¶
- CU_MEM_ACCESS_FLAGS_PROT_READWRITE¶
- CU_MEM_ACCESS_FLAGS_PROT_MAX¶
- enum cuda.cuda.CUmemLocationType(value)¶
Specifies the type of location
- Member Type
int
Valid values are as follows:
- CU_MEM_LOCATION_TYPE_INVALID¶
- CU_MEM_LOCATION_TYPE_DEVICE¶
- CU_MEM_LOCATION_TYPE_MAX¶
- enum cuda.cuda.CUmemAllocationType(value)¶
Defines the allocation types available
- Member Type
int
Valid values are as follows:
- CU_MEM_ALLOCATION_TYPE_INVALID¶
- CU_MEM_ALLOCATION_TYPE_PINNED¶
- CU_MEM_ALLOCATION_TYPE_MAX¶
- enum cuda.cuda.CUmemAllocationGranularity_flags(value)¶
Flag for requesting different optimal and required granularities for an allocation.
- Member Type
int
Valid values are as follows:
- CU_MEM_ALLOC_GRANULARITY_MINIMUM¶
- CU_MEM_ALLOC_GRANULARITY_RECOMMENDED¶
- enum cuda.cuda.CUarraySparseSubresourceType(value)¶
Sparse subresource types
- Member Type
int
Valid values are as follows:
- CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_SPARSE_LEVEL¶
- CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_MIPTAIL¶
- enum cuda.cuda.CUmemOperationType(value)¶
Memory operation types
- Member Type
int
Valid values are as follows:
- CU_MEM_OPERATION_TYPE_MAP¶
- CU_MEM_OPERATION_TYPE_UNMAP¶
- enum cuda.cuda.CUmemHandleType(value)¶
Memory handle types
- Member Type
int
Valid values are as follows:
- CU_MEM_HANDLE_TYPE_GENERIC¶
- enum cuda.cuda.CUmemAllocationCompType(value)¶
Specifies compression attribute for an allocation.
- Member Type
int
Valid values are as follows:
- CU_MEM_ALLOCATION_COMP_NONE¶
- CU_MEM_ALLOCATION_COMP_GENERIC¶
- enum cuda.cuda.CUgraphExecUpdateResult(value)¶
- Member Type
int
Valid values are as follows:
- CU_GRAPH_EXEC_UPDATE_SUCCESS¶
- CU_GRAPH_EXEC_UPDATE_ERROR¶
- CU_GRAPH_EXEC_UPDATE_ERROR_TOPOLOGY_CHANGED¶
- CU_GRAPH_EXEC_UPDATE_ERROR_NODE_TYPE_CHANGED¶
- CU_GRAPH_EXEC_UPDATE_ERROR_FUNCTION_CHANGED¶
- CU_GRAPH_EXEC_UPDATE_ERROR_PARAMETERS_CHANGED¶
- CU_GRAPH_EXEC_UPDATE_ERROR_NOT_SUPPORTED¶
- CU_GRAPH_EXEC_UPDATE_ERROR_UNSUPPORTED_FUNCTION_CHANGE¶
- CU_GRAPH_EXEC_UPDATE_ERROR_ATTRIBUTES_CHANGED¶
- enum cuda.cuda.CUmemPool_attribute(value)¶
CUDA memory pool attributes
- Member Type
int
Valid values are as follows:
- CU_MEMPOOL_ATTR_REUSE_FOLLOW_EVENT_DEPENDENCIES¶
- CU_MEMPOOL_ATTR_REUSE_ALLOW_OPPORTUNISTIC¶
- CU_MEMPOOL_ATTR_REUSE_ALLOW_INTERNAL_DEPENDENCIES¶
- CU_MEMPOOL_ATTR_RELEASE_THRESHOLD¶
- CU_MEMPOOL_ATTR_RESERVED_MEM_CURRENT¶
- CU_MEMPOOL_ATTR_RESERVED_MEM_HIGH¶
- CU_MEMPOOL_ATTR_USED_MEM_CURRENT¶
- CU_MEMPOOL_ATTR_USED_MEM_HIGH¶
- enum cuda.cuda.CUgraphMem_attribute(value)¶
- Member Type
int
Valid values are as follows:
- CU_GRAPH_MEM_ATTR_USED_MEM_CURRENT¶
- CU_GRAPH_MEM_ATTR_USED_MEM_HIGH¶
- CU_GRAPH_MEM_ATTR_RESERVED_MEM_CURRENT¶
- CU_GRAPH_MEM_ATTR_RESERVED_MEM_HIGH¶
- enum cuda.cuda.CUflushGPUDirectRDMAWritesOptions(value)¶
Bitmasks for CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_FLUSH_WRITES_OPTIONS
- Member Type
int
Valid values are as follows:
- CU_FLUSH_GPU_DIRECT_RDMA_WRITES_OPTION_HOST¶
- CU_FLUSH_GPU_DIRECT_RDMA_WRITES_OPTION_MEMOPS¶
- enum cuda.cuda.CUGPUDirectRDMAWritesOrdering(value)¶
Platform native ordering for GPUDirect RDMA writes
- Member Type
int
Valid values are as follows:
- CU_GPU_DIRECT_RDMA_WRITES_ORDERING_NONE¶
- CU_GPU_DIRECT_RDMA_WRITES_ORDERING_OWNER¶
- CU_GPU_DIRECT_RDMA_WRITES_ORDERING_ALL_DEVICES¶
- enum cuda.cuda.CUflushGPUDirectRDMAWritesScope(value)¶
The scopes for cuFlushGPUDirectRDMAWrites
- Member Type
int
Valid values are as follows:
- CU_FLUSH_GPU_DIRECT_RDMA_WRITES_TO_OWNER¶
- CU_FLUSH_GPU_DIRECT_RDMA_WRITES_TO_ALL_DEVICES¶
- enum cuda.cuda.CUflushGPUDirectRDMAWritesTarget(value)¶
The targets for cuFlushGPUDirectRDMAWrites
- Member Type
int
Valid values are as follows:
- CU_FLUSH_GPU_DIRECT_RDMA_WRITES_TARGET_CURRENT_CTX¶
- enum cuda.cuda.CUgraphDebugDot_flags(value)¶
The additional write options for cuGraphDebugDotPrint
- Member Type
int
Valid values are as follows:
- CU_GRAPH_DEBUG_DOT_FLAGS_VERBOSE¶
- CU_GRAPH_DEBUG_DOT_FLAGS_RUNTIME_TYPES¶
- CU_GRAPH_DEBUG_DOT_FLAGS_KERNEL_NODE_PARAMS¶
- CU_GRAPH_DEBUG_DOT_FLAGS_MEMCPY_NODE_PARAMS¶
- CU_GRAPH_DEBUG_DOT_FLAGS_MEMSET_NODE_PARAMS¶
- CU_GRAPH_DEBUG_DOT_FLAGS_HOST_NODE_PARAMS¶
- CU_GRAPH_DEBUG_DOT_FLAGS_EVENT_NODE_PARAMS¶
- CU_GRAPH_DEBUG_DOT_FLAGS_EXT_SEMAS_SIGNAL_NODE_PARAMS¶
- CU_GRAPH_DEBUG_DOT_FLAGS_EXT_SEMAS_WAIT_NODE_PARAMS¶
- CU_GRAPH_DEBUG_DOT_FLAGS_KERNEL_NODE_ATTRIBUTES¶
- CU_GRAPH_DEBUG_DOT_FLAGS_HANDLES¶
- CU_GRAPH_DEBUG_DOT_FLAGS_MEM_ALLOC_NODE_PARAMS¶
- CU_GRAPH_DEBUG_DOT_FLAGS_MEM_FREE_NODE_PARAMS¶
- enum cuda.cuda.CUuserObject_flags(value)¶
Flags for user objects for graphs
- Member Type
int
Valid values are as follows:
- CU_USER_OBJECT_NO_DESTRUCTOR_SYNC¶
- enum cuda.cuda.CUuserObjectRetain_flags(value)¶
Flags for retaining user object references for graphs
- Member Type
int
Valid values are as follows:
- CU_GRAPH_USER_OBJECT_MOVE¶
- enum cuda.cuda.CUgraphInstantiate_flags(value)¶
Flags for instantiating a graph
- Member Type
int
Valid values are as follows:
- CUDA_GRAPH_INSTANTIATE_FLAG_AUTO_FREE_ON_LAUNCH¶
- enum cuda.cuda.CUeglFrameType(value)¶
CUDA EglFrame type - array or pointer
- Member Type
int
Valid values are as follows:
- CU_EGL_FRAME_TYPE_ARRAY¶
- CU_EGL_FRAME_TYPE_PITCH¶
- enum cuda.cuda.CUeglResourceLocationFlags(value)¶
Resource location flags- sysmem or vidmem For CUDA context on iGPU, since video and system memory are equivalent - these flags will not have an effect on the execution. For CUDA context on dGPU, applications can use the flag CUeglResourceLocationFlags to give a hint about the desired location. CU_EGL_RESOURCE_LOCATION_SYSMEM - the frame data is made resident on the system memory to be accessed by CUDA. CU_EGL_RESOURCE_LOCATION_VIDMEM - the frame data is made resident on the dedicated video memory to be accessed by CUDA. There may be an additional latency due to new allocation and data migration, if the frame is produced on a different memory.
- Member Type
int
Valid values are as follows:
- CU_EGL_RESOURCE_LOCATION_SYSMEM¶
- CU_EGL_RESOURCE_LOCATION_VIDMEM¶
- enum cuda.cuda.CUeglColorFormat(value)¶
CUDA EGL Color Format - The different planar and multiplanar formats currently supported for CUDA_EGL interops. Three channel formats are currently not supported for CU_EGL_FRAME_TYPE_ARRAY
- Member Type
int
Valid values are as follows:
- CU_EGL_COLOR_FORMAT_YUV420_PLANAR¶
- CU_EGL_COLOR_FORMAT_YUV420_SEMIPLANAR¶
- CU_EGL_COLOR_FORMAT_YUV422_PLANAR¶
- CU_EGL_COLOR_FORMAT_YUV422_SEMIPLANAR¶
- CU_EGL_COLOR_FORMAT_RGB¶
- CU_EGL_COLOR_FORMAT_BGR¶
- CU_EGL_COLOR_FORMAT_ARGB¶
- CU_EGL_COLOR_FORMAT_RGBA¶
- CU_EGL_COLOR_FORMAT_L¶
- CU_EGL_COLOR_FORMAT_R¶
- CU_EGL_COLOR_FORMAT_YUV444_PLANAR¶
- CU_EGL_COLOR_FORMAT_YUV444_SEMIPLANAR¶
- CU_EGL_COLOR_FORMAT_YUYV_422¶
- CU_EGL_COLOR_FORMAT_UYVY_422¶
- CU_EGL_COLOR_FORMAT_ABGR¶
- CU_EGL_COLOR_FORMAT_BGRA¶
- CU_EGL_COLOR_FORMAT_A¶
- CU_EGL_COLOR_FORMAT_RG¶
- CU_EGL_COLOR_FORMAT_AYUV¶
- CU_EGL_COLOR_FORMAT_YVU444_SEMIPLANAR¶
- CU_EGL_COLOR_FORMAT_YVU422_SEMIPLANAR¶
- CU_EGL_COLOR_FORMAT_YVU420_SEMIPLANAR¶
- CU_EGL_COLOR_FORMAT_Y10V10U10_444_SEMIPLANAR¶
- CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR¶
- CU_EGL_COLOR_FORMAT_Y12V12U12_444_SEMIPLANAR¶
- CU_EGL_COLOR_FORMAT_Y12V12U12_420_SEMIPLANAR¶
- CU_EGL_COLOR_FORMAT_VYUY_ER¶
- CU_EGL_COLOR_FORMAT_UYVY_ER¶
- CU_EGL_COLOR_FORMAT_YUYV_ER¶
- CU_EGL_COLOR_FORMAT_YVYU_ER¶
- CU_EGL_COLOR_FORMAT_YUV_ER¶
- CU_EGL_COLOR_FORMAT_YUVA_ER¶
- CU_EGL_COLOR_FORMAT_AYUV_ER¶
- CU_EGL_COLOR_FORMAT_YUV444_PLANAR_ER¶
- CU_EGL_COLOR_FORMAT_YUV422_PLANAR_ER¶
- CU_EGL_COLOR_FORMAT_YUV420_PLANAR_ER¶
- CU_EGL_COLOR_FORMAT_YUV444_SEMIPLANAR_ER¶
- CU_EGL_COLOR_FORMAT_YUV422_SEMIPLANAR_ER¶
- CU_EGL_COLOR_FORMAT_YUV420_SEMIPLANAR_ER¶
- CU_EGL_COLOR_FORMAT_YVU444_PLANAR_ER¶
- CU_EGL_COLOR_FORMAT_YVU422_PLANAR_ER¶
- CU_EGL_COLOR_FORMAT_YVU420_PLANAR_ER¶
- CU_EGL_COLOR_FORMAT_YVU444_SEMIPLANAR_ER¶
- CU_EGL_COLOR_FORMAT_YVU422_SEMIPLANAR_ER¶
- CU_EGL_COLOR_FORMAT_YVU420_SEMIPLANAR_ER¶
- CU_EGL_COLOR_FORMAT_BAYER_RGGB¶
- CU_EGL_COLOR_FORMAT_BAYER_BGGR¶
- CU_EGL_COLOR_FORMAT_BAYER_GRBG¶
- CU_EGL_COLOR_FORMAT_BAYER_GBRG¶
- CU_EGL_COLOR_FORMAT_BAYER10_RGGB¶
- CU_EGL_COLOR_FORMAT_BAYER10_BGGR¶
- CU_EGL_COLOR_FORMAT_BAYER10_GRBG¶
- CU_EGL_COLOR_FORMAT_BAYER10_GBRG¶
- CU_EGL_COLOR_FORMAT_BAYER12_RGGB¶
- CU_EGL_COLOR_FORMAT_BAYER12_BGGR¶
- CU_EGL_COLOR_FORMAT_BAYER12_GRBG¶
- CU_EGL_COLOR_FORMAT_BAYER12_GBRG¶
- CU_EGL_COLOR_FORMAT_BAYER14_RGGB¶
- CU_EGL_COLOR_FORMAT_BAYER14_BGGR¶
- CU_EGL_COLOR_FORMAT_BAYER14_GRBG¶
- CU_EGL_COLOR_FORMAT_BAYER14_GBRG¶
- CU_EGL_COLOR_FORMAT_BAYER20_RGGB¶
- CU_EGL_COLOR_FORMAT_BAYER20_BGGR¶
- CU_EGL_COLOR_FORMAT_BAYER20_GRBG¶
- CU_EGL_COLOR_FORMAT_BAYER20_GBRG¶
- CU_EGL_COLOR_FORMAT_YVU444_PLANAR¶
- CU_EGL_COLOR_FORMAT_YVU422_PLANAR¶
- CU_EGL_COLOR_FORMAT_YVU420_PLANAR¶
- CU_EGL_COLOR_FORMAT_BAYER_ISP_RGGB¶
- CU_EGL_COLOR_FORMAT_BAYER_ISP_BGGR¶
- CU_EGL_COLOR_FORMAT_BAYER_ISP_GRBG¶
- CU_EGL_COLOR_FORMAT_BAYER_ISP_GBRG¶
- CU_EGL_COLOR_FORMAT_BAYER_BCCR¶
- CU_EGL_COLOR_FORMAT_BAYER_RCCB¶
- CU_EGL_COLOR_FORMAT_BAYER_CRBC¶
- CU_EGL_COLOR_FORMAT_BAYER_CBRC¶
- CU_EGL_COLOR_FORMAT_BAYER10_CCCC¶
- CU_EGL_COLOR_FORMAT_BAYER12_BCCR¶
- CU_EGL_COLOR_FORMAT_BAYER12_RCCB¶
- CU_EGL_COLOR_FORMAT_BAYER12_CRBC¶
- CU_EGL_COLOR_FORMAT_BAYER12_CBRC¶
- CU_EGL_COLOR_FORMAT_BAYER12_CCCC¶
- CU_EGL_COLOR_FORMAT_Y¶
- CU_EGL_COLOR_FORMAT_YUV420_SEMIPLANAR_2020¶
- CU_EGL_COLOR_FORMAT_YVU420_SEMIPLANAR_2020¶
- CU_EGL_COLOR_FORMAT_YUV420_PLANAR_2020¶
- CU_EGL_COLOR_FORMAT_YVU420_PLANAR_2020¶
- CU_EGL_COLOR_FORMAT_YUV420_SEMIPLANAR_709¶
- CU_EGL_COLOR_FORMAT_YVU420_SEMIPLANAR_709¶
- CU_EGL_COLOR_FORMAT_YUV420_PLANAR_709¶
- CU_EGL_COLOR_FORMAT_YVU420_PLANAR_709¶
- CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_709¶
- CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_2020¶
- CU_EGL_COLOR_FORMAT_Y10V10U10_422_SEMIPLANAR_2020¶
- CU_EGL_COLOR_FORMAT_Y10V10U10_422_SEMIPLANAR¶
- CU_EGL_COLOR_FORMAT_Y10V10U10_422_SEMIPLANAR_709¶
- CU_EGL_COLOR_FORMAT_Y_ER¶
- CU_EGL_COLOR_FORMAT_Y_709_ER¶
- CU_EGL_COLOR_FORMAT_Y10_ER¶
- CU_EGL_COLOR_FORMAT_Y10_709_ER¶
- CU_EGL_COLOR_FORMAT_Y12_ER¶
- CU_EGL_COLOR_FORMAT_Y12_709_ER¶
- CU_EGL_COLOR_FORMAT_YUVA¶
- CU_EGL_COLOR_FORMAT_YUV¶
- CU_EGL_COLOR_FORMAT_YVYU¶
- CU_EGL_COLOR_FORMAT_VYUY¶
- CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_ER¶
- CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_709_ER¶
- CU_EGL_COLOR_FORMAT_Y10V10U10_444_SEMIPLANAR_ER¶
- CU_EGL_COLOR_FORMAT_Y10V10U10_444_SEMIPLANAR_709_ER¶
- CU_EGL_COLOR_FORMAT_Y12V12U12_420_SEMIPLANAR_ER¶
- CU_EGL_COLOR_FORMAT_Y12V12U12_420_SEMIPLANAR_709_ER¶
- CU_EGL_COLOR_FORMAT_Y12V12U12_444_SEMIPLANAR_ER¶
- CU_EGL_COLOR_FORMAT_Y12V12U12_444_SEMIPLANAR_709_ER¶
- CU_EGL_COLOR_FORMAT_MAX¶
- class cuda.cuda.CUdeviceptr_v2¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUdeviceptr¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUdevice_v1¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUdevice¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUcontext(*args, **kwargs)¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUmodule(*args, **kwargs)¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUfunction(*args, **kwargs)¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUarray(*args, **kwargs)¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUmipmappedArray(*args, **kwargs)¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUtexref(*args, **kwargs)¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUsurfref(*args, **kwargs)¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUevent(*args, **kwargs)¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUstream(*args, **kwargs)¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUgraphicsResource(*args, **kwargs)¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUtexObject_v1¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUtexObject¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUsurfObject_v1¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUsurfObject¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUexternalMemory(*args, **kwargs)¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUexternalSemaphore(*args, **kwargs)¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUgraph(*args, **kwargs)¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUgraphNode(*args, **kwargs)¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUgraphExec(*args, **kwargs)¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUmemoryPool(*args, **kwargs)¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUuserObject(*args, **kwargs)¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUuuid¶
- Attributes
- bytesbytes
< CUDA definition of UUID
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUipcEventHandle_v1¶
CUDA IPC event handle
- Attributes
- reservedbytes
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUipcEventHandle¶
CUDA IPC event handle
- Attributes
- reservedbytes
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUipcMemHandle_v1¶
CUDA IPC mem handle
- Attributes
- reservedbytes
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUipcMemHandle¶
CUDA IPC mem handle
- Attributes
- reservedbytes
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUstreamBatchMemOpParams_v1¶
- Attributes
- operationCUstreamBatchMemOpType
- waitValueCUstreamMemOpWaitValueParams_st
- writeValueCUstreamMemOpWriteValueParams_st
- flushRemoteWritesCUstreamMemOpFlushRemoteWritesParams_st
- padList[cuuint64_t]
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUstreamBatchMemOpParams¶
- Attributes
- operationCUstreamBatchMemOpType
- waitValueCUstreamMemOpWaitValueParams_st
- writeValueCUstreamMemOpWriteValueParams_st
- flushRemoteWritesCUstreamMemOpFlushRemoteWritesParams_st
- padList[cuuint64_t]
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUdevprop_v1¶
Legacy device properties
- Attributes
- maxThreadsPerBlockint
Maximum number of threads per block
- maxThreadsDimList[int]
Maximum size of each dimension of a block
- maxGridSizeList[int]
Maximum size of each dimension of a grid
- sharedMemPerBlockint
Shared memory available per block in bytes
- totalConstantMemoryint
Constant memory available on device in bytes
- SIMDWidthint
Warp size in threads
- memPitchint
Maximum pitch in bytes allowed by memory copies
- regsPerBlockint
32-bit registers available per block
- clockRateint
Clock frequency in kilohertz
- textureAlignint
Alignment requirement for textures
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUdevprop¶
Legacy device properties
- Attributes
- maxThreadsPerBlockint
Maximum number of threads per block
- maxThreadsDimList[int]
Maximum size of each dimension of a block
- maxGridSizeList[int]
Maximum size of each dimension of a grid
- sharedMemPerBlockint
Shared memory available per block in bytes
- totalConstantMemoryint
Constant memory available on device in bytes
- SIMDWidthint
Warp size in threads
- memPitchint
Maximum pitch in bytes allowed by memory copies
- regsPerBlockint
32-bit registers available per block
- clockRateint
Clock frequency in kilohertz
- textureAlignint
Alignment requirement for textures
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUlinkState(*args, **kwargs)¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUhostFn(*args, **kwargs)¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUaccessPolicyWindow_v1¶
Specifies an access policy for a window, a contiguous extent of memory beginning at base_ptr and ending at base_ptr + num_bytes. num_bytes is limited by CU_DEVICE_ATTRIBUTE_MAX_ACCESS_POLICY_WINDOW_SIZE. Partition into many segments and assign segments such that: sum of “hit segments” / window == approx. ratio. sum of “miss segments” / window == approx 1-ratio. Segments and ratio specifications are fitted to the capabilities of the architecture. Accesses in a hit segment apply the hitProp access policy. Accesses in a miss segment apply the missProp access policy.
- Attributes
- base_ptrAny
Starting address of the access policy window. CUDA driver may align it.
- num_bytessize_t
Size in bytes of the window policy. CUDA driver may restrict the maximum size and alignment.
- hitRatiofloat
hitRatio specifies percentage of lines assigned hitProp, rest are assigned missProp.
- hitPropCUaccessProperty
CUaccessProperty set for hit.
- missPropCUaccessProperty
CUaccessProperty set for miss. Must be either NORMAL or STREAMING
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUaccessPolicyWindow¶
Specifies an access policy for a window, a contiguous extent of memory beginning at base_ptr and ending at base_ptr + num_bytes. num_bytes is limited by CU_DEVICE_ATTRIBUTE_MAX_ACCESS_POLICY_WINDOW_SIZE. Partition into many segments and assign segments such that: sum of “hit segments” / window == approx. ratio. sum of “miss segments” / window == approx 1-ratio. Segments and ratio specifications are fitted to the capabilities of the architecture. Accesses in a hit segment apply the hitProp access policy. Accesses in a miss segment apply the missProp access policy.
- Attributes
- base_ptrAny
Starting address of the access policy window. CUDA driver may align it.
- num_bytessize_t
Size in bytes of the window policy. CUDA driver may restrict the maximum size and alignment.
- hitRatiofloat
hitRatio specifies percentage of lines assigned hitProp, rest are assigned missProp.
- hitPropCUaccessProperty
CUaccessProperty set for hit.
- missPropCUaccessProperty
CUaccessProperty set for miss. Must be either NORMAL or STREAMING
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_KERNEL_NODE_PARAMS_v1¶
GPU kernel node parameters
- Attributes
- funcCUfunction
Kernel to launch
- gridDimXunsigned int
Width of grid in blocks
- gridDimYunsigned int
Height of grid in blocks
- gridDimZunsigned int
Depth of grid in blocks
- blockDimXunsigned int
X dimension of each thread block
- blockDimYunsigned int
Y dimension of each thread block
- blockDimZunsigned int
Z dimension of each thread block
- sharedMemBytesunsigned int
Dynamic shared-memory size per thread block in bytes
- kernelParamsAny
Array of pointers to kernel parameters
- extraAny
Extra options
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_KERNEL_NODE_PARAMS¶
GPU kernel node parameters
- Attributes
- funcCUfunction
Kernel to launch
- gridDimXunsigned int
Width of grid in blocks
- gridDimYunsigned int
Height of grid in blocks
- gridDimZunsigned int
Depth of grid in blocks
- blockDimXunsigned int
X dimension of each thread block
- blockDimYunsigned int
Y dimension of each thread block
- blockDimZunsigned int
Z dimension of each thread block
- sharedMemBytesunsigned int
Dynamic shared-memory size per thread block in bytes
- kernelParamsAny
Array of pointers to kernel parameters
- extraAny
Extra options
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_MEMSET_NODE_PARAMS_v1¶
Memset node parameters
- Attributes
- dstCUdeviceptr
Destination device pointer
- pitchsize_t
Pitch of destination device pointer. Unused if height is 1
- valueunsigned int
Value to be set
- elementSizeunsigned int
Size of each element in bytes. Must be 1, 2, or 4.
- widthsize_t
Width of the row in elements
- heightsize_t
Number of rows
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_MEMSET_NODE_PARAMS¶
Memset node parameters
- Attributes
- dstCUdeviceptr
Destination device pointer
- pitchsize_t
Pitch of destination device pointer. Unused if height is 1
- valueunsigned int
Value to be set
- elementSizeunsigned int
Size of each element in bytes. Must be 1, 2, or 4.
- widthsize_t
Width of the row in elements
- heightsize_t
Number of rows
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_HOST_NODE_PARAMS_v1¶
Host node parameters
- Attributes
- fnCUhostFn
The function to call when the node executes
- userDataAny
Argument to pass to the function
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_HOST_NODE_PARAMS¶
Host node parameters
- Attributes
- fnCUhostFn
The function to call when the node executes
- userDataAny
Argument to pass to the function
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUkernelNodeAttrValue_v1¶
- Attributes
- accessPolicyWindowCUaccessPolicyWindow
- cooperativeint
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUkernelNodeAttrValue¶
- Attributes
- accessPolicyWindowCUaccessPolicyWindow
- cooperativeint
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUstreamAttrValue_v1¶
- Attributes
- accessPolicyWindowCUaccessPolicyWindow
- syncPolicyCUsynchronizationPolicy
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUstreamAttrValue¶
- Attributes
- accessPolicyWindowCUaccessPolicyWindow
- syncPolicyCUsynchronizationPolicy
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUexecAffinitySmCount_v1¶
Value for CU_EXEC_AFFINITY_TYPE_SM_COUNT
- Attributes
- valunsigned int
The number of SMs the context is limited to use.
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUexecAffinitySmCount¶
Value for CU_EXEC_AFFINITY_TYPE_SM_COUNT
- Attributes
- valunsigned int
The number of SMs the context is limited to use.
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUexecAffinityParam_v1¶
Execution Affinity Parameters
- Attributes
- typeCUexecAffinityType
- param_CUexecAffinityParam_v1_CUexecAffinityParam_v1_CUexecAffinityParam_st_param_u
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUexecAffinityParam¶
Execution Affinity Parameters
- Attributes
- typeCUexecAffinityType
- param_CUexecAffinityParam_v1_CUexecAffinityParam_v1_CUexecAffinityParam_st_param_u
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUstreamCallback(*args, **kwargs)¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUoccupancyB2DSize(*args, **kwargs)¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_MEMCPY2D_v2¶
2D memory copy parameters
- Attributes
- srcXInBytessize_t
Source X in bytes
- srcYsize_t
Source Y
- srcMemoryTypeCUmemorytype
Source memory type (host, device, array)
- srcHostAny
Source host pointer
- srcDeviceCUdeviceptr
Source device pointer
- srcArrayCUarray
Source array reference
- srcPitchsize_t
Source pitch (ignored when src is array)
- dstXInBytessize_t
Destination X in bytes
- dstYsize_t
Destination Y
- dstMemoryTypeCUmemorytype
Destination memory type (host, device, array)
- dstHostAny
Destination host pointer
- dstDeviceCUdeviceptr
Destination device pointer
- dstArrayCUarray
Destination array reference
- dstPitchsize_t
Destination pitch (ignored when dst is array)
- WidthInBytessize_t
Width of 2D memory copy in bytes
- Heightsize_t
Height of 2D memory copy
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_MEMCPY2D¶
2D memory copy parameters
- Attributes
- srcXInBytessize_t
Source X in bytes
- srcYsize_t
Source Y
- srcMemoryTypeCUmemorytype
Source memory type (host, device, array)
- srcHostAny
Source host pointer
- srcDeviceCUdeviceptr
Source device pointer
- srcArrayCUarray
Source array reference
- srcPitchsize_t
Source pitch (ignored when src is array)
- dstXInBytessize_t
Destination X in bytes
- dstYsize_t
Destination Y
- dstMemoryTypeCUmemorytype
Destination memory type (host, device, array)
- dstHostAny
Destination host pointer
- dstDeviceCUdeviceptr
Destination device pointer
- dstArrayCUarray
Destination array reference
- dstPitchsize_t
Destination pitch (ignored when dst is array)
- WidthInBytessize_t
Width of 2D memory copy in bytes
- Heightsize_t
Height of 2D memory copy
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_MEMCPY3D_v2¶
3D memory copy parameters
- Attributes
- srcXInBytessize_t
Source X in bytes
- srcYsize_t
Source Y
- srcZsize_t
Source Z
- srcLODsize_t
Source LOD
- srcMemoryTypeCUmemorytype
Source memory type (host, device, array)
- srcHostAny
Source host pointer
- srcDeviceCUdeviceptr
Source device pointer
- srcArrayCUarray
Source array reference
- reserved0Any
Must be NULL
- srcPitchsize_t
Source pitch (ignored when src is array)
- srcHeightsize_t
Source height (ignored when src is array; may be 0 if Depth==1)
- dstXInBytessize_t
Destination X in bytes
- dstYsize_t
Destination Y
- dstZsize_t
Destination Z
- dstLODsize_t
Destination LOD
- dstMemoryTypeCUmemorytype
Destination memory type (host, device, array)
- dstHostAny
Destination host pointer
- dstDeviceCUdeviceptr
Destination device pointer
- dstArrayCUarray
Destination array reference
- reserved1Any
Must be NULL
- dstPitchsize_t
Destination pitch (ignored when dst is array)
- dstHeightsize_t
Destination height (ignored when dst is array; may be 0 if Depth==1)
- WidthInBytessize_t
Width of 3D memory copy in bytes
- Heightsize_t
Height of 3D memory copy
- Depthsize_t
Depth of 3D memory copy
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_MEMCPY3D¶
3D memory copy parameters
- Attributes
- srcXInBytessize_t
Source X in bytes
- srcYsize_t
Source Y
- srcZsize_t
Source Z
- srcLODsize_t
Source LOD
- srcMemoryTypeCUmemorytype
Source memory type (host, device, array)
- srcHostAny
Source host pointer
- srcDeviceCUdeviceptr
Source device pointer
- srcArrayCUarray
Source array reference
- reserved0Any
Must be NULL
- srcPitchsize_t
Source pitch (ignored when src is array)
- srcHeightsize_t
Source height (ignored when src is array; may be 0 if Depth==1)
- dstXInBytessize_t
Destination X in bytes
- dstYsize_t
Destination Y
- dstZsize_t
Destination Z
- dstLODsize_t
Destination LOD
- dstMemoryTypeCUmemorytype
Destination memory type (host, device, array)
- dstHostAny
Destination host pointer
- dstDeviceCUdeviceptr
Destination device pointer
- dstArrayCUarray
Destination array reference
- reserved1Any
Must be NULL
- dstPitchsize_t
Destination pitch (ignored when dst is array)
- dstHeightsize_t
Destination height (ignored when dst is array; may be 0 if Depth==1)
- WidthInBytessize_t
Width of 3D memory copy in bytes
- Heightsize_t
Height of 3D memory copy
- Depthsize_t
Depth of 3D memory copy
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_MEMCPY3D_PEER_v1¶
3D memory cross-context copy parameters
- Attributes
- srcXInBytessize_t
Source X in bytes
- srcYsize_t
Source Y
- srcZsize_t
Source Z
- srcLODsize_t
Source LOD
- srcMemoryTypeCUmemorytype
Source memory type (host, device, array)
- srcHostAny
Source host pointer
- srcDeviceCUdeviceptr
Source device pointer
- srcArrayCUarray
Source array reference
- srcContextCUcontext
Source context (ignored with srcMemoryType is CU_MEMORYTYPE_ARRAY)
- srcPitchsize_t
Source pitch (ignored when src is array)
- srcHeightsize_t
Source height (ignored when src is array; may be 0 if Depth==1)
- dstXInBytessize_t
Destination X in bytes
- dstYsize_t
Destination Y
- dstZsize_t
Destination Z
- dstLODsize_t
Destination LOD
- dstMemoryTypeCUmemorytype
Destination memory type (host, device, array)
- dstHostAny
Destination host pointer
- dstDeviceCUdeviceptr
Destination device pointer
- dstArrayCUarray
Destination array reference
- dstContextCUcontext
Destination context (ignored with dstMemoryType is CU_MEMORYTYPE_ARRAY)
- dstPitchsize_t
Destination pitch (ignored when dst is array)
- dstHeightsize_t
Destination height (ignored when dst is array; may be 0 if Depth==1)
- WidthInBytessize_t
Width of 3D memory copy in bytes
- Heightsize_t
Height of 3D memory copy
- Depthsize_t
Depth of 3D memory copy
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_MEMCPY3D_PEER¶
3D memory cross-context copy parameters
- Attributes
- srcXInBytessize_t
Source X in bytes
- srcYsize_t
Source Y
- srcZsize_t
Source Z
- srcLODsize_t
Source LOD
- srcMemoryTypeCUmemorytype
Source memory type (host, device, array)
- srcHostAny
Source host pointer
- srcDeviceCUdeviceptr
Source device pointer
- srcArrayCUarray
Source array reference
- srcContextCUcontext
Source context (ignored with srcMemoryType is CU_MEMORYTYPE_ARRAY)
- srcPitchsize_t
Source pitch (ignored when src is array)
- srcHeightsize_t
Source height (ignored when src is array; may be 0 if Depth==1)
- dstXInBytessize_t
Destination X in bytes
- dstYsize_t
Destination Y
- dstZsize_t
Destination Z
- dstLODsize_t
Destination LOD
- dstMemoryTypeCUmemorytype
Destination memory type (host, device, array)
- dstHostAny
Destination host pointer
- dstDeviceCUdeviceptr
Destination device pointer
- dstArrayCUarray
Destination array reference
- dstContextCUcontext
Destination context (ignored with dstMemoryType is CU_MEMORYTYPE_ARRAY)
- dstPitchsize_t
Destination pitch (ignored when dst is array)
- dstHeightsize_t
Destination height (ignored when dst is array; may be 0 if Depth==1)
- WidthInBytessize_t
Width of 3D memory copy in bytes
- Heightsize_t
Height of 3D memory copy
- Depthsize_t
Depth of 3D memory copy
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_ARRAY_DESCRIPTOR_v2¶
Array descriptor
- Attributes
- Widthsize_t
Width of array
- Heightsize_t
Height of array
- FormatCUarray_format
Array format
- NumChannelsunsigned int
Channels per array element
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_ARRAY_DESCRIPTOR¶
Array descriptor
- Attributes
- Widthsize_t
Width of array
- Heightsize_t
Height of array
- FormatCUarray_format
Array format
- NumChannelsunsigned int
Channels per array element
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_ARRAY3D_DESCRIPTOR_v2¶
3D array descriptor
- Attributes
- Widthsize_t
Width of 3D array
- Heightsize_t
Height of 3D array
- Depthsize_t
Depth of 3D array
- FormatCUarray_format
Array format
- NumChannelsunsigned int
Channels per array element
- Flagsunsigned int
Flags
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_ARRAY3D_DESCRIPTOR¶
3D array descriptor
- Attributes
- Widthsize_t
Width of 3D array
- Heightsize_t
Height of 3D array
- Depthsize_t
Depth of 3D array
- FormatCUarray_format
Array format
- NumChannelsunsigned int
Channels per array element
- Flagsunsigned int
Flags
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_ARRAY_SPARSE_PROPERTIES_v1¶
CUDA array sparse properties
- Attributes
- tileExtent_CUDA_ARRAY_SPARSE_PROPERTIES_v1_CUDA_ARRAY_SPARSE_PROPERTIES_v1_CUDA_ARRAY_SPARSE_PROPERTIES_st_tileExtent_s
- miptailFirstLevelunsigned int
First mip level at which the mip tail begins.
- miptailSizeunsigned long long
Total size of the mip tail.
- flagsunsigned int
Flags will either be zero or CU_ARRAY_SPARSE_PROPERTIES_SINGLE_MIPTAIL
- reservedList[unsigned int]
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_ARRAY_SPARSE_PROPERTIES¶
CUDA array sparse properties
- Attributes
- tileExtent_CUDA_ARRAY_SPARSE_PROPERTIES_v1_CUDA_ARRAY_SPARSE_PROPERTIES_v1_CUDA_ARRAY_SPARSE_PROPERTIES_st_tileExtent_s
- miptailFirstLevelunsigned int
First mip level at which the mip tail begins.
- miptailSizeunsigned long long
Total size of the mip tail.
- flagsunsigned int
Flags will either be zero or CU_ARRAY_SPARSE_PROPERTIES_SINGLE_MIPTAIL
- reservedList[unsigned int]
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_ARRAY_MEMORY_REQUIREMENTS_v1¶
CUDA array memory requirements
- Attributes
- sizesize_t
Total required memory size
- alignmentsize_t
alignment requirement
- reservedList[unsigned int]
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_ARRAY_MEMORY_REQUIREMENTS¶
CUDA array memory requirements
- Attributes
- sizesize_t
Total required memory size
- alignmentsize_t
alignment requirement
- reservedList[unsigned int]
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_RESOURCE_DESC_v1¶
CUDA Resource descriptor
- Attributes
- resTypeCUresourcetype
Resource type
- res_CUDA_RESOURCE_DESC_v1_CUDA_RESOURCE_DESC_v1_CUDA_RESOURCE_DESC_st_res_u
- flagsunsigned int
Flags (must be zero)
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_RESOURCE_DESC¶
CUDA Resource descriptor
- Attributes
- resTypeCUresourcetype
Resource type
- res_CUDA_RESOURCE_DESC_v1_CUDA_RESOURCE_DESC_v1_CUDA_RESOURCE_DESC_st_res_u
- flagsunsigned int
Flags (must be zero)
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_TEXTURE_DESC_v1¶
Texture descriptor
- Attributes
- addressModeList[CUaddress_mode]
Address modes
- filterModeCUfilter_mode
Filter mode
- flagsunsigned int
Flags
- maxAnisotropyunsigned int
Maximum anisotropy ratio
- mipmapFilterModeCUfilter_mode
Mipmap filter mode
- mipmapLevelBiasfloat
Mipmap level bias
- minMipmapLevelClampfloat
Mipmap minimum level clamp
- maxMipmapLevelClampfloat
Mipmap maximum level clamp
- borderColorList[float]
Border Color
- reservedList[int]
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_TEXTURE_DESC¶
Texture descriptor
- Attributes
- addressModeList[CUaddress_mode]
Address modes
- filterModeCUfilter_mode
Filter mode
- flagsunsigned int
Flags
- maxAnisotropyunsigned int
Maximum anisotropy ratio
- mipmapFilterModeCUfilter_mode
Mipmap filter mode
- mipmapLevelBiasfloat
Mipmap level bias
- minMipmapLevelClampfloat
Mipmap minimum level clamp
- maxMipmapLevelClampfloat
Mipmap maximum level clamp
- borderColorList[float]
Border Color
- reservedList[int]
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_RESOURCE_VIEW_DESC_v1¶
Resource view descriptor
- Attributes
- formatCUresourceViewFormat
Resource view format
- widthsize_t
Width of the resource view
- heightsize_t
Height of the resource view
- depthsize_t
Depth of the resource view
- firstMipmapLevelunsigned int
First defined mipmap level
- lastMipmapLevelunsigned int
Last defined mipmap level
- firstLayerunsigned int
First layer index
- lastLayerunsigned int
Last layer index
- reservedList[unsigned int]
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_RESOURCE_VIEW_DESC¶
Resource view descriptor
- Attributes
- formatCUresourceViewFormat
Resource view format
- widthsize_t
Width of the resource view
- heightsize_t
Height of the resource view
- depthsize_t
Depth of the resource view
- firstMipmapLevelunsigned int
First defined mipmap level
- lastMipmapLevelunsigned int
Last defined mipmap level
- firstLayerunsigned int
First layer index
- lastLayerunsigned int
Last layer index
- reservedList[unsigned int]
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_POINTER_ATTRIBUTE_P2P_TOKENS_v1¶
GPU Direct v3 tokens
- Attributes
- p2pTokenunsigned long long
- vaSpaceTokenunsigned int
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_POINTER_ATTRIBUTE_P2P_TOKENS¶
GPU Direct v3 tokens
- Attributes
- p2pTokenunsigned long long
- vaSpaceTokenunsigned int
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_LAUNCH_PARAMS_v1¶
Kernel launch parameters
- Attributes
- functionCUfunction
Kernel to launch
- gridDimXunsigned int
Width of grid in blocks
- gridDimYunsigned int
Height of grid in blocks
- gridDimZunsigned int
Depth of grid in blocks
- blockDimXunsigned int
X dimension of each thread block
- blockDimYunsigned int
Y dimension of each thread block
- blockDimZunsigned int
Z dimension of each thread block
- sharedMemBytesunsigned int
Dynamic shared-memory size per thread block in bytes
- hStreamCUstream
Stream identifier
- kernelParamsAny
Array of pointers to kernel parameters
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_LAUNCH_PARAMS¶
Kernel launch parameters
- Attributes
- functionCUfunction
Kernel to launch
- gridDimXunsigned int
Width of grid in blocks
- gridDimYunsigned int
Height of grid in blocks
- gridDimZunsigned int
Depth of grid in blocks
- blockDimXunsigned int
X dimension of each thread block
- blockDimYunsigned int
Y dimension of each thread block
- blockDimZunsigned int
Z dimension of each thread block
- sharedMemBytesunsigned int
Dynamic shared-memory size per thread block in bytes
- hStreamCUstream
Stream identifier
- kernelParamsAny
Array of pointers to kernel parameters
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_EXTERNAL_MEMORY_HANDLE_DESC_v1¶
External memory handle descriptor
- Attributes
- typeCUexternalMemoryHandleType
Type of the handle
- handle_CUDA_EXTERNAL_MEMORY_HANDLE_DESC_v1_CUDA_EXTERNAL_MEMORY_HANDLE_DESC_v1_CUDA_EXTERNAL_MEMORY_HANDLE_DESC_st_handle_u
- sizeunsigned long long
Size of the memory allocation
- flagsunsigned int
Flags must either be zero or CUDA_EXTERNAL_MEMORY_DEDICATED
- reservedList[unsigned int]
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_EXTERNAL_MEMORY_HANDLE_DESC¶
External memory handle descriptor
- Attributes
- typeCUexternalMemoryHandleType
Type of the handle
- handle_CUDA_EXTERNAL_MEMORY_HANDLE_DESC_v1_CUDA_EXTERNAL_MEMORY_HANDLE_DESC_v1_CUDA_EXTERNAL_MEMORY_HANDLE_DESC_st_handle_u
- sizeunsigned long long
Size of the memory allocation
- flagsunsigned int
Flags must either be zero or CUDA_EXTERNAL_MEMORY_DEDICATED
- reservedList[unsigned int]
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_EXTERNAL_MEMORY_BUFFER_DESC_v1¶
External memory buffer descriptor
- Attributes
- offsetunsigned long long
Offset into the memory object where the buffer’s base is
- sizeunsigned long long
Size of the buffer
- flagsunsigned int
Flags reserved for future use. Must be zero.
- reservedList[unsigned int]
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_EXTERNAL_MEMORY_BUFFER_DESC¶
External memory buffer descriptor
- Attributes
- offsetunsigned long long
Offset into the memory object where the buffer’s base is
- sizeunsigned long long
Size of the buffer
- flagsunsigned int
Flags reserved for future use. Must be zero.
- reservedList[unsigned int]
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC_v1¶
External memory mipmap descriptor
- Attributes
- offsetunsigned long long
Offset into the memory object where the base level of the mipmap chain is.
- arrayDescCUDA_ARRAY3D_DESCRIPTOR
Format, dimension and type of base level of the mipmap chain
- numLevelsunsigned int
Total number of levels in the mipmap chain
- reservedList[unsigned int]
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC¶
External memory mipmap descriptor
- Attributes
- offsetunsigned long long
Offset into the memory object where the base level of the mipmap chain is.
- arrayDescCUDA_ARRAY3D_DESCRIPTOR
Format, dimension and type of base level of the mipmap chain
- numLevelsunsigned int
Total number of levels in the mipmap chain
- reservedList[unsigned int]
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_v1¶
External semaphore handle descriptor
- Attributes
- typeCUexternalSemaphoreHandleType
Type of the handle
- handle_CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_v1_CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_v1_CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_st_handle_u
- flagsunsigned int
Flags reserved for the future. Must be zero.
- reservedList[unsigned int]
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC¶
External semaphore handle descriptor
- Attributes
- typeCUexternalSemaphoreHandleType
Type of the handle
- handle_CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_v1_CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_v1_CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_st_handle_u
- flagsunsigned int
Flags reserved for the future. Must be zero.
- reservedList[unsigned int]
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_v1¶
External semaphore signal parameters
- Attributes
- params_CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_v1_CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_v1_CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_st_params_s
- flagsunsigned int
Only when CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS is used to signal a CUexternalSemaphore of type CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC, the valid flag is CUDA_EXTERNAL_SEMAPHORE_SIGNAL_SKIP_NVSCIBUF_MEMSYNC which indicates that while signaling the CUexternalSemaphore, no memory synchronization operations should be performed for any external memory object imported as CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF. For all other types of CUexternalSemaphore, flags must be zero.
- reservedList[unsigned int]
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS¶
External semaphore signal parameters
- Attributes
- params_CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_v1_CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_v1_CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_st_params_s
- flagsunsigned int
Only when CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS is used to signal a CUexternalSemaphore of type CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC, the valid flag is CUDA_EXTERNAL_SEMAPHORE_SIGNAL_SKIP_NVSCIBUF_MEMSYNC which indicates that while signaling the CUexternalSemaphore, no memory synchronization operations should be performed for any external memory object imported as CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF. For all other types of CUexternalSemaphore, flags must be zero.
- reservedList[unsigned int]
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_v1¶
External semaphore wait parameters
- Attributes
- params_CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_v1_CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_v1_CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_st_params_s
- flagsunsigned int
Only when CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS is used to wait on a CUexternalSemaphore of type CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC, the valid flag is CUDA_EXTERNAL_SEMAPHORE_WAIT_SKIP_NVSCIBUF_MEMSYNC which indicates that while waiting for the CUexternalSemaphore, no memory synchronization operations should be performed for any external memory object imported as CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF. For all other types of CUexternalSemaphore, flags must be zero.
- reservedList[unsigned int]
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS¶
External semaphore wait parameters
- Attributes
- params_CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_v1_CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_v1_CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_st_params_s
- flagsunsigned int
Only when CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS is used to wait on a CUexternalSemaphore of type CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC, the valid flag is CUDA_EXTERNAL_SEMAPHORE_WAIT_SKIP_NVSCIBUF_MEMSYNC which indicates that while waiting for the CUexternalSemaphore, no memory synchronization operations should be performed for any external memory object imported as CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF. For all other types of CUexternalSemaphore, flags must be zero.
- reservedList[unsigned int]
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v1¶
Semaphore signal node parameters
- Attributes
- extSemArrayCUexternalSemaphore
Array of external semaphore handles.
- paramsArrayCUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS
Array of external semaphore signal parameters.
- numExtSemsunsigned int
Number of handles and parameters supplied in extSemArray and paramsArray.
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_EXT_SEM_SIGNAL_NODE_PARAMS¶
Semaphore signal node parameters
- Attributes
- extSemArrayCUexternalSemaphore
Array of external semaphore handles.
- paramsArrayCUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS
Array of external semaphore signal parameters.
- numExtSemsunsigned int
Number of handles and parameters supplied in extSemArray and paramsArray.
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_EXT_SEM_WAIT_NODE_PARAMS_v1¶
Semaphore wait node parameters
- Attributes
- extSemArrayCUexternalSemaphore
Array of external semaphore handles.
- paramsArrayCUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS
Array of external semaphore wait parameters.
- numExtSemsunsigned int
Number of handles and parameters supplied in extSemArray and paramsArray.
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_EXT_SEM_WAIT_NODE_PARAMS¶
Semaphore wait node parameters
- Attributes
- extSemArrayCUexternalSemaphore
Array of external semaphore handles.
- paramsArrayCUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS
Array of external semaphore wait parameters.
- numExtSemsunsigned int
Number of handles and parameters supplied in extSemArray and paramsArray.
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUmemGenericAllocationHandle_v1¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUmemGenericAllocationHandle¶
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUarrayMapInfo_v1¶
Specifies the CUDA array or CUDA mipmapped array memory mapping information
- Attributes
- resourceTypeCUresourcetype
Resource type
- resource_CUarrayMapInfo_v1_CUarrayMapInfo_v1_CUarrayMapInfo_st_resource_u
- subresourceTypeCUarraySparseSubresourceType
Sparse subresource type
- subresource_CUarrayMapInfo_v1_CUarrayMapInfo_v1_CUarrayMapInfo_st_subresource_u
- memOperationTypeCUmemOperationType
Memory operation type
- memHandleTypeCUmemHandleType
Memory handle type
- memHandle_CUarrayMapInfo_v1_CUarrayMapInfo_v1_CUarrayMapInfo_st_memHandle_u
- offsetunsigned long long
Offset within mip tail Offset within the memory
- deviceBitMaskunsigned int
Device ordinal bit mask
- flagsunsigned int
flags for future use, must be zero now.
- reservedList[unsigned int]
Reserved for future use, must be zero now.
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUarrayMapInfo¶
Specifies the CUDA array or CUDA mipmapped array memory mapping information
- Attributes
- resourceTypeCUresourcetype
Resource type
- resource_CUarrayMapInfo_v1_CUarrayMapInfo_v1_CUarrayMapInfo_st_resource_u
- subresourceTypeCUarraySparseSubresourceType
Sparse subresource type
- subresource_CUarrayMapInfo_v1_CUarrayMapInfo_v1_CUarrayMapInfo_st_subresource_u
- memOperationTypeCUmemOperationType
Memory operation type
- memHandleTypeCUmemHandleType
Memory handle type
- memHandle_CUarrayMapInfo_v1_CUarrayMapInfo_v1_CUarrayMapInfo_st_memHandle_u
- offsetunsigned long long
Offset within mip tail Offset within the memory
- deviceBitMaskunsigned int
Device ordinal bit mask
- flagsunsigned int
flags for future use, must be zero now.
- reservedList[unsigned int]
Reserved for future use, must be zero now.
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUmemLocation_v1¶
Specifies a memory location.
- Attributes
- typeCUmemLocationType
Specifies the location type, which modifies the meaning of id.
- idint
identifier for a given this location’s CUmemLocationType.
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUmemLocation¶
Specifies a memory location.
- Attributes
- typeCUmemLocationType
Specifies the location type, which modifies the meaning of id.
- idint
identifier for a given this location’s CUmemLocationType.
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUmemAllocationProp_v1¶
Specifies the allocation properties for a allocation.
- Attributes
- typeCUmemAllocationType
Allocation type
- requestedHandleTypesCUmemAllocationHandleType
requested CUmemAllocationHandleType
- locationCUmemLocation
Location of allocation
- win32HandleMetaDataAny
Windows-specific POBJECT_ATTRIBUTES required when CU_MEM_HANDLE_TYPE_WIN32 is specified. This object atributes structure includes security attributes that define the scope of which exported allocations may be tranferred to other processes. In all other cases, this field is required to be zero.
- allocFlags_CUmemAllocationProp_v1_CUmemAllocationProp_v1_CUmemAllocationProp_st_allocFlags_s
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUmemAllocationProp¶
Specifies the allocation properties for a allocation.
- Attributes
- typeCUmemAllocationType
Allocation type
- requestedHandleTypesCUmemAllocationHandleType
requested CUmemAllocationHandleType
- locationCUmemLocation
Location of allocation
- win32HandleMetaDataAny
Windows-specific POBJECT_ATTRIBUTES required when CU_MEM_HANDLE_TYPE_WIN32 is specified. This object atributes structure includes security attributes that define the scope of which exported allocations may be tranferred to other processes. In all other cases, this field is required to be zero.
- allocFlags_CUmemAllocationProp_v1_CUmemAllocationProp_v1_CUmemAllocationProp_st_allocFlags_s
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUmemAccessDesc_v1¶
Memory access descriptor
- Attributes
- locationCUmemLocation
Location on which the request is to change it’s accessibility
- flagsCUmemAccess_flags
::CUmemProt accessibility flags to set on the request
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUmemAccessDesc¶
Memory access descriptor
- Attributes
- locationCUmemLocation
Location on which the request is to change it’s accessibility
- flagsCUmemAccess_flags
::CUmemProt accessibility flags to set on the request
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUmemPoolProps_v1¶
Specifies the properties of allocations made from the pool.
- Attributes
- allocTypeCUmemAllocationType
Allocation type. Currently must be specified as CU_MEM_ALLOCATION_TYPE_PINNED
- handleTypesCUmemAllocationHandleType
Handle types that will be supported by allocations from the pool.
- locationCUmemLocation
Location where allocations should reside.
- win32SecurityAttributesAny
Windows-specific LPSECURITYATTRIBUTES required when CU_MEM_HANDLE_TYPE_WIN32 is specified. This security attribute defines the scope of which exported allocations may be tranferred to other processes. In all other cases, this field is required to be zero.
- reservedbytes
reserved for future use, must be 0
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUmemPoolProps¶
Specifies the properties of allocations made from the pool.
- Attributes
- allocTypeCUmemAllocationType
Allocation type. Currently must be specified as CU_MEM_ALLOCATION_TYPE_PINNED
- handleTypesCUmemAllocationHandleType
Handle types that will be supported by allocations from the pool.
- locationCUmemLocation
Location where allocations should reside.
- win32SecurityAttributesAny
Windows-specific LPSECURITYATTRIBUTES required when CU_MEM_HANDLE_TYPE_WIN32 is specified. This security attribute defines the scope of which exported allocations may be tranferred to other processes. In all other cases, this field is required to be zero.
- reservedbytes
reserved for future use, must be 0
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUmemPoolPtrExportData_v1¶
Opaque data for exporting a pool allocation
- Attributes
- reservedbytes
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUmemPoolPtrExportData¶
Opaque data for exporting a pool allocation
- Attributes
- reservedbytes
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUDA_MEM_ALLOC_NODE_PARAMS¶
Memory allocation node parameters
- Attributes
- poolPropsCUmemPoolProps
in: location where the allocation should reside (specified in location). handleTypes must be CU_MEM_HANDLE_TYPE_NONE. IPC is not supported.
- accessDescsCUmemAccessDesc
in: array of memory access descriptors. Used to describe peer GPU access
- accessDescCountsize_t
in: number of memory access descriptors. Must not exceed the number of GPUs.
- bytesizesize_t
in: size in bytes of the requested allocation
- dptrCUdeviceptr
out: address of the allocation returned by CUDA
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUeglFrame_v1¶
CUDA EGLFrame structure Descriptor - structure defining one frame of EGL. Each frame may contain one or more planes depending on whether the surface * is Multiplanar or not.
- Attributes
- frame_CUeglFrame_v1_CUeglFrame_v1_CUeglFrame_st_frame_u
- widthunsigned int
Width of first plane
- heightunsigned int
Height of first plane
- depthunsigned int
Depth of first plane
- pitchunsigned int
Pitch of first plane
- planeCountunsigned int
Number of planes
- numChannelsunsigned int
Number of channels for the plane
- frameTypeCUeglFrameType
Array or Pitch
- eglColorFormatCUeglColorFormat
CUDA EGL Color Format
- cuFormatCUarray_format
CUDA Array Format
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUeglFrame¶
CUDA EGLFrame structure Descriptor - structure defining one frame of EGL. Each frame may contain one or more planes depending on whether the surface * is Multiplanar or not.
- Attributes
- frame_CUeglFrame_v1_CUeglFrame_v1_CUeglFrame_st_frame_u
- widthunsigned int
Width of first plane
- heightunsigned int
Height of first plane
- depthunsigned int
Depth of first plane
- pitchunsigned int
Pitch of first plane
- planeCountunsigned int
Number of planes
- numChannelsunsigned int
Number of channels for the plane
- frameTypeCUeglFrameType
Array or Pitch
- eglColorFormatCUeglColorFormat
CUDA EGL Color Format
- cuFormatCUarray_format
CUDA Array Format
Methods
getPtr()
Get memory address of class instance
- class cuda.cuda.CUeglStreamConnection(*args, **kwargs)¶
Methods
getPtr()
Get memory address of class instance
Error Handling¶
This section describes the error handling functions of the low-level CUDA driver application programming interface.
- cuda.cuda.cuGetErrorString(error: CUresult)¶
Gets the string description of an error code.
Sets *pStr to the address of a NULL-terminated string description of the error code error. If the error code is not recognized, CUDA_ERROR_INVALID_VALUE will be returned and *pStr will be set to the NULL address.
- Parameters
- errorCUresult
Error code to convert to string
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- pStrbytes
Address of the string pointer.
See also
CUresult
cudaGetErrorString
- cuda.cuda.cuGetErrorName(error: CUresult)¶
Gets the string representation of an error code enum name.
Sets *pStr to the address of a NULL-terminated string representation of the name of the enum error code error. If the error code is not recognized, CUDA_ERROR_INVALID_VALUE will be returned and *pStr will be set to the NULL address.
- Parameters
- errorCUresult
Error code to convert to string
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- pStrbytes
Address of the string pointer.
See also
CUresult
cudaGetErrorName
Initialization¶
This section describes the initialization functions of the low-level CUDA driver application programming interface.
- cuda.cuda.cuInit(unsigned int Flags)¶
Initialize the CUDA driver API.
Initializes the driver API and must be called before any other function from the driver API. Currently, the Flags parameter must be 0. If cuInit() has not been called, any function from the driver API will return CUDA_ERROR_NOT_INITIALIZED.
- Parameters
- Flagsunsigned int
Initialization flag for CUDA.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_DEVICE CUDA_ERROR_SYSTEM_DRIVER_MISMATCH CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE
- None
None
Version Management¶
This section describes the version management functions of the low-level CUDA driver application programming interface.
- cuda.cuda.cuDriverGetVersion()¶
Returns the latest CUDA version supported by driver.
Returns in *driverVersion the version of CUDA supported by the driver. The version is returned as (1000 * major + 10 * minor). For example, CUDA 9.2 would be represented by 9020.
This function automatically returns CUDA_ERROR_INVALID_VALUE if driverVersion is NULL.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- driverVersionint
Returns the CUDA driver version
Device Management¶
This section describes the device management functions of the low-level CUDA driver application programming interface.
- cuda.cuda.cuDeviceGet(int ordinal)¶
Returns a handle to a compute device.
Returns in *device a device handle given an ordinal in the range [0, cuDeviceGetCount()-1].
- Parameters
- ordinalint
Device number to get handle for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_DEVICE
- deviceCUdevice
Returned device handle
See also
cuDeviceGetAttribute
cuDeviceGetCount
cuDeviceGetName
cuDeviceGetUuid
cuDeviceGetLuid
cuDeviceTotalMem
cuDeviceGetExecAffinitySupport
- cuda.cuda.cuDeviceGetCount()¶
Returns the number of compute-capable devices.
Returns in *count the number of devices with compute capability greater than or equal to 2.0 that are available for execution. If there is no such device, cuDeviceGetCount() returns 0.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- countint
Returned number of compute-capable devices
See also
cuDeviceGetAttribute
cuDeviceGetName
cuDeviceGetUuid
cuDeviceGetLuid
cuDeviceGet
cuDeviceTotalMem
cuDeviceGetExecAffinitySupport
cudaGetDeviceCount
- cuda.cuda.cuDeviceGetName(int length, dev)¶
Returns an identifer string for the device.
Returns an ASCII string identifying the device dev in the NULL- terminated string pointed to by name. length specifies the maximum length of the string that may be returned.
- Parameters
- lengthint
Maximum length of string to store in name
- devAny
Device to get identifier string for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_DEVICE
- namebytes
Returned identifier string for the device
See also
cuDeviceGetAttribute
cuDeviceGetUuid
cuDeviceGetLuid
cuDeviceGetCount
cuDeviceGet
cuDeviceTotalMem
cuDeviceGetExecAffinitySupport
cudaGetDeviceProperties
- cuda.cuda.cuDeviceGetUuid(dev)¶
Return an UUID for the device.
Note there is a later version of this API, cuDeviceGetUuid_v2. It will supplant this version in 12.0, which is retained for minor version compatibility.
Returns 16-octets identifing the device dev in the structure pointed by the uuid.
- Parameters
- devAny
Device to get identifier string for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_DEVICE
- uuidCUuuid
Returned UUID
See also
cuDeviceGetUuid_v2
cuDeviceGetAttribute
cuDeviceGetCount
cuDeviceGetName
cuDeviceGetLuid
cuDeviceGet
cuDeviceTotalMem
cuDeviceGetExecAffinitySupport
cudaGetDeviceProperties
- cuda.cuda.cuDeviceGetUuid_v2(dev) Return an UUID for the device (11.4+)¶
Return an UUID for the device (11.4+)
Returns 16-octets identifing the device dev in the structure pointed by the uuid. If the device is in MIG mode, returns its MIG UUID which uniquely identifies the subscribed MIG compute instance.
- Parameters
- devAny
Device to get identifier string for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_DEVICE
- uuidCUuuid
Returned UUID
See also
cuDeviceGetAttribute
cuDeviceGetCount
cuDeviceGetName
cuDeviceGetLuid
cuDeviceGet
cuDeviceTotalMem
cudaGetDeviceProperties
- cuda.cuda.cuDeviceGetLuid(dev)¶
Return an LUID and device node mask for the device.
Return identifying information (luid and deviceNodeMask) to allow matching device with graphics APIs.
- Parameters
- devAny
Device to get identifier string for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_DEVICE
- luidbytes
Returned LUID
- deviceNodeMaskunsigned int
Returned device node mask
See also
cuDeviceGetAttribute
cuDeviceGetCount
cuDeviceGetName
cuDeviceGet
cuDeviceTotalMem
cuDeviceGetExecAffinitySupport
cudaGetDeviceProperties
- cuda.cuda.cuDeviceTotalMem(dev)¶
Returns the total amount of memory on the device.
Returns in *bytes the total amount of memory available on the device dev in bytes.
- Parameters
- devAny
Device handle
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_DEVICE
- numbytesint
Returned memory available on device in bytes
See also
cuDeviceGetAttribute
cuDeviceGetCount
cuDeviceGetName
cuDeviceGetUuid
cuDeviceGet
cuDeviceGetExecAffinitySupport
cudaMemGetInfo
- cuda.cuda.cuDeviceGetTexture1DLinearMaxWidth(pformat: CUarray_format, unsigned int numChannels, dev)¶
Returns the maximum number of elements allocatable in a 1D linear texture for a given texture element size.
Returns in maxWidthInElements the maximum number of texture elements allocatable in a 1D linear texture for given pformat and numChannels.
- Parameters
- pformatCUarray_format
Texture format.
- numChannelsunsigned
Number of channels per texture element.
- devAny
Device handle.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_DEVICE
- maxWidthInElementsint
Returned maximum number of texture elements allocatable for given pformat and numChannels.
See also
- cuda.cuda.cuDeviceGetAttribute(attrib: CUdevice_attribute, dev)¶
Returns information about the device.
Returns in *pi the integer value of the attribute attrib on device dev. The supported attributes are: - CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK: Maximum number of threads per block; - CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X: Maximum x-dimension of a block - CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y: Maximum y-dimension of a block - CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z: Maximum z-dimension of a block - CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_X: Maximum x-dimension of a grid - CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y: Maximum y-dimension of a grid - CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z: Maximum z-dimension of a grid - CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK: Maximum amount of shared memory available to a thread block in bytes - CU_DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY: Memory available on device for constant variables in a CUDA C kernel in bytes - CU_DEVICE_ATTRIBUTE_WARP_SIZE: Warp size in threads - CU_DEVICE_ATTRIBUTE_MAX_PITCH: Maximum pitch in bytes allowed by the memory copy functions that involve memory regions allocated through cuMemAllocPitch() - CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH: Maximum 1D texture width - CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH: Maximum width for a 1D texture bound to linear memory - CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH: Maximum mipmapped 1D texture width - CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH: Maximum 2D texture width - CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT: Maximum 2D texture height - CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH: Maximum width for a 2D texture bound to linear memory - CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT: Maximum height for a 2D texture bound to linear memory - CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH: Maximum pitch in bytes for a 2D texture bound to linear memory - CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH: Maximum mipmapped 2D texture width - CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT: Maximum mipmapped 2D texture height - CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH: Maximum 3D texture width - CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT: Maximum 3D texture height - CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH: Maximum 3D texture depth - CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE: Alternate maximum 3D texture width, 0 if no alternate maximum 3D texture size is supported - CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE: Alternate maximum 3D texture height, 0 if no alternate maximum 3D texture size is supported - CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE: Alternate maximum 3D texture depth, 0 if no alternate maximum 3D texture size is supported - CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH: Maximum cubemap texture width or height - CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH: Maximum 1D layered texture width - CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS: Maximum layers in a 1D layered texture - CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH: Maximum 2D layered texture width - CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT: Maximum 2D layered texture height - CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS: Maximum layers in a 2D layered texture - CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH: Maximum cubemap layered texture width or height - CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS: Maximum layers in a cubemap layered texture - CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH: Maximum 1D surface width - CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH: Maximum 2D surface width - CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT: Maximum 2D surface height - CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH: Maximum 3D surface width - CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT: Maximum 3D surface height - CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH: Maximum 3D surface depth - CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH: Maximum 1D layered surface width - CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS: Maximum layers in a 1D layered surface - CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH: Maximum 2D layered surface width - CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT: Maximum 2D layered surface height - CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS: Maximum layers in a 2D layered surface - CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH: Maximum cubemap surface width - CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH: Maximum cubemap layered surface width - CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS: Maximum layers in a cubemap layered surface - CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK: Maximum number of 32-bit registers available to a thread block - CU_DEVICE_ATTRIBUTE_CLOCK_RATE: The typical clock frequency in kilohertz - CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT: Alignment requirement; texture base addresses aligned to textureAlign bytes do not need an offset applied to texture fetches - CU_DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT: Pitch alignment requirement for 2D texture references bound to pitched memory - CU_DEVICE_ATTRIBUTE_GPU_OVERLAP: 1 if the device can concurrently copy memory between host and device while executing a kernel, or 0 if not - CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT: Number of multiprocessors on the device - CU_DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT: 1 if there is a run time limit for kernels executed on the device, or 0 if not - CU_DEVICE_ATTRIBUTE_INTEGRATED: 1 if the device is integrated with the memory subsystem, or 0 if not - CU_DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY: 1 if the device can map host memory into the CUDA address space, or 0 if not - CU_DEVICE_ATTRIBUTE_COMPUTE_MODE: Compute mode that device is currently in. Available modes are as follows: - CU_COMPUTEMODE_DEFAULT: Default mode - Device is not restricted and can have multiple CUDA contexts present at a single time. - CU_COMPUTEMODE_PROHIBITED: Compute-prohibited mode - Device is prohibited from creating new CUDA contexts. - CU_COMPUTEMODE_EXCLUSIVE_PROCESS: Compute-exclusive- process mode - Device can have only one context used by a single process at a time. - CU_DEVICE_ATTRIBUTE_CONCURRENT_KERNELS: 1 if the device supports executing multiple kernels within the same context simultaneously, or 0 if not. It is not guaranteed that multiple kernels will be resident on the device concurrently so this feature should not be relied upon for correctness. - CU_DEVICE_ATTRIBUTE_ECC_ENABLED: 1 if error correction is enabled on the device, 0 if error correction is disabled or not supported by the device - CU_DEVICE_ATTRIBUTE_PCI_BUS_ID: PCI bus identifier of the device - CU_DEVICE_ATTRIBUTE_PCI_DEVICE_ID: PCI device (also known as slot) identifier of the device - CU_DEVICE_ATTRIBUTE_PCI_DOMAIN_ID: PCI domain identifier of the device - CU_DEVICE_ATTRIBUTE_TCC_DRIVER: 1 if the device is using a TCC driver. TCC is only available on Tesla hardware running Windows Vista or later - CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE: Peak memory clock frequency in kilohertz - CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH: Global memory bus width in bits - CU_DEVICE_ATTRIBUTE_L2_CACHE_SIZE: Size of L2 cache in bytes. 0 if the device doesn’t have L2 cache - CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR: Maximum resident threads per multiprocessor - CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING: 1 if the device shares a unified address space with the host, or 0 if not - CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR: Major compute capability version number - CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR: Minor compute capability version number - CU_DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED: 1 if device supports caching globals in L1 cache, 0 if caching globals in L1 cache is not supported by the device - CU_DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED: 1 if device supports caching locals in L1 cache, 0 if caching locals in L1 cache is not supported by the device - CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR: Maximum amount of shared memory available to a multiprocessor in bytes; this amount is shared by all thread blocks simultaneously resident on a multiprocessor - CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR: Maximum number of 32-bit registers available to a multiprocessor; this number is shared by all thread blocks simultaneously resident on a multiprocessor - CU_DEVICE_ATTRIBUTE_MANAGED_MEMORY: 1 if device supports allocating managed memory on this system, 0 if allocating managed memory is not supported by the device on this system. - CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD: 1 if device is on a multi-GPU board, 0 if not. - CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID: Unique identifier for a group of devices associated with the same board. Devices on the same multi-GPU board will share the same identifier. - CU_DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED: 1 if Link between the device and the host supports native atomic operations. - CU_DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO: Ratio of single precision performance (in floating-point operations per second) to double precision performance. - CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS: Device suppports coherently accessing pageable memory without calling cudaHostRegister on it. - CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS: Device can coherently access managed memory concurrently with the CPU. - CU_DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED: Device supports Compute Preemption. - CU_DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM: Device can access host registered memory at the same virtual address as the CPU. - CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN: The maximum per block shared memory size suported on this device. This is the maximum value that can be opted into when using the cuFuncSetAttribute() call. For more details see CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES - CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES: Device accesses pageable memory via the host’s page tables. - CU_DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST: The host can directly access managed memory on the device without migration. - CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED: Device supports virtual memory management APIs like cuMemAddressReserve, cuMemCreate, cuMemMap and related APIs - CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_POSIX_FILE_DESCRIPTOR_SUPPORTED: Device supports exporting memory to a posix file descriptor with cuMemExportToShareableHandle, if requested via cuMemCreate - CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_WIN32_HANDLE_SUPPORTED: Device supports exporting memory to a Win32 NT handle with cuMemExportToShareableHandle, if requested via cuMemCreate - CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_WIN32_KMT_HANDLE_SUPPORTED: Device supports exporting memory to a Win32 KMT handle with cuMemExportToShareableHandle, if requested via cuMemCreate - CU_DEVICE_ATTRIBUTE_MAX_BLOCKS_PER_MULTIPROCESSOR: Maximum number of thread blocks that can reside on a multiprocessor - CU_DEVICE_ATTRIBUTE_GENERIC_COMPRESSION_SUPPORTED: Device supports compressible memory allocation via cuMemCreate - CU_DEVICE_ATTRIBUTE_MAX_PERSISTING_L2_CACHE_SIZE: Maximum L2 persisting lines capacity setting in bytes - CU_DEVICE_ATTRIBUTE_MAX_ACCESS_POLICY_WINDOW_SIZE: Maximum value of CUaccessPolicyWindow::num_bytes - CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_WITH_CUDA_VMM_SUPPORTED: Device supports specifying the GPUDirect RDMA flag with cuMemCreate. - CU_DEVICE_ATTRIBUTE_RESERVED_SHARED_MEMORY_PER_BLOCK: Amount of shared memory per block reserved by CUDA driver in bytes - CU_DEVICE_ATTRIBUTE_SPARSE_CUDA_ARRAY_SUPPORTED: Device supports sparse CUDA arrays and sparse CUDA mipmapped arrays. - CU_DEVICE_ATTRIBUTE_READ_ONLY_HOST_REGISTER_SUPPORTED: Device supports using the cuMemHostRegister flag CU_MEMHOSTERGISTER_READ_ONLY to register memory that must be mapped as read-only to the GPU - CU_DEVICE_ATTRIBUTE_MEMORY_POOLS_SUPPORTED: Device supports using the cuMemAllocAsync and cuMemPool family of APIs - CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_SUPPORTED: Device supports GPUDirect RDMA APIs, like nvidia_p2p_get_pages (see https://docs.nvidia.com/cuda/gpudirect-rdma for more information) - CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_FLUSH_WRITES_OPTIONS: The returned attribute shall be interpreted as a bitmask, where the individual bits are described by the CUflushGPUDirectRDMAWritesOptions enum - CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_WRITES_ORDERING: GPUDirect RDMA writes to the device do not need to be flushed for consumers within the scope indicated by the returned attribute. See CUGPUDirectRDMAWritesOrdering for the numerical values returned here. - CU_DEVICE_ATTRIBUTE_MEMPOOL_SUPPORTED_HANDLE_TYPES: Bitmask of handle types supported with mempool based IPC - CU_DEVICE_ATTRIBUTE_DEFERRED_MAPPING_CUDA_ARRAY_SUPPORTED: Device supports deferred mapping CUDA arrays and CUDA mipmapped arrays.
- Parameters
- attribCUdevice_attribute
Device attribute to query
- devAny
Device handle
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_DEVICE
- piint
Returned device attribute value
See also
cuDeviceGetCount
cuDeviceGetName
cuDeviceGetUuid
cuDeviceGet
cuDeviceTotalMem
cuDeviceGetExecAffinitySupport
cudaDeviceGetAttribute
cudaGetDeviceProperties
- cuda.cuda.cuDeviceGetNvSciSyncAttributes(dev, int flags)¶
Return NvSciSync attributes that this device can support.
Returns in nvSciSyncAttrList, the properties of NvSciSync that this CUDA device, dev can support. The returned nvSciSyncAttrList can be used to create an NvSciSync object that matches this device’s capabilities.
If NvSciSyncAttrKey_RequiredPerm field in nvSciSyncAttrList is already set this API will return CUDA_ERROR_INVALID_VALUE.
The applications should set nvSciSyncAttrList to a valid NvSciSyncAttrList failing which this API will return CUDA_ERROR_INVALID_HANDLE.
The flags controls how applications intends to use the NvSciSync created from the nvSciSyncAttrList. The valid flags are: - CUDA_NVSCISYNC_ATTR_SIGNAL, specifies that the applications intends to signal an NvSciSync on this CUDA device. - CUDA_NVSCISYNC_ATTR_WAIT, specifies that the applications intends to wait on an NvSciSync on this CUDA device.
At least one of these flags must be set, failing which the API returns CUDA_ERROR_INVALID_VALUE. Both the flags are orthogonal to one another: a developer may set both these flags that allows to set both wait and signal specific attributes in the same nvSciSyncAttrList.
- Parameters
- devAny
Valid Cuda Device to get NvSciSync attributes for.
- flagsint
flags describing NvSciSync usage.
- Returns
- CUresult
- nvSciSyncAttrListint
Return NvSciSync attributes supported.
- cuda.cuda.cuDeviceSetMemPool(dev, pool)¶
Sets the current memory pool of a device.
The memory pool must be local to the specified device. cuMemAllocAsync allocates from the current mempool of the provided stream’s device. By default, a device’s current memory pool is its default memory pool.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- None
None
See also
Notes
Use cuMemAllocFromPoolAsync to specify asynchronous allocations from a device different than the one the stream runs on.
- cuda.cuda.cuDeviceGetMemPool(dev)¶
Gets the current mempool for a device.
Returns the last pool provided to cuDeviceSetMemPool for this device or the device’s default memory pool if cuDeviceSetMemPool has never been called. By default the current mempool is the default mempool for a device. Otherwise the returned pool must have been set with cuDeviceSetMemPool.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- None
None
- cuda.cuda.cuDeviceGetDefaultMemPool(dev)¶
Returns the default mempool of a device.
The default mempool of a device contains device memory from that device.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_DEVICE CUDA_ERROR_NOT_SUPPORTED
- None
None
- cuda.cuda.cuFlushGPUDirectRDMAWrites(target: CUflushGPUDirectRDMAWritesTarget, scope: CUflushGPUDirectRDMAWritesScope)¶
Blocks until remote writes are visible to the specified scope.
Blocks until GPUDirect RDMA writes to the target context via mappings created through APIs like nvidia_p2p_get_pages (see https://docs.nvidia.com/cuda/gpudirect-rdma for more information), are visible to the specified scope.
If the scope equals or lies within the scope indicated by CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_WRITES_ORDERING, the call will be a no-op and can be safely omitted for performance. This can be determined by comparing the numerical values between the two enums, with smaller scopes having smaller values.
Users may query support for this API via CU_DEVICE_ATTRIBUTE_FLUSH_FLUSH_GPU_DIRECT_RDMA_OPTIONS.
- Parameters
- targetCUflushGPUDirectRDMAWritesTarget
The target of the operation, see CUflushGPUDirectRDMAWritesTarget
- scopeCUflushGPUDirectRDMAWritesScope
The scope of the operation, see CUflushGPUDirectRDMAWritesScope
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
Primary Context Management¶
This section describes the primary context management functions of the low-level CUDA driver application programming interface.
The primary context is unique per device and shared with the CUDA runtime API. These functions allow integration with other libraries using CUDA.
- cuda.cuda.cuDevicePrimaryCtxRetain(dev)¶
Retain the primary context on the GPU.
Retains the primary context on the device. Once the user successfully retains the primary context, the primary context will be active and available to the user until the user releases it with cuDevicePrimaryCtxRelease() or resets it with cuDevicePrimaryCtxReset(). Unlike cuCtxCreate() the newly retained context is not pushed onto the stack.
Retaining the primary context for the first time will fail with CUDA_ERROR_UNKNOWN if the compute mode of the device is CU_COMPUTEMODE_PROHIBITED. The function cuDeviceGetAttribute() can be used with CU_DEVICE_ATTRIBUTE_COMPUTE_MODE to determine the compute mode of the device. The nvidia-smi tool can be used to set the compute mode for devices. Documentation for nvidia-smi can be obtained by passing a -h option to it.
Please note that the primary context always supports pinned allocations. Other flags can be specified by cuDevicePrimaryCtxSetFlags().
- Parameters
- devAny
Device for which primary context is requested
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_DEVICE CUDA_ERROR_INVALID_VALUE CUDA_ERROR_OUT_OF_MEMORY CUDA_ERROR_UNKNOWN
- pctxCUcontext
Returned context handle of the new context
- cuda.cuda.cuDevicePrimaryCtxRelease(dev)¶
Release the primary context on the GPU.
Releases the primary context interop on the device. A retained context should always be released once the user is done using it. The context is automatically reset once the last reference to it is released. This behavior is different when the primary context was retained by the CUDA runtime from CUDA 4.0 and earlier. In this case, the primary context remains always active.
Releasing a primary context that has not been previously retained will fail with CUDA_ERROR_INVALID_CONTEXT.
Please note that unlike cuCtxDestroy() this method does not pop the context from stack in any circumstances.
- Parameters
- devAny
Device which primary context is released
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_DEVICE CUDA_ERROR_INVALID_CONTEXT
- None
None
- cuda.cuda.cuDevicePrimaryCtxSetFlags(dev, unsigned int flags)¶
Set flags for the primary context.
Sets the flags for the primary context on the device overwriting perviously set ones.
The three LSBs of the flags parameter can be used to control how the OS thread, which owns the CUDA context at the time of an API call, interacts with the OS scheduler when waiting for results from the GPU. Only one of the scheduling flags can be set when creating a context.
CU_CTX_SCHED_SPIN: Instruct CUDA to actively spin when waiting for
results from the GPU. This can decrease latency when waiting for the GPU, but may lower the performance of CPU threads if they are performing work in parallel with the CUDA thread. - CU_CTX_SCHED_YIELD: Instruct CUDA to yield its thread when waiting for results from the GPU. This can increase latency when waiting for the GPU, but can increase the performance of CPU threads performing work in parallel with the GPU. - CU_CTX_SCHED_BLOCKING_SYNC: Instruct CUDA to block the CPU thread on a synchronization primitive when waiting for the GPU to finish work. - CU_CTX_BLOCKING_SYNC: Instruct CUDA to block the CPU thread on a synchronization primitive when waiting for the GPU to finish work. Deprecated: This flag was deprecated as of CUDA 4.0 and was replaced with CU_CTX_SCHED_BLOCKING_SYNC. - CU_CTX_SCHED_AUTO: The default value if the flags parameter is zero, uses a heuristic based on the number of active CUDA contexts in the process C and the number of logical processors in the system P. If C > P, then CUDA will yield to other OS threads when waiting for the GPU (CU_CTX_SCHED_YIELD), otherwise CUDA will not yield while waiting for results and actively spin on the processor (CU_CTX_SCHED_SPIN). Additionally, on Tegra devices, CU_CTX_SCHED_AUTO uses a heuristic based on the power profile of the platform and may choose CU_CTX_SCHED_BLOCKING_SYNC for low-powered devices. - CU_CTX_LMEM_RESIZE_TO_MAX: Instruct CUDA to not reduce local memory after resizing local memory for a kernel. This can prevent thrashing by local memory allocations when launching many kernels with high local memory usage at the cost of potentially increased memory usage. Deprecated: This flag is deprecated and the behavior enabled by this flag is now the default and cannot be disabled.
- Parameters
- devAny
Device for which the primary context flags are set
- flagsunsigned int
New flags for the device
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_DEVICE CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuDevicePrimaryCtxRetain
cuDevicePrimaryCtxGetState
cuCtxCreate
cuCtxGetFlags
cudaSetDeviceFlags
- cuda.cuda.cuDevicePrimaryCtxGetState(dev)¶
Get the state of the primary context.
Returns in *flags the flags for the primary context of dev, and in *active whether it is active. See cuDevicePrimaryCtxSetFlags for flag values.
- Parameters
- devAny
Device to get primary context flags for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_DEVICE CUDA_ERROR_INVALID_VALUE
- flagsunsigned int
Pointer to store flags
- activeint
Pointer to store context state; 0 = inactive, 1 = active
See also
cuDevicePrimaryCtxSetFlags
cuCtxGetFlags
cudaGetDeviceFlags
- cuda.cuda.cuDevicePrimaryCtxReset(dev)¶
Destroy all allocations and reset all state on the primary context.
Explicitly destroys and cleans up all resources associated with the current device in the current process.
Note that it is responsibility of the calling function to ensure that no other module in the process is using the device any more. For that reason it is recommended to use cuDevicePrimaryCtxRelease() in most cases. However it is safe for other modules to call cuDevicePrimaryCtxRelease() even after resetting the device. Resetting the primary context does not release it, an application that has retained the primary context should explicitly release its usage.
- Parameters
- devAny
Device for which primary context is destroyed
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_DEVICE CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE
- None
None
Context Management¶
This section describes the context management functions of the low-level CUDA driver application programming interface.
Please note that some functions are described in Primary Context Management section.
- cuda.cuda.cuCtxCreate(unsigned int flags, dev)¶
Create a CUDA context.
The three LSBs of the flags parameter can be used to control how the OS thread, which owns the CUDA context at the time of an API call, interacts with the OS scheduler when waiting for results from the GPU. Only one of the scheduling flags can be set when creating a context.
CU_CTX_SCHED_SPIN: Instruct CUDA to actively spin when waiting for
results from the GPU. This can decrease latency when waiting for the GPU, but may lower the performance of CPU threads if they are performing work in parallel with the CUDA thread. - CU_CTX_SCHED_YIELD: Instruct CUDA to yield its thread when waiting for results from the GPU. This can increase latency when waiting for the GPU, but can increase the performance of CPU threads performing work in parallel with the GPU. - CU_CTX_SCHED_BLOCKING_SYNC: Instruct CUDA to block the CPU thread on a synchronization primitive when waiting for the GPU to finish work. - CU_CTX_BLOCKING_SYNC: Instruct CUDA to block the CPU thread on a synchronization primitive when waiting for the GPU to finish work. Deprecated: This flag was deprecated as of CUDA 4.0 and was replaced with CU_CTX_SCHED_BLOCKING_SYNC. - CU_CTX_SCHED_AUTO: The default value if the flags parameter is zero, uses a heuristic based on the number of active CUDA contexts in the process C and the number of logical processors in the system P. If C > P, then CUDA will yield to other OS threads when waiting for the GPU (CU_CTX_SCHED_YIELD), otherwise CUDA will not yield while waiting for results and actively spin on the processor (CU_CTX_SCHED_SPIN). Additionally, on Tegra devices, CU_CTX_SCHED_AUTO uses a heuristic based on the power profile of the platform and may choose CU_CTX_SCHED_BLOCKING_SYNC for low-powered devices. - CU_CTX_MAP_HOST: Instruct CUDA to support mapped pinned allocations. This flag must be set in order to allocate pinned host memory that is accessible to the GPU. - CU_CTX_LMEM_RESIZE_TO_MAX: Instruct CUDA to not reduce local memory after resizing local memory for a kernel. This can prevent thrashing by local memory allocations when launching many kernels with high local memory usage at the cost of potentially increased memory usage. Deprecated: This flag is deprecated and the behavior enabled by this flag is now the default and cannot be disabled. Instead, the per-thread stack size can be controlled with cuCtxSetLimit().
Context creation will fail with CUDA_ERROR_UNKNOWN if the compute mode of the device is CU_COMPUTEMODE_PROHIBITED. The function cuDeviceGetAttribute() can be used with CU_DEVICE_ATTRIBUTE_COMPUTE_MODE to determine the compute mode of the device. The nvidia-smi tool can be used to set the compute mode for * devices. Documentation for nvidia-smi can be obtained by passing a -h option to it.
- Parameters
- flagsunsigned int
Context creation flags
- devAny
Device to create context on
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_DEVICE CUDA_ERROR_INVALID_VALUE CUDA_ERROR_OUT_OF_MEMORY CUDA_ERROR_UNKNOWN
- pctxCUcontext
Returned context handle of the new context
See also
Notes
In most cases it is recommended to use cuDevicePrimaryCtxRetain.
- cuda.cuda.cuCtxCreate_v3(paramsArray: List[CUexecAffinityParam], int numParams, unsigned int flags, dev)¶
Create a CUDA context with execution affinity.
Creates a new CUDA context with execution affinity and associates it with the calling thread. The paramsArray and flags parameter are described below. The context is created with a usage count of 1 and the caller of cuCtxCreate() must call cuCtxDestroy() or when done using the context. If a context is already current to the thread, it is supplanted by the newly created context and may be restored by a subsequent call to cuCtxPopCurrent().
The type and the amount of execution resource the context can use is limited by paramsArray and numParams. The paramsArray is an array of CUexecAffinityParam and the numParams describes the size of the array. If two CUexecAffinityParam in the array have the same type, the latter execution affinity parameter overrides the former execution affinity parameter. The supported execution affinity types are: - CU_EXEC_AFFINITY_TYPE_SM_COUNT limits the portion of SMs that the context can use. The portion of SMs is specified as the number of SMs via CUexecAffinitySmCount. This limit will be internally rounded up to the next hardware-supported amount. Hence, it is imperative to query the actual execution affinity of the context via cuCtxGetExecAffinity after context creation. Currently, this attribute is only supported under Volta+ MPS.
The three LSBs of the flags parameter can be used to control how the OS thread, which owns the CUDA context at the time of an API call, interacts with the OS scheduler when waiting for results from the GPU. Only one of the scheduling flags can be set when creating a context.
CU_CTX_SCHED_SPIN: Instruct CUDA to actively spin when waiting for
results from the GPU. This can decrease latency when waiting for the GPU, but may lower the performance of CPU threads if they are performing work in parallel with the CUDA thread. - CU_CTX_SCHED_YIELD: Instruct CUDA to yield its thread when waiting for results from the GPU. This can increase latency when waiting for the GPU, but can increase the performance of CPU threads performing work in parallel with the GPU. - CU_CTX_SCHED_BLOCKING_SYNC: Instruct CUDA to block the CPU thread on a synchronization primitive when waiting for the GPU to finish work. - CU_CTX_BLOCKING_SYNC: Instruct CUDA to block the CPU thread on a synchronization primitive when waiting for the GPU to finish work. Deprecated: This flag was deprecated as of CUDA 4.0 and was replaced with CU_CTX_SCHED_BLOCKING_SYNC. - CU_CTX_SCHED_AUTO: The default value if the flags parameter is zero, uses a heuristic based on the number of active CUDA contexts in the process C and the number of logical processors in the system P. If C > P, then CUDA will yield to other OS threads when waiting for the GPU (CU_CTX_SCHED_YIELD), otherwise CUDA will not yield while waiting for results and actively spin on the processor (CU_CTX_SCHED_SPIN). Additionally, on Tegra devices, CU_CTX_SCHED_AUTO uses a heuristic based on the power profile of the platform and may choose CU_CTX_SCHED_BLOCKING_SYNC for low-powered devices. - CU_CTX_MAP_HOST: Instruct CUDA to support mapped pinned allocations. This flag must be set in order to allocate pinned host memory that is accessible to the GPU. - CU_CTX_LMEM_RESIZE_TO_MAX: Instruct CUDA to not reduce local memory after resizing local memory for a kernel. This can prevent thrashing by local memory allocations when launching many kernels with high local memory usage at the cost of potentially increased memory usage. Deprecated: This flag is deprecated and the behavior enabled by this flag is now the default and cannot be disabled. Instead, the per-thread stack size can be controlled with cuCtxSetLimit().
Context creation will fail with CUDA_ERROR_UNKNOWN if the compute mode of the device is CU_COMPUTEMODE_PROHIBITED. The function cuDeviceGetAttribute() can be used with CU_DEVICE_ATTRIBUTE_COMPUTE_MODE to determine the compute mode of the device. The nvidia-smi tool can be used to set the compute mode for * devices. Documentation for nvidia-smi can be obtained by passing a -h option to it.
- Parameters
- paramsArrayList[CUexecAffinityParam]
Execution affinity parameters
- numParamsint
Number of execution affinity parameters
- flagsunsigned int
Context creation flags
- devAny
Device to create context on
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_DEVICE CUDA_ERROR_INVALID_VALUE CUDA_ERROR_OUT_OF_MEMORY CUDA_ERROR_UNSUPPORTED_EXEC_AFFINITY CUDA_ERROR_UNKNOWN
- pctxCUcontext
Returned context handle of the new context
- cuda.cuda.cuCtxDestroy(ctx)¶
Destroy a CUDA context.
Destroys the CUDA context specified by ctx. The context ctx will be destroyed regardless of how many threads it is current to. It is the responsibility of the calling function to ensure that no API call issues using ctx while cuCtxDestroy() is executing.
Destroys and cleans up all resources associated with the context. It is the caller’s responsibility to ensure that the context or its resources are not accessed or passed in subsequent API calls and doing so will result in undefined behavior. These resources include CUDA types such as CUmodule, CUfunction, CUstream, CUevent, CUarray, CUmipmappedArray, CUtexObject, CUsurfObject, CUtexref, CUsurfref, CUgraphicsResource, CUlinkState, CUexternalMemory and CUexternalSemaphore.
If ctx is current to the calling thread then ctx will also be popped from the current thread’s context stack (as though cuCtxPopCurrent() were called). If ctx is current to other threads, then ctx will remain current to those threads, and attempting to access ctx from those threads will result in the error CUDA_ERROR_CONTEXT_IS_DESTROYED.
- Parameters
- ctxAny
Context to destroy
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
- cuda.cuda.cuCtxPushCurrent(ctx)¶
Pushes a context on the current CPU thread.
Pushes the given context ctx onto the CPU thread’s stack of current contexts. The specified context becomes the CPU thread’s current context, so all CUDA functions that operate on the current context are affected.
The previous current context may be made current again by calling cuCtxDestroy() or cuCtxPopCurrent().
- Parameters
- ctxAny
Context to push
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
- cuda.cuda.cuCtxPopCurrent()¶
Pops the current CUDA context from the current CPU thread.
Pops the current CUDA context from the CPU thread and passes back the old context handle in *pctx. That context may then be made current to a different CPU thread by calling cuCtxPushCurrent().
If a context was current to the CPU thread before cuCtxCreate() or cuCtxPushCurrent() was called, this function makes that context current to the CPU thread again.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT
- pctxCUcontext
Returned popped context handle
- cuda.cuda.cuCtxSetCurrent(ctx)¶
Binds the specified CUDA context to the calling CPU thread.
Binds the specified CUDA context to the calling CPU thread. If ctx is NULL then the CUDA context previously bound to the calling CPU thread is unbound and CUDA_SUCCESS is returned.
If there exists a CUDA context stack on the calling CPU thread, this will replace the top of that stack with ctx. If ctx is NULL then this will be equivalent to popping the top of the calling CPU thread’s CUDA context stack (or a no-op if the calling CPU thread’s CUDA context stack is empty).
- Parameters
- ctxAny
Context to bind to the calling CPU thread
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT
- None
None
See also
cuCtxGetCurrent
cuCtxCreate
cuCtxDestroy
cudaSetDevice
- cuda.cuda.cuCtxGetCurrent()¶
Returns the CUDA context bound to the calling CPU thread.
Returns in *pctx the CUDA context bound to the calling CPU thread. If no context is bound to the calling CPU thread then *pctx is set to NULL and CUDA_SUCCESS is returned.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED
- pctxCUcontext
Returned context handle
See also
cuCtxSetCurrent
cuCtxCreate
cuCtxDestroy
cudaGetDevice
- cuda.cuda.cuCtxGetDevice()¶
Returns the device ID for the current context.
Returns in *device the ordinal of the current context’s device.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- deviceCUdevice
Returned device ID for the current context
- cuda.cuda.cuCtxGetFlags()¶
Returns the flags for the current context.
Returns in *flags the flags of the current context. See cuCtxCreate for flag values.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- flagsunsigned int
Pointer to store flags of current context
See also
- cuda.cuda.cuCtxSynchronize()¶
Block for a context’s tasks to complete.
Blocks until the device has completed all preceding requested tasks. cuCtxSynchronize() returns an error if one of the preceding tasks failed. If the context was created with the CU_CTX_SCHED_BLOCKING_SYNC flag, the CPU thread will block until the GPU context has finished its work.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT
- None
None
See also
- cuda.cuda.cuCtxSetLimit(limit: CUlimit, size_t value)¶
Set resource limits.
Setting limit to value is a request by the application to update the current limit maintained by the context. The driver is free to modify the requested value to meet h/w requirements (this could be clamping to minimum or maximum values, rounding up to nearest element size, etc). The application can use cuCtxGetLimit() to find out exactly what the limit has been set to.
Setting each CUlimit has its own specific restrictions, so each is discussed here.
CU_LIMIT_STACK_SIZE controls the stack size in bytes of each GPU
thread. The driver automatically increases the per-thread stack size for each kernel launch as needed. This size isn’t reset back to the original value after each launch. Setting this value will take effect immediately, and if necessary, the device will block until all preceding requested tasks are complete. - CU_LIMIT_PRINTF_FIFO_SIZE controls the size in bytes of the FIFO used by the printf() device system call. Setting CU_LIMIT_PRINTF_FIFO_SIZE must be performed before launching any kernel that uses the printf() device system call, otherwise CUDA_ERROR_INVALID_VALUE will be returned. - CU_LIMIT_MALLOC_HEAP_SIZE controls the size in bytes of the heap used by the malloc() and free() device system calls. Setting CU_LIMIT_MALLOC_HEAP_SIZE must be performed before launching any kernel that uses the malloc() or free() device system calls, otherwise CUDA_ERROR_INVALID_VALUE will be returned. - CU_LIMIT_DEV_RUNTIME_SYNC_DEPTH controls the maximum nesting depth of a grid at which a thread can safely call cudaDeviceSynchronize(). Setting this limit must be performed before any launch of a kernel that uses the device runtime and calls cudaDeviceSynchronize() above the default sync depth, two levels of grids. Calls to cudaDeviceSynchronize() will fail with error code cudaErrorSyncDepthExceeded if the limitation is violated. This limit can be set smaller than the default or up the maximum launch depth of 24. When setting this limit, keep in mind that additional levels of sync depth require the driver to reserve large amounts of device memory which can no longer be used for user allocations. If these reservations of device memory fail, cuCtxSetLimit() will return CUDA_ERROR_OUT_OF_MEMORY, and the limit can be reset to a lower value. This limit is only applicable to devices of compute capability 3.5 and higher. Attempting to set this limit on devices of compute capability less than 3.5 will result in the error CUDA_ERROR_UNSUPPORTED_LIMIT being returned. - CU_LIMIT_DEV_RUNTIME_PENDING_LAUNCH_COUNT controls the maximum number of outstanding device runtime launches that can be made from the current context. A grid is outstanding from the point of launch up until the grid is known to have been completed. Device runtime launches which violate this limitation fail and return cudaErrorLaunchPendingCountExceeded when cudaGetLastError() is called after launch. If more pending launches than the default (2048 launches) are needed for a module using the device runtime, this limit can be increased. Keep in mind that being able to sustain additional pending launches will require the driver to reserve larger amounts of device memory upfront which can no longer be used for allocations. If these reservations fail, cuCtxSetLimit() will return CUDA_ERROR_OUT_OF_MEMORY, and the limit can be reset to a lower value. This limit is only applicable to devices of compute capability 3.5 and higher. Attempting to set this limit on devices of compute capability less than 3.5 will result in the error CUDA_ERROR_UNSUPPORTED_LIMIT being returned. - CU_LIMIT_MAX_L2_FETCH_GRANULARITY controls the L2 cache fetch granularity. Values can range from 0B to 128B. This is purely a performence hint and it can be ignored or clamped depending on the platform. - CU_LIMIT_PERSISTING_L2_CACHE_SIZE controls size in bytes availabe for persisting L2 cache. This is purely a performance hint and it can be ignored or clamped depending on the platform.
- Parameters
- limitCUlimit
Limit to set
- valuesize_t
Size of limit
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_UNSUPPORTED_LIMIT CUDA_ERROR_OUT_OF_MEMORY CUDA_ERROR_INVALID_CONTEXT
- None
None
- cuda.cuda.cuCtxGetLimit(limit: CUlimit)¶
Returns resource limits.
Returns in *pvalue the current size of limit. The supported CUlimit values are: - CU_LIMIT_STACK_SIZE: stack size in bytes of each GPU thread. - CU_LIMIT_PRINTF_FIFO_SIZE: size in bytes of the FIFO used by the printf() device system call. - CU_LIMIT_MALLOC_HEAP_SIZE: size in bytes of the heap used by the malloc() and free() device system calls. - CU_LIMIT_DEV_RUNTIME_SYNC_DEPTH: maximum grid depth at which a thread can issue the device runtime call cudaDeviceSynchronize() to wait on child grid launches to complete. - CU_LIMIT_DEV_RUNTIME_PENDING_LAUNCH_COUNT: maximum number of outstanding device runtime launches that can be made from this context. - CU_LIMIT_MAX_L2_FETCH_GRANULARITY: L2 cache fetch granularity. - CU_LIMIT_PERSISTING_L2_CACHE_SIZE: Persisting L2 cache size in bytes
- Parameters
- limitCUlimit
Limit to query
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_UNSUPPORTED_LIMIT
- pvalueint
Returned size of limit
- cuda.cuda.cuCtxGetCacheConfig()¶
Returns the preferred cache configuration for the current context.
On devices where the L1 cache and shared memory use the same hardware resources, this function returns through pconfig the preferred cache configuration for the current context. This is only a preference. The driver will use the requested configuration if possible, but it is free to choose a different configuration if required to execute functions.
This will return a pconfig of CU_FUNC_CACHE_PREFER_NONE on devices where the size of the L1 cache and shared memory are fixed.
The supported cache configurations are: - CU_FUNC_CACHE_PREFER_NONE: no preference for shared memory or L1 (default) - CU_FUNC_CACHE_PREFER_SHARED: prefer larger shared memory and smaller L1 cache - CU_FUNC_CACHE_PREFER_L1: prefer larger L1 cache and smaller shared memory - CU_FUNC_CACHE_PREFER_EQUAL: prefer equal sized L1 cache and shared memory
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- pconfigCUfunc_cache
Returned cache configuration
See also
- cuda.cuda.cuCtxSetCacheConfig(config: CUfunc_cache)¶
Sets the preferred cache configuration for the current context.
On devices where the L1 cache and shared memory use the same hardware resources, this sets through config the preferred cache configuration for the current context. This is only a preference. The driver will use the requested configuration if possible, but it is free to choose a different configuration if required to execute the function. Any function preference set via cuFuncSetCacheConfig() will be preferred over this context-wide setting. Setting the context-wide cache configuration to CU_FUNC_CACHE_PREFER_NONE will cause subsequent kernel launches to prefer to not change the cache configuration unless required to launch the kernel.
This setting does nothing on devices where the size of the L1 cache and shared memory are fixed.
Launching a kernel with a different preference than the most recent preference setting may insert a device-side synchronization point.
The supported cache configurations are: - CU_FUNC_CACHE_PREFER_NONE: no preference for shared memory or L1 (default) - CU_FUNC_CACHE_PREFER_SHARED: prefer larger shared memory and smaller L1 cache - CU_FUNC_CACHE_PREFER_L1: prefer larger L1 cache and smaller shared memory - CU_FUNC_CACHE_PREFER_EQUAL: prefer equal sized L1 cache and shared memory
- Parameters
- configCUfunc_cache
Requested cache configuration
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
Returns the current shared memory configuration for the current context.
This function will return in pConfig the current size of shared memory banks in the current context. On devices with configurable shared memory banks, cuCtxSetSharedMemConfig can be used to change this setting, so that all subsequent kernel launches will by default use the new bank size. When cuCtxGetSharedMemConfig is called on devices without configurable shared memory, it will return the fixed bank size of the hardware.
The returned bank configurations can be either: - CU_SHARED_MEM_CONFIG_FOUR_BYTE_BANK_SIZE: shared memory bank width is four bytes. - CU_SHARED_MEM_CONFIG_EIGHT_BYTE_BANK_SIZE: shared memory bank width will eight bytes.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- pConfigCUsharedconfig
returned shared memory configuration
Sets the shared memory configuration for the current context.
On devices with configurable shared memory banks, this function will set the context’s shared memory bank size which is used for subsequent kernel launches.
Changed the shared memory configuration between launches may insert a device side synchronization point between those launches.
Changing the shared memory bank size will not increase shared memory usage or affect occupancy of kernels, but may have major effects on performance. Larger bank sizes will allow for greater potential bandwidth to shared memory, but will change what kinds of accesses to shared memory will result in bank conflicts.
This function will do nothing on devices with fixed shared memory bank size.
The supported bank configurations are: - CU_SHARED_MEM_CONFIG_DEFAULT_BANK_SIZE: set bank width to the default initial setting (currently, four bytes). - CU_SHARED_MEM_CONFIG_FOUR_BYTE_BANK_SIZE: set shared memory bank width to be natively four bytes. - CU_SHARED_MEM_CONFIG_EIGHT_BYTE_BANK_SIZE: set shared memory bank width to be natively eight bytes.
- Parameters
- configCUsharedconfig
requested shared memory configuration
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
- cuda.cuda.cuCtxGetApiVersion(ctx)¶
Gets the context’s API version.
Returns a version number in version corresponding to the capabilities of the context (e.g. 3010 or 3020), which library developers can use to direct callers to a specific API version. If ctx is NULL, returns the API version used to create the currently bound context.
Note that new API versions are only introduced when context capabilities are changed that break binary compatibility, so the API version and driver version may be different. For example, it is valid for the API version to be 3020 while the driver version is 4020.
- Parameters
- ctxAny
Context to check
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_UNKNOWN
- versionunsigned int
Pointer to version
- cuda.cuda.cuCtxGetStreamPriorityRange()¶
Returns numerical values that correspond to the least and greatest stream priorities.
Returns in *leastPriority and *greatestPriority the numerical values that correspond to the least and greatest stream priorities respectively. Stream priorities follow a convention where lower numbers imply greater priorities. The range of meaningful stream priorities is given by [*greatestPriority, *leastPriority]. If the user attempts to create a stream with a priority value that is outside the meaningful range as specified by this API, the priority is automatically clamped down or up to either *leastPriority or *greatestPriority respectively. See cuStreamCreateWithPriority for details on creating a priority stream. A NULL may be passed in for *leastPriority or *greatestPriority if the value is not desired.
This function will return ‘0’ in both *leastPriority and *greatestPriority if the current context’s device does not support stream priorities (see cuDeviceGetAttribute).
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- leastPriorityint
Pointer to an int in which the numerical value for least stream priority is returned
- greatestPriorityint
Pointer to an int in which the numerical value for greatest stream priority is returned
See also
cuStreamCreateWithPriority
cuStreamGetPriority
cuCtxGetDevice
cuCtxGetFlags
cuCtxSetLimit
cuCtxSynchronize
cudaDeviceGetStreamPriorityRange
- cuda.cuda.cuCtxResetPersistingL2Cache()¶
Resets all persisting lines in cache to normal status.
cuCtxResetPersistingL2Cache Resets all persisting lines in cache to normal status. Takes effect on function return.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_NOT_SUPPORTED
- None
None
- cuda.cuda.cuCtxGetExecAffinity(typename: CUexecAffinityType)¶
Returns the execution affinity setting for the current context.
Returns in *pExecAffinity the current value of typename. The supported CUexecAffinityType values are: - CU_EXEC_AFFINITY_TYPE_SM_COUNT: number of SMs the context is limited to use.
- Parameters
- typenameCUexecAffinityType
Execution affinity type to query
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_UNSUPPORTED_EXEC_AFFINITY
- pExecAffinityCUexecAffinityParam
Returned execution affinity
Module Management¶
This section describes the module management functions of the low-level CUDA driver application programming interface.
- cuda.cuda.cuModuleLoad(char *fname)¶
Loads a compute module.
Takes a filename fname and loads the corresponding module module into the current context. The CUDA driver API does not attempt to lazily allocate the resources needed by a module; if the memory for functions and data (constant and global) needed by the module cannot be allocated, cuModuleLoad() fails. The file should be a cubin file as output by nvcc, or a PTX file either as output by nvcc or handwritten, or a fatbin file as output by nvcc from toolchain 4.0 or later.
- Parameters
- fnamebytes
Filename of module to load
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_PTX CUDA_ERROR_UNSUPPORTED_PTX_VERSION CUDA_ERROR_NOT_FOUND CUDA_ERROR_OUT_OF_MEMORY CUDA_ERROR_FILE_NOT_FOUND CUDA_ERROR_NO_BINARY_FOR_GPU CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND CUDA_ERROR_SHARED_OBJECT_INIT_FAILED CUDA_ERROR_JIT_COMPILER_NOT_FOUND
- moduleCUmodule
Returned module
- cuda.cuda.cuModuleLoadData(image)¶
Load a module’s data.
Takes a pointer image and loads the corresponding module module into the current context. The pointer may be obtained by mapping a cubin or PTX or fatbin file, passing a cubin or PTX or fatbin file as a NULL-terminated text string, or incorporating a cubin or fatbin object into the executable resources and using operating system calls such as Windows FindResource() to obtain the pointer.
- Parameters
- imageAny
Module data to load
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_PTX CUDA_ERROR_UNSUPPORTED_PTX_VERSION CUDA_ERROR_OUT_OF_MEMORY CUDA_ERROR_NO_BINARY_FOR_GPU CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND CUDA_ERROR_SHARED_OBJECT_INIT_FAILED CUDA_ERROR_JIT_COMPILER_NOT_FOUND
- moduleCUmodule
Returned module
- cuda.cuda.cuModuleLoadDataEx(image, unsigned int numOptions, options: List[CUjit_option], optionValues: List[Any])¶
Load a module’s data with options.
Takes a pointer image and loads the corresponding module module into the current context. The pointer may be obtained by mapping a cubin or PTX or fatbin file, passing a cubin or PTX or fatbin file as a NULL-terminated text string, or incorporating a cubin or fatbin object into the executable resources and using operating system calls such as Windows FindResource() to obtain the pointer. Options are passed as an array via options and any corresponding parameters are passed in optionValues. The number of total options is supplied via numOptions. Any outputs will be returned via optionValues.
- Parameters
- imageAny
Module data to load
- numOptionsunsigned int
Number of options
- optionsList[CUjit_option]
Options for JIT
- optionValuesList[Any]
Option values for JIT
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_PTX CUDA_ERROR_UNSUPPORTED_PTX_VERSION CUDA_ERROR_OUT_OF_MEMORY CUDA_ERROR_NO_BINARY_FOR_GPU CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND CUDA_ERROR_SHARED_OBJECT_INIT_FAILED CUDA_ERROR_JIT_COMPILER_NOT_FOUND
- moduleCUmodule
Returned module
- cuda.cuda.cuModuleLoadFatBinary(fatCubin)¶
Load a module’s data.
Takes a pointer fatCubin and loads the corresponding module module into the current context. The pointer represents a fat binary object, which is a collection of different cubin and/or PTX files, all representing the same device code, but compiled and optimized for different architectures.
Prior to CUDA 4.0, there was no documented API for constructing and using fat binary objects by programmers. Starting with CUDA 4.0, fat binary objects can be constructed by providing the -fatbin option to nvcc. More information can be found in the nvcc document.
- Parameters
- fatCubinAny
Fat binary to load
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_PTX CUDA_ERROR_UNSUPPORTED_PTX_VERSION CUDA_ERROR_NOT_FOUND CUDA_ERROR_OUT_OF_MEMORY CUDA_ERROR_NO_BINARY_FOR_GPU CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND CUDA_ERROR_SHARED_OBJECT_INIT_FAILED CUDA_ERROR_JIT_COMPILER_NOT_FOUND
- moduleCUmodule
Returned module
- cuda.cuda.cuModuleUnload(hmod)¶
Unloads a module.
Unloads a module hmod from the current context.
- Parameters
- hmodAny
Module to unload
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
- cuda.cuda.cuModuleGetFunction(hmod, char *name)¶
Returns a function handle.
Returns in *hfunc the handle of the function of name name located in module hmod. If no function of that name exists, cuModuleGetFunction() returns CUDA_ERROR_NOT_FOUND.
- Parameters
- hmodAny
Module to retrieve function from
- namebytes
Name of function to retrieve
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_FOUND
- hfuncCUfunction
Returned function handle
- cuda.cuda.cuModuleGetGlobal(hmod, char *name)¶
Returns a global pointer from a module.
Returns in *dptr and *bytes the base pointer and size of the global of name name located in module hmod. If no variable of that name exists, cuModuleGetGlobal() returns CUDA_ERROR_NOT_FOUND. Both parameters dptr and numbytes are optional. If one of them is NULL, it is ignored.
- Parameters
- hmodAny
Module to retrieve global from
- namebytes
Name of global to retrieve
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_FOUND
- dptrCUdeviceptr
Returned global device pointer
- numbytesint
Returned global size in bytes
See also
cuModuleGetFunction
cuModuleGetTexRef
cuModuleLoad
cuModuleLoadData
cuModuleLoadDataEx
cuModuleLoadFatBinary
cuModuleUnload
cudaGetSymbolAddress
cudaGetSymbolSize
- cuda.cuda.cuModuleGetTexRef(hmod, char *name)¶
Returns a handle to a texture reference.
Returns in *pTexRef the handle of the texture reference of name name in the module hmod. If no texture reference of that name exists, cuModuleGetTexRef() returns CUDA_ERROR_NOT_FOUND. This texture reference handle should not be destroyed, since it will be destroyed when the module is unloaded.
- Parameters
- hmodAny
Module to retrieve texture reference from
- namebytes
Name of texture reference to retrieve
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_FOUND
- pTexRefCUtexref
Returned texture reference
See also
- cuda.cuda.cuModuleGetSurfRef(hmod, char *name)¶
Returns a handle to a surface reference.
Returns in *pSurfRef the handle of the surface reference of name name in the module hmod. If no surface reference of that name exists, cuModuleGetSurfRef() returns CUDA_ERROR_NOT_FOUND.
- Parameters
- hmodAny
Module to retrieve surface reference from
- namebytes
Name of surface reference to retrieve
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_FOUND
- pSurfRefCUsurfref
Returned surface reference
See also
- cuda.cuda.cuLinkCreate(unsigned int numOptions, options: List[CUjit_option], optionValues: List[Any])¶
Creates a pending JIT linker invocation.
If the call is successful, the caller owns the returned CUlinkState, which should eventually be destroyed with cuLinkDestroy. The device code machine size (32 or 64 bit) will match the calling application.
Both linker and compiler options may be specified. Compiler options will be applied to inputs to this linker action which must be compiled from PTX. The options CU_JIT_WALL_TIME, CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES, and CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES will accumulate data until the CUlinkState is destroyed.
optionValues must remain valid for the life of the CUlinkState if output options are used. No other references to inputs are maintained after this call returns.
- Parameters
- numOptionsunsigned int
Size of options arrays
- optionsList[CUjit_option]
Array of linker and compiler options
- optionValuesList[Any]
Array of option values, each cast to void *
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_OUT_OF_MEMORY CUDA_ERROR_JIT_COMPILER_NOT_FOUND
- stateOutCUlinkState
On success, this will contain a CUlinkState to specify and complete this action
- cuda.cuda.cuLinkAddData(state, typename: CUjitInputType, data, size_t size, char *name, unsigned int numOptions, options: List[CUjit_option], optionValues: List[Any])¶
Add an input to a pending linker invocation.
Ownership of data is retained by the caller. No reference is retained to any inputs after this call returns.
This method accepts only compiler options, which are used if the data must be compiled from PTX, and does not accept any of CU_JIT_WALL_TIME, CU_JIT_INFO_LOG_BUFFER, CU_JIT_ERROR_LOG_BUFFER, CU_JIT_TARGET_FROM_CUCONTEXT, or CU_JIT_TARGET.
- Parameters
- stateAny
A pending linker action.
- typenameCUjitInputType
The type of the input data.
- dataAny
The input data. PTX must be NULL-terminated.
- sizesize_t
The length of the input data.
- namebytes
An optional name for this input in log messages.
- numOptionsunsigned int
Size of options.
- optionsList[CUjit_option]
Options to be applied only for this input (overrides options from cuLinkCreate).
- optionValuesList[Any]
Array of option values, each cast to void *.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_IMAGE CUDA_ERROR_INVALID_PTX CUDA_ERROR_UNSUPPORTED_PTX_VERSION CUDA_ERROR_OUT_OF_MEMORY CUDA_ERROR_NO_BINARY_FOR_GPU
- None
None
- cuda.cuda.cuLinkAddFile(state, typename: CUjitInputType, char *path, unsigned int numOptions, options: List[CUjit_option], optionValues: List[Any])¶
Add a file input to a pending linker invocation.
No reference is retained to any inputs after this call returns.
This method accepts only compiler options, which are used if the input must be compiled from PTX, and does not accept any of CU_JIT_WALL_TIME, CU_JIT_INFO_LOG_BUFFER, CU_JIT_ERROR_LOG_BUFFER, CU_JIT_TARGET_FROM_CUCONTEXT, or CU_JIT_TARGET.
This method is equivalent to invoking cuLinkAddData on the contents of the file.
- Parameters
- stateAny
A pending linker action
- typenameCUjitInputType
The type of the input data
- pathbytes
Path to the input file
- numOptionsunsigned int
Size of options
- optionsList[CUjit_option]
Options to be applied only for this input (overrides options from cuLinkCreate)
- optionValuesList[Any]
Array of option values, each cast to void *
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_FILE_NOT_FOUND CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_IMAGE CUDA_ERROR_INVALID_PTX CUDA_ERROR_UNSUPPORTED_PTX_VERSION CUDA_ERROR_OUT_OF_MEMORY CUDA_ERROR_NO_BINARY_FOR_GPU
- None
None
- cuda.cuda.cuLinkComplete(state)¶
Complete a pending linker invocation.
Completes the pending linker action and returns the cubin image for the linked device code, which can be used with cuModuleLoadData. The cubin is owned by state, so it should be loaded before state is destroyed via cuLinkDestroy. This call does not destroy state.
- Parameters
- stateAny
A pending linker invocation
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_OUT_OF_MEMORY
- cubinOutint
On success, this will point to the output image
- sizeOutint
Optional parameter to receive the size of the generated image
- cuda.cuda.cuLinkDestroy(state)¶
Destroys state for a JIT linker invocation.
- Parameters
- stateAny
State object for the linker invocation
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_HANDLE
- None
None
See also
Memory Management¶
This section describes the memory management functions of the low-level CUDA driver application programming interface.
- cuda.cuda.cuMemGetInfo()¶
Gets free and total memory.
Returns in *total the total amount of memory available to the the current context. Returns in *free the amount of memory on the device that is free according to the OS. CUDA is not guaranteed to be able to allocate all of the memory that the OS reports as free. In a multi- tenet situation, free estimate returned is prone to race condition where a new allocation/free done by a different process or a different thread in the same process between the time when free memory was estimated and reported, will result in deviation in free value reported and actual free memory.
The integrated GPU on Tegra shares memory with CPU and other component of the SoC. The free and total values returned by the API excludes the SWAP memory space maintained by the OS on some platforms. The OS may move some of the memory pages into swap area as the GPU or CPU allocate or access memory. See Tegra app note on how to calculate total and free memory on Tegra.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- freeint
Returned free memory in bytes
- totalint
Returned total memory in bytes
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cudaMemGetInfo
- cuda.cuda.cuMemAlloc(size_t bytesize)¶
Allocates device memory.
Allocates bytesize bytes of linear memory on the device and returns in *dptr a pointer to the allocated memory. The allocated memory is suitably aligned for any kind of variable. The memory is not cleared. If bytesize is 0, cuMemAlloc() returns CUDA_ERROR_INVALID_VALUE.
- Parameters
- bytesizesize_t
Requested allocation size in bytes
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_OUT_OF_MEMORY
- dptrCUdeviceptr
Returned device pointer
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cudaMalloc
- cuda.cuda.cuMemAllocPitch(size_t WidthInBytes, size_t Height, unsigned int ElementSizeBytes)¶
Allocates pitched device memory.
Allocates at least WidthInBytes * Height bytes of linear memory on the device and returns in *dptr a pointer to the allocated memory. The function may pad the allocation to ensure that corresponding pointers in any given row will continue to meet the alignment requirements for coalescing as the address is updated from row to row. ElementSizeBytes specifies the size of the largest reads and writes that will be performed on the memory range. ElementSizeBytes may be 4, 8 or 16 (since coalesced memory transactions are not possible on other data sizes). If ElementSizeBytes is smaller than the actual read/write size of a kernel, the kernel will run correctly, but possibly at reduced speed. The pitch returned in *pPitch by cuMemAllocPitch() is the width in bytes of the allocation. The intended usage of pitch is as a separate parameter of the allocation, used to compute addresses within the 2D array. Given the row and column of an array element of type T, the address is computed as: T*pElement=(T*)((char*)BaseAddress+Row*Pitch)+Column;
The pitch returned by cuMemAllocPitch() is guaranteed to work with cuMemcpy2D() under all circumstances. For allocations of 2D arrays, it is recommended that programmers consider performing pitch allocations using cuMemAllocPitch(). Due to alignment restrictions in the hardware, this is especially true if the application will be performing 2D memory copies between different regions of device memory (whether linear memory or CUDA arrays).
The byte alignment of the pitch returned by cuMemAllocPitch() is guaranteed to match or exceed the alignment requirement for texture binding with cuTexRefSetAddress2D().
- Parameters
- WidthInBytessize_t
Requested allocation width in bytes
- Heightsize_t
Requested allocation height in rows
- ElementSizeBytesunsigned int
Size of largest reads/writes for range
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_OUT_OF_MEMORY
- dptrCUdeviceptr
Returned device pointer
- pPitchint
Returned pitch of allocation in bytes
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cudaMallocPitch
- cuda.cuda.cuMemFree(dptr)¶
Frees device memory.
Frees the memory space pointed to by dptr, which must have been returned by a previous call to cuMemAlloc() or cuMemAllocPitch().
- Parameters
- dptrAny
Pointer to memory to free
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cudaFree
- cuda.cuda.cuMemGetAddressRange(dptr)¶
Get information on memory allocations.
Returns the base address in *pbase and size in *psize of the allocation by cuMemAlloc() or cuMemAllocPitch() that contains the input pointer dptr. Both parameters pbase and psize are optional. If one of them is NULL, it is ignored.
- Parameters
- dptrAny
Device pointer to query
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_NOT_FOUND CUDA_ERROR_INVALID_VALUE
- pbaseCUdeviceptr
Returned base address
- psizeint
Returned size of device memory allocation
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
- cuda.cuda.cuMemAllocHost(size_t bytesize)¶
Allocates page-locked host memory.
Allocates bytesize bytes of host memory that is page-locked and accessible to the device. The driver tracks the virtual memory ranges allocated with this function and automatically accelerates calls to functions such as cuMemcpy(). Since the memory can be accessed directly by the device, it can be read or written with much higher bandwidth than pageable memory obtained with functions such as malloc(). Allocating excessive amounts of memory with cuMemAllocHost() may degrade system performance, since it reduces the amount of memory available to the system for paging. As a result, this function is best used sparingly to allocate staging areas for data exchange between host and device.
Note all host memory allocated using cuMemHostAlloc() will automatically be immediately accessible to all contexts on all devices which support unified addressing (as may be queried using CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING). The device pointer that may be used to access this host memory from those contexts is always equal to the returned host pointer *pp. See Unified Addressing for additional details.
- Parameters
- bytesizesize_t
Requested allocation size in bytes
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_OUT_OF_MEMORY
- ppint
Returned host pointer to page-locked memory
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cudaMallocHost
- cuda.cuda.cuMemFreeHost(p)¶
Frees page-locked host memory.
Frees the memory space pointed to by p, which must have been returned by a previous call to cuMemAllocHost().
- Parameters
- pAny
Pointer to memory to free
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cudaFreeHost
- cuda.cuda.cuMemHostAlloc(size_t bytesize, unsigned int Flags)¶
Allocates page-locked host memory.
Allocates bytesize bytes of host memory that is page-locked and accessible to the device. The driver tracks the virtual memory ranges allocated with this function and automatically accelerates calls to functions such as cuMemcpyHtoD(). Since the memory can be accessed directly by the device, it can be read or written with much higher bandwidth than pageable memory obtained with functions such as malloc(). Allocating excessive amounts of pinned memory may degrade system performance, since it reduces the amount of memory available to the system for paging. As a result, this function is best used sparingly to allocate staging areas for data exchange between host and device.
The Flags parameter enables different options to be specified that affect the allocation, as follows.
CU_MEMHOSTALLOC_PORTABLE: The memory returned by this call will be
considered as pinned memory by all CUDA contexts, not just the one that performed the allocation. - CU_MEMHOSTALLOC_DEVICEMAP: Maps the allocation into the CUDA address space. The device pointer to the memory may be obtained by calling cuMemHostGetDevicePointer(). - CU_MEMHOSTALLOC_WRITECOMBINED: Allocates the memory as write-combined (WC). WC memory can be transferred across the PCI Express bus more quickly on some system configurations, but cannot be read efficiently by most CPUs. WC memory is a good option for buffers that will be written by the CPU and read by the GPU via mapped pinned memory or host->device transfers.
All of these flags are orthogonal to one another: a developer may allocate memory that is portable, mapped and/or write-combined with no restrictions.
The CU_MEMHOSTALLOC_DEVICEMAP flag may be specified on CUDA contexts for devices that do not support mapped pinned memory. The failure is deferred to cuMemHostGetDevicePointer() because the memory may be mapped into other CUDA contexts via the CU_MEMHOSTALLOC_PORTABLE flag.
The memory allocated by this function must be freed with cuMemFreeHost().
Note all host memory allocated using cuMemHostAlloc() will automatically be immediately accessible to all contexts on all devices which support unified addressing (as may be queried using CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING). Unless the flag CU_MEMHOSTALLOC_WRITECOMBINED is specified, the device pointer that may be used to access this host memory from those contexts is always equal to the returned host pointer *pp. If the flag CU_MEMHOSTALLOC_WRITECOMBINED is specified, then the function cuMemHostGetDevicePointer() must be used to query the device pointer, even if the context supports unified addressing. See Unified Addressing for additional details.
- Parameters
- bytesizesize_t
Requested allocation size in bytes
- Flagsunsigned int
Flags for allocation request
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_OUT_OF_MEMORY
- ppint
Returned host pointer to page-locked memory
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cudaHostAlloc
- cuda.cuda.cuMemHostGetDevicePointer(p, unsigned int Flags)¶
Passes back device pointer of mapped pinned memory.
Passes back the device pointer pdptr corresponding to the mapped, pinned host buffer p allocated by cuMemHostAlloc.
cuMemHostGetDevicePointer() will fail if the CU_MEMHOSTALLOC_DEVICEMAP flag was not specified at the time the memory was allocated, or if the function is called on a GPU that does not support mapped pinned memory.
For devices that have a non-zero value for the device attribute CU_DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM, the memory can also be accessed from the device using the host pointer p. The device pointer returned by cuMemHostGetDevicePointer() may or may not match the original host pointer p and depends on the devices visible to the application. If all devices visible to the application have a non-zero value for the device attribute, the device pointer returned by cuMemHostGetDevicePointer() will match the original pointer p. If any device visible to the application has a zero value for the device attribute, the device pointer returned by cuMemHostGetDevicePointer() will not match the original host pointer p, but it will be suitable for use on all devices provided Unified Virtual Addressing is enabled. In such systems, it is valid to access the memory using either pointer on devices that have a non-zero value for the device attribute. Note however that such devices should access the memory using only one of the two pointers and not both.
Flags provides for future releases. For now, it must be set to 0.
- Parameters
- pAny
Host pointer
- Flagsunsigned int
Options (must be 0)
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- pdptrCUdeviceptr
Returned device pointer
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cudaHostGetDevicePointer
- cuda.cuda.cuMemHostGetFlags(p)¶
Passes back flags that were used for a pinned allocation.
Passes back the flags pFlags that were specified when allocating the pinned host buffer p allocated by cuMemHostAlloc.
cuMemHostGetFlags() will fail if the pointer does not reside in an allocation performed by cuMemAllocHost() or cuMemHostAlloc().
- Parameters
- pAny
Host pointer
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- pFlagsunsigned int
Returned flags word
See also
cuMemAllocHost
cuMemHostAlloc
cudaHostGetFlags
- cuda.cuda.cuMemAllocManaged(size_t bytesize, unsigned int flags)¶
Allocates memory that will be automatically managed by the Unified Memory system.
Allocates bytesize bytes of managed memory on the device and returns in *dptr a pointer to the allocated memory. If the device doesn’t support allocating managed memory, CUDA_ERROR_NOT_SUPPORTED is returned. Support for managed memory can be queried using the device attribute CU_DEVICE_ATTRIBUTE_MANAGED_MEMORY. The allocated memory is suitably aligned for any kind of variable. The memory is not cleared. If bytesize is 0, cuMemAllocManaged returns CUDA_ERROR_INVALID_VALUE. The pointer is valid on the CPU and on all GPUs in the system that support managed memory. All accesses to this pointer must obey the Unified Memory programming model.
flags specifies the default stream association for this allocation. flags must be one of CU_MEM_ATTACH_GLOBAL or CU_MEM_ATTACH_HOST. If CU_MEM_ATTACH_GLOBAL is specified, then this memory is accessible from any stream on any device. If CU_MEM_ATTACH_HOST is specified, then the allocation should not be accessed from devices that have a zero value for the device attribute CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS; an explicit call to cuStreamAttachMemAsync will be required to enable access on such devices.
If the association is later changed via cuStreamAttachMemAsync to a single stream, the default association as specifed during cuMemAllocManaged is restored when that stream is destroyed. For managed variables, the default association is always CU_MEM_ATTACH_GLOBAL. Note that destroying a stream is an asynchronous operation, and as a result, the change to default association won’t happen until all work in the stream has completed.
Memory allocated with cuMemAllocManaged should be released with cuMemFree.
Device memory oversubscription is possible for GPUs that have a non- zero value for the device attribute CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS. Managed memory on such GPUs may be evicted from device memory to host memory at any time by the Unified Memory driver in order to make room for other allocations.
In a multi-GPU system where all GPUs have a non-zero value for the device attribute CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS, managed memory may not be populated when this API returns and instead may be populated on access. In such systems, managed memory can migrate to any processor’s memory at any time. The Unified Memory driver will employ heuristics to maintain data locality and prevent excessive page faults to the extent possible. The application can also guide the driver about memory usage patterns via cuMemAdvise. The application can also explicitly migrate memory to a desired processor’s memory via cuMemPrefetchAsync.
In a multi-GPU system where all of the GPUs have a zero value for the device attribute CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS and all the GPUs have peer-to-peer support with each other, the physical storage for managed memory is created on the GPU which is active at the time cuMemAllocManaged is called. All other GPUs will reference the data at reduced bandwidth via peer mappings over the PCIe bus. The Unified Memory driver does not migrate memory among such GPUs.
In a multi-GPU system where not all GPUs have peer-to-peer support with each other and where the value of the device attribute CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS is zero for at least one of those GPUs, the location chosen for physical storage of managed memory is system-dependent. - On Linux, the location chosen will be device memory as long as the current set of active contexts are on devices that either have peer-to-peer support with each other or have a non-zero value for the device attribute CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS. If there is an active context on a GPU that does not have a non-zero value for that device attribute and it does not have peer-to-peer support with the other devices that have active contexts on them, then the location for physical storage will be ‘zero-copy’ or host memory. Note that this means that managed memory that is located in device memory is migrated to host memory if a new context is created on a GPU that doesn’t have a non-zero value for the device attribute and does not support peer-to- peer with at least one of the other devices that has an active context. This in turn implies that context creation may fail if there is insufficient host memory to migrate all managed allocations. - On Windows, the physical storage is always created in ‘zero-copy’ or host memory. All GPUs will reference the data at reduced bandwidth over the PCIe bus. In these circumstances, use of the environment variable CUDA_VISIBLE_DEVICES is recommended to restrict CUDA to only use those GPUs that have peer-to-peer support. Alternatively, users can also set CUDA_MANAGED_FORCE_DEVICE_ALLOC to a non-zero value to force the driver to always use device memory for physical storage. When this environment variable is set to a non-zero value, all contexts created in that process on devices that support managed memory have to be peer-to-peer compatible with each other. Context creation will fail if a context is created on a device that supports managed memory and is not peer-to- peer compatible with any of the other managed memory supporting devices on which contexts were previously created, even if those contexts have been destroyed. These environment variables are described in the CUDA programming guide under the “CUDA environment variables” section. - On ARM, managed memory is not available on discrete gpu with Drive PX-2.
- Parameters
- bytesizesize_t
Requested allocation size in bytes
- flagsunsigned int
Must be one of CU_MEM_ATTACH_GLOBAL or CU_MEM_ATTACH_HOST
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_NOT_SUPPORTED CUDA_ERROR_INVALID_VALUE CUDA_ERROR_OUT_OF_MEMORY
- dptrCUdeviceptr
Returned device pointer
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cuDeviceGetAttribute
cuStreamAttachMemAsync
cudaMallocManaged
- cuda.cuda.cuDeviceGetByPCIBusId(char *pciBusId)¶
Returns a handle to a compute device.
Returns in *device a device handle given a PCI bus ID string.
- Parameters
- pciBusIdbytes
String in one of the following forms:
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_DEVICE
- devCUdevice
Returned device handle
See also
cuDeviceGet
cuDeviceGetAttribute
cuDeviceGetPCIBusId
cudaDeviceGetByPCIBusId
- cuda.cuda.cuDeviceGetPCIBusId(int length, dev)¶
Returns a PCI Bus Id string for the device.
Returns an ASCII string identifying the device dev in the NULL- terminated string pointed to by pciBusId. length specifies the maximum length of the string that may be returned.
- Parameters
- lengthint
Maximum length of string to store in name
- devAny
Device to get identifier string for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_DEVICE
- pciBusIdbytes
Returned identifier string for the device in the following format
See also
cuDeviceGet
cuDeviceGetAttribute
cuDeviceGetByPCIBusId
cudaDeviceGetPCIBusId
- cuda.cuda.cuIpcGetEventHandle(event)¶
Gets an interprocess handle for a previously allocated event.
Takes as input a previously allocated event. This event must have been created with the CU_EVENT_INTERPROCESS and CU_EVENT_DISABLE_TIMING flags set. This opaque handle may be copied into other processes and opened with cuIpcOpenEventHandle to allow efficient hardware synchronization between GPU work in different processes.
After the event has been opened in the importing process, cuEventRecord, cuEventSynchronize, cuStreamWaitEvent and cuEventQuery may be used in either process. Performing operations on the imported event after the exported event has been freed with cuEventDestroy will result in undefined behavior.
IPC functionality is restricted to devices with support for unified addressing on Linux and Windows operating systems. IPC functionality on Windows is restricted to GPUs in TCC mode
- Parameters
- eventCUevent or cudaEvent_t
Event allocated with CU_EVENT_INTERPROCESS and CU_EVENT_DISABLE_TIMING flags.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_OUT_OF_MEMORY CUDA_ERROR_MAP_FAILED CUDA_ERROR_INVALID_VALUE
- pHandleCUipcEventHandle
Pointer to a user allocated CUipcEventHandle in which to return the opaque event handle
See also
- cuda.cuda.cuIpcOpenEventHandle(CUipcEventHandle handle: CUipcEventHandle)¶
Opens an interprocess event handle for use in the current process.
Opens an interprocess event handle exported from another process with cuIpcGetEventHandle. This function returns a CUevent that behaves like a locally created event with the CU_EVENT_DISABLE_TIMING flag specified. This event must be freed with cuEventDestroy.
Performing operations on the imported event after the exported event has been freed with cuEventDestroy will result in undefined behavior.
IPC functionality is restricted to devices with support for unified addressing on Linux and Windows operating systems. IPC functionality on Windows is restricted to GPUs in TCC mode
- Parameters
- handleCUipcEventHandle
Interprocess handle to open
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_MAP_FAILED CUDA_ERROR_PEER_ACCESS_UNSUPPORTED CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_INVALID_VALUE
- phEventCUevent
Returns the imported event
See also
- cuda.cuda.cuIpcGetMemHandle(dptr)¶
Gets an interprocess memory handle for an existing device memory allocation.
Takes a pointer to the base of an existing device memory allocation created with cuMemAlloc and exports it for use in another process. This is a lightweight operation and may be called multiple times on an allocation without adverse effects.
If a region of memory is freed with cuMemFree and a subsequent call to cuMemAlloc returns memory with the same device address, cuIpcGetMemHandle will return a unique handle for the new memory.
IPC functionality is restricted to devices with support for unified addressing on Linux and Windows operating systems. IPC functionality on Windows is restricted to GPUs in TCC mode
- Parameters
- dptrAny
Base pointer to previously allocated device memory
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_OUT_OF_MEMORY CUDA_ERROR_MAP_FAILED CUDA_ERROR_INVALID_VALUE
- pHandleCUipcMemHandle
Pointer to user allocated CUipcMemHandle to return the handle in.
See also
cuMemAlloc
cuMemFree
cuIpcGetEventHandle
cuIpcOpenEventHandle
cuIpcOpenMemHandle
cuIpcCloseMemHandle
cudaIpcGetMemHandle
- cuda.cuda.cuIpcOpenMemHandle(CUipcMemHandle handle: CUipcMemHandle, unsigned int Flags)¶
Opens an interprocess memory handle exported from another process and returns a device pointer usable in the local process.
Maps memory exported from another process with cuIpcGetMemHandle into the current device address space. For contexts on different devices cuIpcOpenMemHandle can attempt to enable peer access between the devices as if the user called cuCtxEnablePeerAccess. This behavior is controlled by the CU_IPC_MEM_LAZY_ENABLE_PEER_ACCESS flag. cuDeviceCanAccessPeer can determine if a mapping is possible.
Contexts that may open CUipcMemHandles are restricted in the following way. CUipcMemHandles from each CUdevice in a given process may only be opened by one CUcontext per CUdevice per other process.
If the memory handle has already been opened by the current context, the reference count on the handle is incremented by 1 and the existing device pointer is returned.
Memory returned from cuIpcOpenMemHandle must be freed with cuIpcCloseMemHandle.
Calling cuMemFree on an exported memory region before calling cuIpcCloseMemHandle in the importing context will result in undefined behavior.
IPC functionality is restricted to devices with support for unified addressing on Linux and Windows operating systems. IPC functionality on Windows is restricted to GPUs in TCC mode
- Parameters
- handleCUipcMemHandle
::CUipcMemHandle to open
- Flagsunsigned int
Flags for this operation. Must be specified as CU_IPC_MEM_LAZY_ENABLE_PEER_ACCESS
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_MAP_FAILED CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_TOO_MANY_PEERS CUDA_ERROR_INVALID_VALUE
- pdptrCUdeviceptr
Returned device pointer
See also
Notes
No guarantees are made about the address returned in *pdptr. In particular, multiple processes may not receive the same address for the same handle.
- cuda.cuda.cuIpcCloseMemHandle(dptr)¶
Attempts to close memory mapped with cuIpcOpenMemHandle.
Decrements the reference count of the memory returned by cuIpcOpenMemHandle by 1. When the reference count reaches 0, this API unmaps the memory. The original allocation in the exporting process as well as imported mappings in other processes will be unaffected.
Any resources used to enable peer access will be freed if this is the last mapping using them.
IPC functionality is restricted to devices with support for unified addressing on Linux and Windows operating systems. IPC functionality on Windows is restricted to GPUs in TCC mode
- Parameters
- dptrAny
Device pointer returned by cuIpcOpenMemHandle
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_MAP_FAILED CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuMemAlloc
cuMemFree
cuIpcGetEventHandle
cuIpcOpenEventHandle
cuIpcGetMemHandle
cuIpcOpenMemHandle
cudaIpcCloseMemHandle
- cuda.cuda.cuMemHostRegister(p, size_t bytesize, unsigned int Flags)¶
Registers an existing host memory range for use by CUDA.
Page-locks the memory range specified by p and bytesize and maps it for the device(s) as specified by Flags. This memory range also is added to the same tracking mechanism as cuMemHostAlloc to automatically accelerate calls to functions such as cuMemcpyHtoD(). Since the memory can be accessed directly by the device, it can be read or written with much higher bandwidth than pageable memory that has not been registered. Page-locking excessive amounts of memory may degrade system performance, since it reduces the amount of memory available to the system for paging. As a result, this function is best used sparingly to register staging areas for data exchange between host and device.
This function has limited support on Mac OS X. OS 10.7 or higher is required.
The Flags parameter enables different options to be specified that affect the allocation, as follows.
CU_MEMHOSTREGISTER_PORTABLE: The memory returned by this call will be
considered as pinned memory by all CUDA contexts, not just the one that performed the allocation. - CU_MEMHOSTREGISTER_DEVICEMAP: Maps the allocation into the CUDA address space. The device pointer to the memory may be obtained by calling cuMemHostGetDevicePointer(). - CU_MEMHOSTREGISTER_IOMEMORY: The pointer is treated as pointing to some I/O memory space, e.g. the PCI Express resource of a 3rd party device. - CU_MEMHOSTREGISTER_READ_ONLY: The pointer is treated as pointing to memory that is considered read-only by the device. On platforms without CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES, this flag is required in order to register memory mapped to the CPU as read- only. Support for the use of this flag can be queried from the device attribute CU_DEVICE_ATTRIBUTE_READ_ONLY_HOST_REGISTER_SUPPORTED. Using this flag with a current context associated with a device that does not have this attribute set will cause cuMemHostRegister to error with CUDA_ERROR_NOT_SUPPORTED.
All of these flags are orthogonal to one another: a developer may page- lock memory that is portable or mapped with no restrictions.
The CU_MEMHOSTREGISTER_DEVICEMAP flag may be specified on CUDA contexts for devices that do not support mapped pinned memory. The failure is deferred to cuMemHostGetDevicePointer() because the memory may be mapped into other CUDA contexts via the CU_MEMHOSTREGISTER_PORTABLE flag.
For devices that have a non-zero value for the device attribute CU_DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM, the memory can also be accessed from the device using the host pointer p. The device pointer returned by cuMemHostGetDevicePointer() may or may not match the original host pointer ptr and depends on the devices visible to the application. If all devices visible to the application have a non-zero value for the device attribute, the device pointer returned by cuMemHostGetDevicePointer() will match the original pointer ptr. If any device visible to the application has a zero value for the device attribute, the device pointer returned by cuMemHostGetDevicePointer() will not match the original host pointer ptr, but it will be suitable for use on all devices provided Unified Virtual Addressing is enabled. In such systems, it is valid to access the memory using either pointer on devices that have a non-zero value for the device attribute. Note however that such devices should access the memory using only of the two pointers and not both.
The memory page-locked by this function must be unregistered with cuMemHostUnregister().
- Parameters
- pAny
Host pointer to memory to page-lock
- bytesizesize_t
Size in bytes of the address range to page-lock
- Flagsunsigned int
Flags for allocation request
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_OUT_OF_MEMORY CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED CUDA_ERROR_NOT_PERMITTED CUDA_ERROR_NOT_SUPPORTED
- None
None
See also
cuMemHostUnregister
cuMemHostGetFlags
cuMemHostGetDevicePointer
cudaHostRegister
- cuda.cuda.cuMemHostUnregister(p)¶
Unregisters a memory range that was registered with cuMemHostRegister.
Unmaps the memory range whose base address is specified by p, and makes it pageable again.
The base address must be the same one specified to cuMemHostRegister().
- Parameters
- pAny
Host pointer to memory to unregister
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_OUT_OF_MEMORY CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED
- None
None
See also
cuMemHostRegister
cudaHostUnregister
- cuda.cuda.cuMemcpy(dst, src, size_t ByteCount)¶
Copies memory.
Copies data between two pointers. dst and src are base pointers of the destination and source, respectively. ByteCount specifies the number of bytes to copy. Note that this function infers the type of the transfer (host to host, host to device, device to device, or device to host) from the pointer values. This function is only allowed in contexts which support unified addressing.
- Parameters
- dstAny
Destination unified virtual address space pointer
- srcAny
Source unified virtual address space pointer
- ByteCountsize_t
Size of memory copy in bytes
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cudaMemcpy
cudaMemcpyToSymbol
cudaMemcpyFromSymbol
- cuda.cuda.cuMemcpyPeer(dstDevice, dstContext, srcDevice, srcContext, size_t ByteCount)¶
Copies device memory between two contexts.
Copies from device memory in one context to device memory in another context. dstDevice is the base device pointer of the destination memory and dstContext is the destination context. srcDevice is the base device pointer of the source memory and srcContext is the source pointer. ByteCount specifies the number of bytes to copy.
- Parameters
- dstDeviceAny
Destination device pointer
- dstContextAny
Destination context
- srcDeviceAny
Source device pointer
- srcContextAny
Source context
- ByteCountsize_t
Size of memory copy in bytes
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
- cuda.cuda.cuMemcpyHtoD(dstDevice, srcHost, size_t ByteCount)¶
Copies memory from Host to Device.
Copies from host memory to device memory. dstDevice and srcHost are the base addresses of the destination and source, respectively. ByteCount specifies the number of bytes to copy.
- Parameters
- dstDeviceAny
Destination device pointer
- srcHostAny
Source host pointer
- ByteCountsize_t
Size of memory copy in bytes
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cudaMemcpy
cudaMemcpyToSymbol
- cuda.cuda.cuMemcpyDtoH(dstHost, srcDevice, size_t ByteCount)¶
Copies memory from Device to Host.
Copies from device to host memory. dstHost and srcDevice specify the base pointers of the destination and source, respectively. ByteCount specifies the number of bytes to copy.
- Parameters
- dstHostAny
Destination host pointer
- srcDeviceAny
Source device pointer
- ByteCountsize_t
Size of memory copy in bytes
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cudaMemcpy
cudaMemcpyFromSymbol
- cuda.cuda.cuMemcpyDtoD(dstDevice, srcDevice, size_t ByteCount)¶
Copies memory from Device to Device.
Copies from device memory to device memory. dstDevice and srcDevice are the base pointers of the destination and source, respectively. ByteCount specifies the number of bytes to copy.
- Parameters
- dstDeviceAny
Destination device pointer
- srcDeviceAny
Source device pointer
- ByteCountsize_t
Size of memory copy in bytes
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cudaMemcpy
cudaMemcpyToSymbol
cudaMemcpyFromSymbol
- cuda.cuda.cuMemcpyDtoA(dstArray, size_t dstOffset, srcDevice, size_t ByteCount)¶
Copies memory from Device to Array.
Copies from device memory to a 1D CUDA array. dstArray and dstOffset specify the CUDA array handle and starting index of the destination data. srcDevice specifies the base pointer of the source. ByteCount specifies the number of bytes to copy.
- Parameters
- dstArrayAny
Destination array
- dstOffsetsize_t
Offset in bytes of destination array
- srcDeviceAny
Source device pointer
- ByteCountsize_t
Size of memory copy in bytes
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cudaMemcpyToArray
- cuda.cuda.cuMemcpyAtoD(dstDevice, srcArray, size_t srcOffset, size_t ByteCount)¶
Copies memory from Array to Device.
Copies from one 1D CUDA array to device memory. dstDevice specifies the base pointer of the destination and must be naturally aligned with the CUDA array elements. srcArray and srcOffset specify the CUDA array handle and the offset in bytes into the array where the copy is to begin. ByteCount specifies the number of bytes to copy and must be evenly divisible by the array element size.
- Parameters
- dstDeviceAny
Destination device pointer
- srcArrayAny
Source array
- srcOffsetsize_t
Offset in bytes of source array
- ByteCountsize_t
Size of memory copy in bytes
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cudaMemcpyFromArray
- cuda.cuda.cuMemcpyHtoA(dstArray, size_t dstOffset, srcHost, size_t ByteCount)¶
Copies memory from Host to Array.
Copies from host memory to a 1D CUDA array. dstArray and dstOffset specify the CUDA array handle and starting offset in bytes of the destination data. pSrc specifies the base address of the source. ByteCount specifies the number of bytes to copy.
- Parameters
- dstArrayAny
Destination array
- dstOffsetsize_t
Offset in bytes of destination array
- srcHostAny
Source host pointer
- ByteCountsize_t
Size of memory copy in bytes
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cudaMemcpyToArray
- cuda.cuda.cuMemcpyAtoH(dstHost, srcArray, size_t srcOffset, size_t ByteCount)¶
Copies memory from Array to Host.
Copies from one 1D CUDA array to host memory. dstHost specifies the base pointer of the destination. srcArray and srcOffset specify the CUDA array handle and starting offset in bytes of the source data. ByteCount specifies the number of bytes to copy.
- Parameters
- dstHostAny
Destination device pointer
- srcArrayAny
Source array
- srcOffsetsize_t
Offset in bytes of source array
- ByteCountsize_t
Size of memory copy in bytes
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cudaMemcpyFromArray
- cuda.cuda.cuMemcpyAtoA(dstArray, size_t dstOffset, srcArray, size_t srcOffset, size_t ByteCount)¶
Copies memory from Array to Array.
Copies from one 1D CUDA array to another. dstArray and srcArray specify the handles of the destination and source CUDA arrays for the copy, respectively. dstOffset and srcOffset specify the destination and source offsets in bytes into the CUDA arrays. ByteCount is the number of bytes to be copied. The size of the elements in the CUDA arrays need not be the same format, but the elements must be the same size; and count must be evenly divisible by that size.
- Parameters
- dstArrayAny
Destination array
- dstOffsetsize_t
Offset in bytes of destination array
- srcArrayAny
Source array
- srcOffsetsize_t
Offset in bytes of source array
- ByteCountsize_t
Size of memory copy in bytes
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cudaMemcpyArrayToArray
- cuda.cuda.cuMemcpy2D(CUDA_MEMCPY2D pCopy: CUDA_MEMCPY2D)¶
Copies memory for 2D arrays.
Perform a 2D memory copy according to the parameters specified in pCopy. The CUDA_MEMCPY2D structure is defined as:
typedefstructCUDA_MEMCPY2D_st{ unsignedintsrcXInBytes,srcY; CUmemorytypesrcMemoryType; constvoid*srcHost; CUdeviceptrsrcDevice; CUarraysrcArray; unsignedintsrcPitch; unsignedintdstXInBytes,dstY; CUmemorytypedstMemoryType; void*dstHost; CUdeviceptrdstDevice; CUarraydstArray; unsignedintdstPitch; unsignedintWidthInBytes; unsignedintHeight; }CUDA_MEMCPY2D; where: - srcMemoryType and dstMemoryType specify the type of memory of the source and destination, respectively; CUmemorytype_enum is defined as:
typedefenumCUmemorytype_enum{ CU_MEMORYTYPE_HOST=0x01, CU_MEMORYTYPE_DEVICE=0x02, CU_MEMORYTYPE_ARRAY=0x03, CU_MEMORYTYPE_UNIFIED=0x04 }CUmemorytype;
If srcMemoryType is CU_MEMORYTYPE_UNIFIED, srcDevice and srcPitch specify the (unified virtual address space) base address of the source data and the bytes per row to apply. srcArray is ignored. This value may be used only if unified addressing is supported in the calling context.
If srcMemoryType is CU_MEMORYTYPE_HOST, srcHost and srcPitch specify the (host) base address of the source data and the bytes per row to apply. srcArray is ignored.
If srcMemoryType is CU_MEMORYTYPE_DEVICE, srcDevice and srcPitch specify the (device) base address of the source data and the bytes per row to apply. srcArray is ignored.
If srcMemoryType is CU_MEMORYTYPE_ARRAY, srcArray specifies the handle of the source data. srcHost, srcDevice and srcPitch are ignored.
If dstMemoryType is CU_MEMORYTYPE_HOST, dstHost and dstPitch specify the (host) base address of the destination data and the bytes per row to apply. dstArray is ignored.
If dstMemoryType is CU_MEMORYTYPE_UNIFIED, dstDevice and dstPitch specify the (unified virtual address space) base address of the source data and the bytes per row to apply. dstArray is ignored. This value may be used only if unified addressing is supported in the calling context.
If dstMemoryType is CU_MEMORYTYPE_DEVICE, dstDevice and dstPitch specify the (device) base address of the destination data and the bytes per row to apply. dstArray is ignored.
If dstMemoryType is CU_MEMORYTYPE_ARRAY, dstArray specifies the handle of the destination data. dstHost, dstDevice and dstPitch are ignored.
For host pointers, the starting address is void*Start=(void*)((char*)srcHost+srcY*srcPitch+srcXInBytes);
For device pointers, the starting address is CUdeviceptrStart=srcDevice+srcY*srcPitch+srcXInBytes;
For CUDA arrays, srcXInBytes must be evenly divisible by the array element size.
For host pointers, the base address is void*dstStart=(void*)((char*)dstHost+dstY*dstPitch+dstXInBytes);
For device pointers, the starting address is CUdeviceptrdstStart=dstDevice+dstY*dstPitch+dstXInBytes;
For CUDA arrays, dstXInBytes must be evenly divisible by the array element size.
cuMemcpy2D() returns an error if any pitch is greater than the maximum allowed (CU_DEVICE_ATTRIBUTE_MAX_PITCH). cuMemAllocPitch() passes back pitches that always work with cuMemcpy2D(). On intra-device memory copies (device to device, CUDA array to device, CUDA array to CUDA array), cuMemcpy2D() may fail for pitches not computed by cuMemAllocPitch(). cuMemcpy2DUnaligned() does not have this restriction, but may run significantly slower in the cases where cuMemcpy2D() would have returned an error code.
- Parameters
- pCopyCUDA_MEMCPY2D
Parameters for the memory copy
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cudaMemcpy2D
cudaMemcpy2DToArray
cudaMemcpy2DFromArray
- cuda.cuda.cuMemcpy2DUnaligned(CUDA_MEMCPY2D pCopy: CUDA_MEMCPY2D)¶
Copies memory for 2D arrays.
Perform a 2D memory copy according to the parameters specified in pCopy. The CUDA_MEMCPY2D structure is defined as:
typedefstructCUDA_MEMCPY2D_st{ unsignedintsrcXInBytes,srcY; CUmemorytypesrcMemoryType; constvoid*srcHost; CUdeviceptrsrcDevice; CUarraysrcArray; unsignedintsrcPitch; unsignedintdstXInBytes,dstY; CUmemorytypedstMemoryType; void*dstHost; CUdeviceptrdstDevice; CUarraydstArray; unsignedintdstPitch; unsignedintWidthInBytes; unsignedintHeight; }CUDA_MEMCPY2D; where: - srcMemoryType and dstMemoryType specify the type of memory of the source and destination, respectively; CUmemorytype_enum is defined as:
typedefenumCUmemorytype_enum{ CU_MEMORYTYPE_HOST=0x01, CU_MEMORYTYPE_DEVICE=0x02, CU_MEMORYTYPE_ARRAY=0x03, CU_MEMORYTYPE_UNIFIED=0x04 }CUmemorytype;
If srcMemoryType is CU_MEMORYTYPE_UNIFIED, srcDevice and srcPitch specify the (unified virtual address space) base address of the source data and the bytes per row to apply. srcArray is ignored. This value may be used only if unified addressing is supported in the calling context.
If srcMemoryType is CU_MEMORYTYPE_HOST, srcHost and srcPitch specify the (host) base address of the source data and the bytes per row to apply. srcArray is ignored.
If srcMemoryType is CU_MEMORYTYPE_DEVICE, srcDevice and srcPitch specify the (device) base address of the source data and the bytes per row to apply. srcArray is ignored.
If srcMemoryType is CU_MEMORYTYPE_ARRAY, srcArray specifies the handle of the source data. srcHost, srcDevice and srcPitch are ignored.
If dstMemoryType is CU_MEMORYTYPE_UNIFIED, dstDevice and dstPitch specify the (unified virtual address space) base address of the source data and the bytes per row to apply. dstArray is ignored. This value may be used only if unified addressing is supported in the calling context.
If dstMemoryType is CU_MEMORYTYPE_HOST, dstHost and dstPitch specify the (host) base address of the destination data and the bytes per row to apply. dstArray is ignored.
If dstMemoryType is CU_MEMORYTYPE_DEVICE, dstDevice and dstPitch specify the (device) base address of the destination data and the bytes per row to apply. dstArray is ignored.
If dstMemoryType is CU_MEMORYTYPE_ARRAY, dstArray specifies the handle of the destination data. dstHost, dstDevice and dstPitch are ignored.
For host pointers, the starting address is void*Start=(void*)((char*)srcHost+srcY*srcPitch+srcXInBytes);
For device pointers, the starting address is CUdeviceptrStart=srcDevice+srcY*srcPitch+srcXInBytes;
For CUDA arrays, srcXInBytes must be evenly divisible by the array element size.
For host pointers, the base address is void*dstStart=(void*)((char*)dstHost+dstY*dstPitch+dstXInBytes);
For device pointers, the starting address is CUdeviceptrdstStart=dstDevice+dstY*dstPitch+dstXInBytes;
For CUDA arrays, dstXInBytes must be evenly divisible by the array element size.
cuMemcpy2D() returns an error if any pitch is greater than the maximum allowed (CU_DEVICE_ATTRIBUTE_MAX_PITCH). cuMemAllocPitch() passes back pitches that always work with cuMemcpy2D(). On intra-device memory copies (device to device, CUDA array to device, CUDA array to CUDA array), cuMemcpy2D() may fail for pitches not computed by cuMemAllocPitch(). cuMemcpy2DUnaligned() does not have this restriction, but may run significantly slower in the cases where cuMemcpy2D() would have returned an error code.
- Parameters
- pCopyCUDA_MEMCPY2D
Parameters for the memory copy
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cudaMemcpy2D
cudaMemcpy2DToArray
cudaMemcpy2DFromArray
- cuda.cuda.cuMemcpy3D(CUDA_MEMCPY3D pCopy: CUDA_MEMCPY3D)¶
Copies memory for 3D arrays.
Perform a 3D memory copy according to the parameters specified in pCopy. The CUDA_MEMCPY3D structure is defined as:
typedefstructCUDA_MEMCPY3D_st{ unsignedintsrcXInBytes,srcY,srcZ; unsignedintsrcLOD; CUmemorytypesrcMemoryType; constvoid*srcHost; CUdeviceptrsrcDevice; CUarraysrcArray; unsignedintsrcPitch;//ignoredwhensrcisarray unsignedintsrcHeight;//ignoredwhensrcisarray;maybe0ifDepth==1 unsignedintdstXInBytes,dstY,dstZ; unsignedintdstLOD; CUmemorytypedstMemoryType; void*dstHost; CUdeviceptrdstDevice; CUarraydstArray; unsignedintdstPitch;//ignoredwhendstisarray unsignedintdstHeight;//ignoredwhendstisarray;maybe0ifDepth==1 unsignedintWidthInBytes; unsignedintHeight; unsignedintDepth; }CUDA_MEMCPY3D; where: - srcMemoryType and dstMemoryType specify the type of memory of the source and destination, respectively; CUmemorytype_enum is defined as:
typedefenumCUmemorytype_enum{ CU_MEMORYTYPE_HOST=0x01, CU_MEMORYTYPE_DEVICE=0x02, CU_MEMORYTYPE_ARRAY=0x03, CU_MEMORYTYPE_UNIFIED=0x04 }CUmemorytype;
If srcMemoryType is CU_MEMORYTYPE_UNIFIED, srcDevice and srcPitch specify the (unified virtual address space) base address of the source data and the bytes per row to apply. srcArray is ignored. This value may be used only if unified addressing is supported in the calling context.
If srcMemoryType is CU_MEMORYTYPE_HOST, srcHost, srcPitch and srcHeight specify the (host) base address of the source data, the bytes per row, and the height of each 2D slice of the 3D array. srcArray is ignored.
If srcMemoryType is CU_MEMORYTYPE_DEVICE, srcDevice, srcPitch and srcHeight specify the (device) base address of the source data, the bytes per row, and the height of each 2D slice of the 3D array. srcArray is ignored.
If srcMemoryType is CU_MEMORYTYPE_ARRAY, srcArray specifies the handle of the source data. srcHost, srcDevice, srcPitch and srcHeight are ignored.
If dstMemoryType is CU_MEMORYTYPE_UNIFIED, dstDevice and dstPitch specify the (unified virtual address space) base address of the source data and the bytes per row to apply. dstArray is ignored. This value may be used only if unified addressing is supported in the calling context.
If dstMemoryType is CU_MEMORYTYPE_HOST, dstHost and dstPitch specify the (host) base address of the destination data, the bytes per row, and the height of each 2D slice of the 3D array. dstArray is ignored.
If dstMemoryType is CU_MEMORYTYPE_DEVICE, dstDevice and dstPitch specify the (device) base address of the destination data, the bytes per row, and the height of each 2D slice of the 3D array. dstArray is ignored.
If dstMemoryType is CU_MEMORYTYPE_ARRAY, dstArray specifies the handle of the destination data. dstHost, dstDevice, dstPitch and dstHeight are ignored.
For host pointers, the starting address is void*Start=(void*)((char*)sr cHost+(srcZ*srcHeight+srcY)*srcPitch+srcXInBytes);
For device pointers, the starting address is CUdeviceptrStart=srcDevice+(srcZ*srcHeight+srcY)*srcPitch+srcXInBytes;
For CUDA arrays, srcXInBytes must be evenly divisible by the array element size.
For host pointers, the base address is void*dstStart=(void*)((char*)dst Host+(dstZ*dstHeight+dstY)*dstPitch+dstXInBytes);
For device pointers, the starting address is CUdeviceptrdstStart=dstDev ice+(dstZ*dstHeight+dstY)*dstPitch+dstXInBytes;
For CUDA arrays, dstXInBytes must be evenly divisible by the array element size.
cuMemcpy3D() returns an error if any pitch is greater than the maximum allowed (CU_DEVICE_ATTRIBUTE_MAX_PITCH).
- Parameters
- pCopyCUDA_MEMCPY3D
Parameters for the memory copy
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cudaMemcpy3D
- cuda.cuda.cuMemcpy3DPeer(CUDA_MEMCPY3D_PEER pCopy: CUDA_MEMCPY3D_PEER)¶
Copies memory between contexts.
Perform a 3D memory copy according to the parameters specified in pCopy. See the definition of the CUDA_MEMCPY3D_PEER structure for documentation of its parameters.
- Parameters
- pCopyCUDA_MEMCPY3D_PEER
Parameters for the memory copy
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuMemcpyDtoD
cuMemcpyPeer
cuMemcpyDtoDAsync
cuMemcpyPeerAsync
cuMemcpy3DPeerAsync
cudaMemcpy3DPeer
- cuda.cuda.cuMemcpyAsync(dst, src, size_t ByteCount, hStream)¶
Copies memory asynchronously.
Copies data between two pointers. dst and src are base pointers of the destination and source, respectively. ByteCount specifies the number of bytes to copy. Note that this function infers the type of the transfer (host to host, host to device, device to device, or device to host) from the pointer values. This function is only allowed in contexts which support unified addressing.
- Parameters
- dstAny
Destination unified virtual address space pointer
- srcAny
Source unified virtual address space pointer
- ByteCountsize_t
Size of memory copy in bytes
- hStreamCUstream or cudaStream_t
Stream identifier
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D8Async
cuMemsetD2D16
cuMemsetD2D16Async
cuMemsetD2D32
cuMemsetD2D32Async
cuMemsetD8
cuMemsetD8Async
cuMemsetD16
cuMemsetD16Async
cuMemsetD32
cuMemsetD32Async
cudaMemcpyAsync
cudaMemcpyToSymbolAsync
cudaMemcpyFromSymbolAsync
- cuda.cuda.cuMemcpyPeerAsync(dstDevice, dstContext, srcDevice, srcContext, size_t ByteCount, hStream)¶
Copies device memory between two contexts asynchronously.
Copies from device memory in one context to device memory in another context. dstDevice is the base device pointer of the destination memory and dstContext is the destination context. srcDevice is the base device pointer of the source memory and srcContext is the source pointer. ByteCount specifies the number of bytes to copy.
- Parameters
- dstDeviceAny
Destination device pointer
- dstContextAny
Destination context
- srcDeviceAny
Source device pointer
- srcContextAny
Source context
- ByteCountsize_t
Size of memory copy in bytes
- hStreamCUstream or cudaStream_t
Stream identifier
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE
- None
None
See also
cuMemcpyDtoD
cuMemcpyPeer
cuMemcpy3DPeer
cuMemcpyDtoDAsync
cuMemcpy3DPeerAsync
cudaMemcpyPeerAsync
- cuda.cuda.cuMemcpyHtoDAsync(dstDevice, srcHost, size_t ByteCount, hStream)¶
Copies memory from Host to Device.
Copies from host memory to device memory. dstDevice and srcHost are the base addresses of the destination and source, respectively. ByteCount specifies the number of bytes to copy.
- Parameters
- dstDeviceAny
Destination device pointer
- srcHostAny
Source host pointer
- ByteCountsize_t
Size of memory copy in bytes
- hStreamCUstream or cudaStream_t
Stream identifier
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D8Async
cuMemsetD2D16
cuMemsetD2D16Async
cuMemsetD2D32
cuMemsetD2D32Async
cuMemsetD8
cuMemsetD8Async
cuMemsetD16
cuMemsetD16Async
cuMemsetD32
cuMemsetD32Async
cudaMemcpyAsync
cudaMemcpyToSymbolAsync
- cuda.cuda.cuMemcpyDtoHAsync(dstHost, srcDevice, size_t ByteCount, hStream)¶
Copies memory from Device to Host.
Copies from device to host memory. dstHost and srcDevice specify the base pointers of the destination and source, respectively. ByteCount specifies the number of bytes to copy.
- Parameters
- dstHostAny
Destination host pointer
- srcDeviceAny
Source device pointer
- ByteCountsize_t
Size of memory copy in bytes
- hStreamCUstream or cudaStream_t
Stream identifier
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D8Async
cuMemsetD2D16
cuMemsetD2D16Async
cuMemsetD2D32
cuMemsetD2D32Async
cuMemsetD8
cuMemsetD8Async
cuMemsetD16
cuMemsetD16Async
cuMemsetD32
cuMemsetD32Async
cudaMemcpyAsync
cudaMemcpyFromSymbolAsync
- cuda.cuda.cuMemcpyDtoDAsync(dstDevice, srcDevice, size_t ByteCount, hStream)¶
Copies memory from Device to Device.
Copies from device memory to device memory. dstDevice and srcDevice are the base pointers of the destination and source, respectively. ByteCount specifies the number of bytes to copy.
- Parameters
- dstDeviceAny
Destination device pointer
- srcDeviceAny
Source device pointer
- ByteCountsize_t
Size of memory copy in bytes
- hStreamCUstream or cudaStream_t
Stream identifier
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D8Async
cuMemsetD2D16
cuMemsetD2D16Async
cuMemsetD2D32
cuMemsetD2D32Async
cuMemsetD8
cuMemsetD8Async
cuMemsetD16
cuMemsetD16Async
cuMemsetD32
cuMemsetD32Async
cudaMemcpyAsync
cudaMemcpyToSymbolAsync
cudaMemcpyFromSymbolAsync
- cuda.cuda.cuMemcpyHtoAAsync(dstArray, size_t dstOffset, srcHost, size_t ByteCount, hStream)¶
Copies memory from Host to Array.
Copies from host memory to a 1D CUDA array. dstArray and dstOffset specify the CUDA array handle and starting offset in bytes of the destination data. srcHost specifies the base address of the source. ByteCount specifies the number of bytes to copy.
- Parameters
- dstArrayAny
Destination array
- dstOffsetsize_t
Offset in bytes of destination array
- srcHostAny
Source host pointer
- ByteCountsize_t
Size of memory copy in bytes
- hStreamCUstream or cudaStream_t
Stream identifier
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D8Async
cuMemsetD2D16
cuMemsetD2D16Async
cuMemsetD2D32
cuMemsetD2D32Async
cuMemsetD8
cuMemsetD8Async
cuMemsetD16
cuMemsetD16Async
cuMemsetD32
cuMemsetD32Async
cudaMemcpyToArrayAsync
- cuda.cuda.cuMemcpyAtoHAsync(dstHost, srcArray, size_t srcOffset, size_t ByteCount, hStream)¶
Copies memory from Array to Host.
Copies from one 1D CUDA array to host memory. dstHost specifies the base pointer of the destination. srcArray and srcOffset specify the CUDA array handle and starting offset in bytes of the source data. ByteCount specifies the number of bytes to copy.
- Parameters
- dstHostAny
Destination pointer
- srcArrayAny
Source array
- srcOffsetsize_t
Offset in bytes of source array
- ByteCountsize_t
Size of memory copy in bytes
- hStreamCUstream or cudaStream_t
Stream identifier
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D8Async
cuMemsetD2D16
cuMemsetD2D16Async
cuMemsetD2D32
cuMemsetD2D32Async
cuMemsetD8
cuMemsetD8Async
cuMemsetD16
cuMemsetD16Async
cuMemsetD32
cuMemsetD32Async
cudaMemcpyFromArrayAsync
- cuda.cuda.cuMemcpy2DAsync(CUDA_MEMCPY2D pCopy: CUDA_MEMCPY2D, hStream)¶
Copies memory for 2D arrays.
Perform a 2D memory copy according to the parameters specified in pCopy. The CUDA_MEMCPY2D structure is defined as:
typedefstructCUDA_MEMCPY2D_st{ unsignedintsrcXInBytes,srcY; CUmemorytypesrcMemoryType; constvoid*srcHost; CUdeviceptrsrcDevice; CUarraysrcArray; unsignedintsrcPitch; unsignedintdstXInBytes,dstY; CUmemorytypedstMemoryType; void*dstHost; CUdeviceptrdstDevice; CUarraydstArray; unsignedintdstPitch; unsignedintWidthInBytes; unsignedintHeight; }CUDA_MEMCPY2D; where: - srcMemoryType and dstMemoryType specify the type of memory of the source and destination, respectively; CUmemorytype_enum is defined as:
typedefenumCUmemorytype_enum{ CU_MEMORYTYPE_HOST=0x01, CU_MEMORYTYPE_DEVICE=0x02, CU_MEMORYTYPE_ARRAY=0x03, CU_MEMORYTYPE_UNIFIED=0x04 }CUmemorytype;
If srcMemoryType is CU_MEMORYTYPE_HOST, srcHost and srcPitch specify the (host) base address of the source data and the bytes per row to apply. srcArray is ignored.
If srcMemoryType is CU_MEMORYTYPE_UNIFIED, srcDevice and srcPitch specify the (unified virtual address space) base address of the source data and the bytes per row to apply. srcArray is ignored. This value may be used only if unified addressing is supported in the calling context.
If srcMemoryType is CU_MEMORYTYPE_DEVICE, srcDevice and srcPitch specify the (device) base address of the source data and the bytes per row to apply. srcArray is ignored.
If srcMemoryType is CU_MEMORYTYPE_ARRAY, srcArray specifies the handle of the source data. srcHost, srcDevice and srcPitch are ignored.
If dstMemoryType is CU_MEMORYTYPE_UNIFIED, dstDevice and dstPitch specify the (unified virtual address space) base address of the source data and the bytes per row to apply. dstArray is ignored. This value may be used only if unified addressing is supported in the calling context.
If dstMemoryType is CU_MEMORYTYPE_HOST, dstHost and dstPitch specify the (host) base address of the destination data and the bytes per row to apply. dstArray is ignored.
If dstMemoryType is CU_MEMORYTYPE_DEVICE, dstDevice and dstPitch specify the (device) base address of the destination data and the bytes per row to apply. dstArray is ignored.
If dstMemoryType is CU_MEMORYTYPE_ARRAY, dstArray specifies the handle of the destination data. dstHost, dstDevice and dstPitch are ignored.
For host pointers, the starting address is void*Start=(void*)((char*)srcHost+srcY*srcPitch+srcXInBytes);
For device pointers, the starting address is CUdeviceptrStart=srcDevice+srcY*srcPitch+srcXInBytes;
For CUDA arrays, srcXInBytes must be evenly divisible by the array element size.
For host pointers, the base address is void*dstStart=(void*)((char*)dstHost+dstY*dstPitch+dstXInBytes);
For device pointers, the starting address is CUdeviceptrdstStart=dstDevice+dstY*dstPitch+dstXInBytes;
For CUDA arrays, dstXInBytes must be evenly divisible by the array element size.
cuMemcpy2DAsync() returns an error if any pitch is greater than the maximum allowed (CU_DEVICE_ATTRIBUTE_MAX_PITCH). cuMemAllocPitch() passes back pitches that always work with cuMemcpy2D(). On intra-device memory copies (device to device, CUDA array to device, CUDA array to CUDA array), cuMemcpy2DAsync() may fail for pitches not computed by cuMemAllocPitch().
- Parameters
- pCopyCUDA_MEMCPY2D
Parameters for the memory copy
- hStreamCUstream or cudaStream_t
Stream identifier
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D8Async
cuMemsetD2D16
cuMemsetD2D16Async
cuMemsetD2D32
cuMemsetD2D32Async
cuMemsetD8
cuMemsetD8Async
cuMemsetD16
cuMemsetD16Async
cuMemsetD32
cuMemsetD32Async
cudaMemcpy2DAsync
cudaMemcpy2DToArrayAsync
cudaMemcpy2DFromArrayAsync
- cuda.cuda.cuMemcpy3DAsync(CUDA_MEMCPY3D pCopy: CUDA_MEMCPY3D, hStream)¶
Copies memory for 3D arrays.
Perform a 3D memory copy according to the parameters specified in pCopy. The CUDA_MEMCPY3D structure is defined as:
typedefstructCUDA_MEMCPY3D_st{ unsignedintsrcXInBytes,srcY,srcZ; unsignedintsrcLOD; CUmemorytypesrcMemoryType; constvoid*srcHost; CUdeviceptrsrcDevice; CUarraysrcArray; unsignedintsrcPitch;//ignoredwhensrcisarray unsignedintsrcHeight;//ignoredwhensrcisarray;maybe0ifDepth==1 unsignedintdstXInBytes,dstY,dstZ; unsignedintdstLOD; CUmemorytypedstMemoryType; void*dstHost; CUdeviceptrdstDevice; CUarraydstArray; unsignedintdstPitch;//ignoredwhendstisarray unsignedintdstHeight;//ignoredwhendstisarray;maybe0ifDepth==1 unsignedintWidthInBytes; unsignedintHeight; unsignedintDepth; }CUDA_MEMCPY3D; where: - srcMemoryType and dstMemoryType specify the type of memory of the source and destination, respectively; CUmemorytype_enum is defined as:
typedefenumCUmemorytype_enum{ CU_MEMORYTYPE_HOST=0x01, CU_MEMORYTYPE_DEVICE=0x02, CU_MEMORYTYPE_ARRAY=0x03, CU_MEMORYTYPE_UNIFIED=0x04 }CUmemorytype;
If srcMemoryType is CU_MEMORYTYPE_UNIFIED, srcDevice and srcPitch specify the (unified virtual address space) base address of the source data and the bytes per row to apply. srcArray is ignored. This value may be used only if unified addressing is supported in the calling context.
If srcMemoryType is CU_MEMORYTYPE_HOST, srcHost, srcPitch and srcHeight specify the (host) base address of the source data, the bytes per row, and the height of each 2D slice of the 3D array. srcArray is ignored.
If srcMemoryType is CU_MEMORYTYPE_DEVICE, srcDevice, srcPitch and srcHeight specify the (device) base address of the source data, the bytes per row, and the height of each 2D slice of the 3D array. srcArray is ignored.
If srcMemoryType is CU_MEMORYTYPE_ARRAY, srcArray specifies the handle of the source data. srcHost, srcDevice, srcPitch and srcHeight are ignored.
If dstMemoryType is CU_MEMORYTYPE_UNIFIED, dstDevice and dstPitch specify the (unified virtual address space) base address of the source data and the bytes per row to apply. dstArray is ignored. This value may be used only if unified addressing is supported in the calling context.
If dstMemoryType is CU_MEMORYTYPE_HOST, dstHost and dstPitch specify the (host) base address of the destination data, the bytes per row, and the height of each 2D slice of the 3D array. dstArray is ignored.
If dstMemoryType is CU_MEMORYTYPE_DEVICE, dstDevice and dstPitch specify the (device) base address of the destination data, the bytes per row, and the height of each 2D slice of the 3D array. dstArray is ignored.
If dstMemoryType is CU_MEMORYTYPE_ARRAY, dstArray specifies the handle of the destination data. dstHost, dstDevice, dstPitch and dstHeight are ignored.
For host pointers, the starting address is void*Start=(void*)((char*)sr cHost+(srcZ*srcHeight+srcY)*srcPitch+srcXInBytes);
For device pointers, the starting address is CUdeviceptrStart=srcDevice+(srcZ*srcHeight+srcY)*srcPitch+srcXInBytes;
For CUDA arrays, srcXInBytes must be evenly divisible by the array element size.
For host pointers, the base address is void*dstStart=(void*)((char*)dst Host+(dstZ*dstHeight+dstY)*dstPitch+dstXInBytes);
For device pointers, the starting address is CUdeviceptrdstStart=dstDev ice+(dstZ*dstHeight+dstY)*dstPitch+dstXInBytes;
For CUDA arrays, dstXInBytes must be evenly divisible by the array element size.
cuMemcpy3DAsync() returns an error if any pitch is greater than the maximum allowed (CU_DEVICE_ATTRIBUTE_MAX_PITCH).
- Parameters
- pCopyCUDA_MEMCPY3D
Parameters for the memory copy
- hStreamCUstream or cudaStream_t
Stream identifier
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D8Async
cuMemsetD2D16
cuMemsetD2D16Async
cuMemsetD2D32
cuMemsetD2D32Async
cuMemsetD8
cuMemsetD8Async
cuMemsetD16
cuMemsetD16Async
cuMemsetD32
cuMemsetD32Async
cudaMemcpy3DAsync
- cuda.cuda.cuMemcpy3DPeerAsync(CUDA_MEMCPY3D_PEER pCopy: CUDA_MEMCPY3D_PEER, hStream)¶
Copies memory between contexts asynchronously.
Perform a 3D memory copy according to the parameters specified in pCopy. See the definition of the CUDA_MEMCPY3D_PEER structure for documentation of its parameters.
- Parameters
- pCopyCUDA_MEMCPY3D_PEER
Parameters for the memory copy
- hStreamCUstream or cudaStream_t
Stream identifier
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuMemcpyDtoD
cuMemcpyPeer
cuMemcpyDtoDAsync
cuMemcpyPeerAsync
cuMemcpy3DPeerAsync
cudaMemcpy3DPeerAsync
- cuda.cuda.cuMemsetD8(dstDevice, unsigned char uc, size_t N)¶
Initializes device memory.
Sets the memory range of N 8-bit values to the specified value uc.
- Parameters
- dstDeviceAny
Destination device pointer
- ucunsigned char
Value to set
- Nsize_t
Number of elements
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D8Async
cuMemsetD2D16
cuMemsetD2D16Async
cuMemsetD2D32
cuMemsetD2D32Async
cuMemsetD8Async
cuMemsetD16
cuMemsetD16Async
cuMemsetD32
cuMemsetD32Async
cudaMemset
- cuda.cuda.cuMemsetD16(dstDevice, unsigned short us, size_t N)¶
Initializes device memory.
Sets the memory range of N 16-bit values to the specified value us. The dstDevice pointer must be two byte aligned.
- Parameters
- dstDeviceAny
Destination device pointer
- usunsigned short
Value to set
- Nsize_t
Number of elements
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D8Async
cuMemsetD2D16
cuMemsetD2D16Async
cuMemsetD2D32
cuMemsetD2D32Async
cuMemsetD8
cuMemsetD8Async
cuMemsetD16Async
cuMemsetD32
cuMemsetD32Async
cudaMemset
- cuda.cuda.cuMemsetD32(dstDevice, unsigned int ui, size_t N)¶
Initializes device memory.
Sets the memory range of N 32-bit values to the specified value ui. The dstDevice pointer must be four byte aligned.
- Parameters
- dstDeviceAny
Destination device pointer
- uiunsigned int
Value to set
- Nsize_t
Number of elements
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D8Async
cuMemsetD2D16
cuMemsetD2D16Async
cuMemsetD2D32
cuMemsetD2D32Async
cuMemsetD8
cuMemsetD8Async
cuMemsetD16
cuMemsetD16Async
cuMemsetD32Async
cudaMemset
- cuda.cuda.cuMemsetD2D8(dstDevice, size_t dstPitch, unsigned char uc, size_t Width, size_t Height)¶
Initializes device memory.
Sets the 2D memory range of Width 8-bit values to the specified value uc. Height specifies the number of rows to set, and dstPitch specifies the number of bytes between each row. This function performs fastest when the pitch is one that has been passed back by cuMemAllocPitch().
- Parameters
- dstDeviceAny
Destination device pointer
- dstPitchsize_t
Pitch of destination device pointer(Unused if Height is 1)
- ucunsigned char
Value to set
- Widthsize_t
Width of row
- Heightsize_t
Number of rows
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8Async
cuMemsetD2D16
cuMemsetD2D16Async
cuMemsetD2D32
cuMemsetD2D32Async
cuMemsetD8
cuMemsetD8Async
cuMemsetD16
cuMemsetD16Async
cuMemsetD32
cuMemsetD32Async
cudaMemset2D
- cuda.cuda.cuMemsetD2D16(dstDevice, size_t dstPitch, unsigned short us, size_t Width, size_t Height)¶
Initializes device memory.
Sets the 2D memory range of Width 16-bit values to the specified value us. Height specifies the number of rows to set, and dstPitch specifies the number of bytes between each row. The dstDevice pointer and dstPitch offset must be two byte aligned. This function performs fastest when the pitch is one that has been passed back by cuMemAllocPitch().
- Parameters
- dstDeviceAny
Destination device pointer
- dstPitchsize_t
Pitch of destination device pointer(Unused if Height is 1)
- usunsigned short
Value to set
- Widthsize_t
Width of row
- Heightsize_t
Number of rows
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D8Async
cuMemsetD2D16Async
cuMemsetD2D32
cuMemsetD2D32Async
cuMemsetD8
cuMemsetD8Async
cuMemsetD16
cuMemsetD16Async
cuMemsetD32
cuMemsetD32Async
cudaMemset2D
- cuda.cuda.cuMemsetD2D32(dstDevice, size_t dstPitch, unsigned int ui, size_t Width, size_t Height)¶
Initializes device memory.
Sets the 2D memory range of Width 32-bit values to the specified value ui. Height specifies the number of rows to set, and dstPitch specifies the number of bytes between each row. The dstDevice pointer and dstPitch offset must be four byte aligned. This function performs fastest when the pitch is one that has been passed back by cuMemAllocPitch().
- Parameters
- dstDeviceAny
Destination device pointer
- dstPitchsize_t
Pitch of destination device pointer(Unused if Height is 1)
- uiunsigned int
Value to set
- Widthsize_t
Width of row
- Heightsize_t
Number of rows
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D8Async
cuMemsetD2D16
cuMemsetD2D16Async
cuMemsetD2D32Async
cuMemsetD8
cuMemsetD8Async
cuMemsetD16
cuMemsetD16Async
cuMemsetD32
cuMemsetD32Async
cudaMemset2D
- cuda.cuda.cuMemsetD8Async(dstDevice, unsigned char uc, size_t N, hStream)¶
Sets device memory.
Sets the memory range of N 8-bit values to the specified value uc.
- Parameters
- dstDeviceAny
Destination device pointer
- ucunsigned char
Value to set
- Nsize_t
Number of elements
- hStreamCUstream or cudaStream_t
Stream identifier
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D8Async
cuMemsetD2D16
cuMemsetD2D16Async
cuMemsetD2D32
cuMemsetD2D32Async
cuMemsetD8
cuMemsetD16
cuMemsetD16Async
cuMemsetD32
cuMemsetD32Async
cudaMemsetAsync
- cuda.cuda.cuMemsetD16Async(dstDevice, unsigned short us, size_t N, hStream)¶
Sets device memory.
Sets the memory range of N 16-bit values to the specified value us. The dstDevice pointer must be two byte aligned.
- Parameters
- dstDeviceAny
Destination device pointer
- usunsigned short
Value to set
- Nsize_t
Number of elements
- hStreamCUstream or cudaStream_t
Stream identifier
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D8Async
cuMemsetD2D16
cuMemsetD2D16Async
cuMemsetD2D32
cuMemsetD2D32Async
cuMemsetD8
cuMemsetD8Async
cuMemsetD16
cuMemsetD32
cuMemsetD32Async
cudaMemsetAsync
- cuda.cuda.cuMemsetD32Async(dstDevice, unsigned int ui, size_t N, hStream)¶
Sets device memory.
Sets the memory range of N 32-bit values to the specified value ui. The dstDevice pointer must be four byte aligned.
- Parameters
- dstDeviceAny
Destination device pointer
- uiunsigned int
Value to set
- Nsize_t
Number of elements
- hStreamCUstream or cudaStream_t
Stream identifier
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D8Async
cuMemsetD2D16
cuMemsetD2D16Async
cuMemsetD2D32
cuMemsetD2D32Async
cuMemsetD8
cuMemsetD8Async
cuMemsetD16
cuMemsetD16Async
cuMemsetD32
cudaMemsetAsync
- cuda.cuda.cuMemsetD2D8Async(dstDevice, size_t dstPitch, unsigned char uc, size_t Width, size_t Height, hStream)¶
Sets device memory.
Sets the 2D memory range of Width 8-bit values to the specified value uc. Height specifies the number of rows to set, and dstPitch specifies the number of bytes between each row. This function performs fastest when the pitch is one that has been passed back by cuMemAllocPitch().
- Parameters
- dstDeviceAny
Destination device pointer
- dstPitchsize_t
Pitch of destination device pointer(Unused if Height is 1)
- ucunsigned char
Value to set
- Widthsize_t
Width of row
- Heightsize_t
Number of rows
- hStreamCUstream or cudaStream_t
Stream identifier
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D16Async
cuMemsetD2D32
cuMemsetD2D32Async
cuMemsetD8
cuMemsetD8Async
cuMemsetD16
cuMemsetD16Async
cuMemsetD32
cuMemsetD32Async
cudaMemset2DAsync
- cuda.cuda.cuMemsetD2D16Async(dstDevice, size_t dstPitch, unsigned short us, size_t Width, size_t Height, hStream)¶
Sets device memory.
Sets the 2D memory range of Width 16-bit values to the specified value us. Height specifies the number of rows to set, and dstPitch specifies the number of bytes between each row. The dstDevice pointer and dstPitch offset must be two byte aligned. This function performs fastest when the pitch is one that has been passed back by cuMemAllocPitch().
- Parameters
- dstDeviceAny
Destination device pointer
- dstPitchsize_t
Pitch of destination device pointer(Unused if Height is 1)
- usunsigned short
Value to set
- Widthsize_t
Width of row
- Heightsize_t
Number of rows
- hStreamCUstream or cudaStream_t
Stream identifier
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D8Async
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD2D32Async
cuMemsetD8
cuMemsetD8Async
cuMemsetD16
cuMemsetD16Async
cuMemsetD32
cuMemsetD32Async
cudaMemset2DAsync
- cuda.cuda.cuMemsetD2D32Async(dstDevice, size_t dstPitch, unsigned int ui, size_t Width, size_t Height, hStream)¶
Sets device memory.
Sets the 2D memory range of Width 32-bit values to the specified value ui. Height specifies the number of rows to set, and dstPitch specifies the number of bytes between each row. The dstDevice pointer and dstPitch offset must be four byte aligned. This function performs fastest when the pitch is one that has been passed back by cuMemAllocPitch().
- Parameters
- dstDeviceAny
Destination device pointer
- dstPitchsize_t
Pitch of destination device pointer(Unused if Height is 1)
- uiunsigned int
Value to set
- Widthsize_t
Width of row
- Heightsize_t
Number of rows
- hStreamCUstream or cudaStream_t
Stream identifier
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D8Async
cuMemsetD2D16
cuMemsetD2D16Async
cuMemsetD2D32
cuMemsetD8
cuMemsetD8Async
cuMemsetD16
cuMemsetD16Async
cuMemsetD32
cuMemsetD32Async
cudaMemset2DAsync
- cuda.cuda.cuArrayCreate(CUDA_ARRAY_DESCRIPTOR pAllocateArray: CUDA_ARRAY_DESCRIPTOR)¶
Creates a 1D or 2D CUDA array.
Creates a CUDA array according to the CUDA_ARRAY_DESCRIPTOR structure pAllocateArray and returns a handle to the new CUDA array in *pHandle. The CUDA_ARRAY_DESCRIPTOR is defined as:
typedefstruct{ unsignedintWidth; unsignedintHeight; CUarray_formatFormat; unsignedintNumChannels; }CUDA_ARRAY_DESCRIPTOR; where:
Width, and Height are the width, and height of the CUDA array (in
elements); the CUDA array is one-dimensional if height is 0, two- dimensional otherwise; - Format specifies the format of the elements; CUarray_format is defined as: typedefenumCUarray_format_enum{ CU_AD_FORMAT_UNSIGNED_INT8=0x01, CU_AD_FORMAT_UNSIGNED_INT16=0x02, CU_AD_FORMAT_UNSIGNED_INT32=0x03, CU_AD_FORMAT_SIGNED_INT8=0x08, CU_AD_FORMAT_SIGNED_INT16=0x09, CU_AD_FORMAT_SIGNED_INT32=0x0a, CU_AD_FORMAT_HALF=0x10, CU_AD_FORMAT_FLOAT=0x20 }CUarray_format; - NumChannels specifies the number of packed components per CUDA array element; it may be 1, 2, or 4;
Here are examples of CUDA array descriptions:
Description for a CUDA array of 2048 floats: CUDA_ARRAY_DESCRIPTORdesc; desc.Format=CU_AD_FORMAT_FLOAT; desc.NumChannels=1; desc.Width=2048; desc.Height=1;
Description for a 64 x 64 CUDA array of floats: CUDA_ARRAY_DESCRIPTORdesc; desc.Format=CU_AD_FORMAT_FLOAT; desc.NumChannels=1; desc.Width=64; desc.Height=64;
Description for a width x height CUDA array of 64-bit, 4x16-bit float16’s: CUDA_ARRAY_DESCRIPTORdesc; desc.Format=CU_AD_FORMAT_HALF; desc.NumChannels=4; desc.Width=width; desc.Height=height;
Description for a width x height CUDA array of 16-bit elements, each of which is two 8-bit unsigned chars: CUDA_ARRAY_DESCRIPTORarrayDesc; desc.Format=CU_AD_FORMAT_UNSIGNED_INT8; desc.NumChannels=2; desc.Width=width; desc.Height=height;
- Parameters
- pAllocateArrayCUDA_ARRAY_DESCRIPTOR
Array descriptor
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_OUT_OF_MEMORY CUDA_ERROR_UNKNOWN
- pHandleCUarray
Returned array
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cudaMallocArray
- cuda.cuda.cuArrayGetDescriptor(hArray)¶
Get a 1D or 2D CUDA array descriptor.
Returns in *pArrayDescriptor a descriptor containing information on the format and dimensions of the CUDA array hArray. It is useful for subroutines that have been passed a CUDA array, but need to know the CUDA array parameters for validation or other purposes.
- Parameters
- hArrayAny
Array to get descriptor of
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE
- pArrayDescriptorCUDA_ARRAY_DESCRIPTOR
Returned array descriptor
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cudaArrayGetInfo
- cuda.cuda.cuArrayGetSparseProperties(array)¶
Returns the layout properties of a sparse CUDA array.
Returns the layout properties of a sparse CUDA array in sparseProperties If the CUDA array is not allocated with flag CUDA_ARRAY3D_SPARSE CUDA_ERROR_INVALID_VALUE will be returned.
If the returned value in CUDA_ARRAY_SPARSE_PROPERTIES::flags contains CU_ARRAY_SPARSE_PROPERTIES_SINGLE_MIPTAIL, then CUDA_ARRAY_SPARSE_PROPERTIES::miptailSize represents the total size of the array. Otherwise, it will be zero. Also, the returned value in CUDA_ARRAY_SPARSE_PROPERTIES::miptailFirstLevel is always zero. Note that the array must have been allocated using cuArrayCreate or cuArray3DCreate. For CUDA arrays obtained using cuMipmappedArrayGetLevel, CUDA_ERROR_INVALID_VALUE will be returned. Instead, cuMipmappedArrayGetSparseProperties must be used to obtain the sparse properties of the entire CUDA mipmapped array to which array belongs to.
- Parameters
- arrayAny
CUDA array to get the sparse properties of
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- sparsePropertiesCUDA_ARRAY_SPARSE_PROPERTIES
Pointer to CUDA_ARRAY_SPARSE_PROPERTIES
- cuda.cuda.cuMipmappedArrayGetSparseProperties(mipmap)¶
Returns the layout properties of a sparse CUDA mipmapped array.
Returns the sparse array layout properties in sparseProperties If the CUDA mipmapped array is not allocated with flag CUDA_ARRAY3D_SPARSE CUDA_ERROR_INVALID_VALUE will be returned.
For non-layered CUDA mipmapped arrays, CUDA_ARRAY_SPARSE_PROPERTIES::miptailSize returns the size of the mip tail region. The mip tail region includes all mip levels whose width, height or depth is less than that of the tile. For layered CUDA mipmapped arrays, if CUDA_ARRAY_SPARSE_PROPERTIES::flags contains CU_ARRAY_SPARSE_PROPERTIES_SINGLE_MIPTAIL, then CUDA_ARRAY_SPARSE_PROPERTIES::miptailSize specifies the size of the mip tail of all layers combined. Otherwise, CUDA_ARRAY_SPARSE_PROPERTIES::miptailSize specifies mip tail size per layer. The returned value of CUDA_ARRAY_SPARSE_PROPERTIES::miptailFirstLevel is valid only if CUDA_ARRAY_SPARSE_PROPERTIES::miptailSize is non-zero.
- Parameters
- mipmapAny
CUDA mipmapped array to get the sparse properties of
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- sparsePropertiesCUDA_ARRAY_SPARSE_PROPERTIES
Pointer to CUDA_ARRAY_SPARSE_PROPERTIES
- cuda.cuda.cuArrayGetMemoryRequirements(array, device)¶
Returns the memory requirements of a CUDA array.
Returns the memory requirements of a CUDA array in memoryRequirements If the CUDA array is not allocated with flag CUDA_ARRAY3D_DEFERRED_MAPPING CUDA_ERROR_INVALID_VALUE will be returned.
The returned value in CUDA_ARRAY_MEMORY_REQUIREMENTS::size represents the total size of the CUDA array. The returned value in CUDA_ARRAY_MEMORY_REQUIREMENTS::alignment represents the alignment necessary for mapping the CUDA array.
- Parameters
- arrayAny
CUDA array to get the memory requirements of
- deviceAny
Device to get the memory requirements for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- memoryRequirementsCUDA_ARRAY_MEMORY_REQUIREMENTS
Pointer to CUDA_ARRAY_MEMORY_REQUIREMENTS
- cuda.cuda.cuMipmappedArrayGetMemoryRequirements(mipmap, device)¶
Returns the memory requirements of a CUDA mipmapped array.
Returns the memory requirements of a CUDA mipmapped array in memoryRequirements If the CUDA mipmapped array is not allocated with flag CUDA_ARRAY3D_DEFERRED_MAPPING CUDA_ERROR_INVALID_VALUE will be returned.
The returned value in CUDA_ARRAY_MEMORY_REQUIREMENTS::size represents the total size of the CUDA mipmapped array. The returned value in CUDA_ARRAY_MEMORY_REQUIREMENTS::alignment represents the alignment necessary for mapping the CUDA mipmapped array.
- Parameters
- mipmapAny
CUDA mipmapped array to get the memory requirements of
- deviceAny
Device to get the memory requirements for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- memoryRequirementsCUDA_ARRAY_MEMORY_REQUIREMENTS
Pointer to CUDA_ARRAY_MEMORY_REQUIREMENTS
- cuda.cuda.cuArrayGetPlane(hArray, unsigned int planeIdx)¶
Gets a CUDA array plane from a CUDA array.
Returns in pPlaneArray a CUDA array that represents a single format plane of the CUDA array hArray.
If planeIdx is greater than the maximum number of planes in this array or if the array does not have a multi-planar format e.g: CU_AD_FORMAT_NV12, then CUDA_ERROR_INVALID_VALUE is returned.
Note that if the hArray has format CU_AD_FORMAT_NV12, then passing in 0 for planeIdx returns a CUDA array of the same size as hArray but with one channel and CU_AD_FORMAT_UNSIGNED_INT8 as its format. If 1 is passed for planeIdx, then the returned CUDA array has half the height and width of hArray with two channels and CU_AD_FORMAT_UNSIGNED_INT8 as its format.
- Parameters
- hArrayAny
Multiplanar CUDA array
- planeIdxunsigned int
Plane index
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE
- pPlaneArrayCUarray
Returned CUDA array referenced by the planeIdx
See also
cuArrayCreate
cudaGetArrayPlane
- cuda.cuda.cuArrayDestroy(hArray)¶
Destroys a CUDA array.
Destroys the CUDA array hArray.
- Parameters
- hArrayAny
Array to destroy
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_ARRAY_IS_MAPPED CUDA_ERROR_CONTEXT_IS_DESTROYED
- None
None
See also
cuArray3DCreate
cuArray3DGetDescriptor
cuArrayCreate
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cudaFreeArray
- cuda.cuda.cuArray3DCreate(CUDA_ARRAY3D_DESCRIPTOR pAllocateArray: CUDA_ARRAY3D_DESCRIPTOR)¶
Creates a 3D CUDA array.
Creates a CUDA array according to the CUDA_ARRAY3D_DESCRIPTOR structure pAllocateArray and returns a handle to the new CUDA array in *pHandle. The CUDA_ARRAY3D_DESCRIPTOR is defined as:
typedefstruct{ unsignedintWidth; unsignedintHeight; unsignedintDepth; CUarray_formatFormat; unsignedintNumChannels; unsignedintFlags; }CUDA_ARRAY3D_DESCRIPTOR; where:
Width, Height, and Depth are the width, height, and depth of
the CUDA array (in elements); the following types of CUDA arrays can be allocated: - A 1D array is allocated if Height and Depth extents are both zero. - A 2D array is allocated if only Depth extent is zero. - A 3D array is allocated if all three extents are non-zero. - A 1D layered CUDA array is allocated if only Height is zero and the CUDA_ARRAY3D_LAYERED flag is set. Each layer is a 1D array. The number of layers is determined by the depth extent. - A 2D layered CUDA array is allocated if all three extents are non-zero and the CUDA_ARRAY3D_LAYERED flag is set. Each layer is a 2D array. The number of layers is determined by the depth extent. - A cubemap CUDA array is allocated if all three extents are non-zero and the CUDA_ARRAY3D_CUBEMAP flag is set. Width must be equal to Height, and Depth must be six. A cubemap is a special type of 2D layered CUDA array, where the six layers represent the six faces of a cube. The order of the six layers in memory is the same as that listed in CUarray_cubemap_face. - A cubemap layered CUDA array is allocated if all three extents are non-zero, and both, CUDA_ARRAY3D_CUBEMAP and CUDA_ARRAY3D_LAYERED flags are set. Width must be equal to Height, and Depth must be a multiple of six. A cubemap layered CUDA array is a special type of 2D layered CUDA array that consists of a collection of cubemaps. The first six layers represent the first cubemap, the next six layers form the second cubemap, and so on. - Format specifies the format of the elements; CUarray_format is defined as: typedefenumCUarray_format_enum{ CU_AD_FORMAT_UNSIGNED_INT8=0x01, CU_AD_FORMAT_UNSIGNED_INT16=0x02, CU_AD_FORMAT_UNSIGNED_INT32=0x03, CU_AD_FORMAT_SIGNED_INT8=0x08, CU_AD_FORMAT_SIGNED_INT16=0x09, CU_AD_FORMAT_SIGNED_INT32=0x0a, CU_AD_FORMAT_HALF=0x10, CU_AD_FORMAT_FLOAT=0x20 }CUarray_format; - NumChannels specifies the number of packed components per CUDA array element; it may be 1, 2, or 4; - Flags may be set to - CUDA_ARRAY3D_LAYERED to enable creation of layered CUDA arrays. If this flag is set, Depth specifies the number of layers, not the depth of a 3D array. - CUDA_ARRAY3D_SURFACE_LDST to enable surface references to be bound to the CUDA array. If this flag is not set, cuSurfRefSetArray will fail when attempting to bind the CUDA array to a surface reference. - CUDA_ARRAY3D_CUBEMAP to enable creation of cubemaps. If this flag is set, Width must be equal to Height, and Depth must be six. If the CUDA_ARRAY3D_LAYERED flag is also set, then Depth must be a multiple of six. - CUDA_ARRAY3D_TEXTURE_GATHER to indicate that the CUDA array will be used for texture gather. Texture gather can only be performed on 2D CUDA arrays.
Width, Height and Depth must meet certain size requirements as listed in the following table. All values are specified in elements. Note that for brevity’s sake, the full name of the device attribute is not specified. For ex., TEXTURE1D_WIDTH refers to the device attribute CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH.
Note that 2D CUDA arrays have different size requirements if the CUDA_ARRAY3D_TEXTURE_GATHER flag is set. Width and Height must not be greater than CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH and CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT respectively, in that case.
CUDA array type Valid extents that must always be met {(width range in elements), (height range), (depth range)} Valid extents with CUDA_ARRAY3D_SURFACE_LDST set {(width range in elements), (height range), (depth range)} 1D { (1,TEXTURE1D_WIDTH), 0, 0 } { (1,SURFACE1D_WIDTH), 0, 0 } 2D { (1,TEXTURE2D_WIDTH), (1,TEXTURE2D_HEIGHT), 0 } { (1,SURFACE2D_WIDTH), (1,SURFACE2D_HEIGHT), 0 } 3D { (1,TEXTURE3D_WIDTH), (1,TEXTURE3D_HEIGHT), (1,TEXTURE3D_DEPTH) } OR { (1,TEXTURE3D_WIDTH_ALTERNATE), (1,TEXTURE3D_HEIGHT_ALTERNATE), (1,TEXTURE3D_DEPTH_ALTERNATE) } { (1,SURFACE3D_WIDTH), (1,SURFACE3D_HEIGHT), (1,SURFACE3D_DEPTH) } 1D Layered { (1,TEXTURE1D_LAYERED_WIDTH), 0, (1,TEXTURE1D_LAYERED_LAYERS) } { (1,SURFACE1D_LAYERED_WIDTH), 0, (1,SURFACE1D_LAYERED_LAYERS) } 2D Layered { (1,TEXTURE2D_LAYERED_WIDTH), (1,TEXTURE2D_LAYERED_HEIGHT), (1,TEXTURE2D_LAYERED_LAYERS) } { (1,SURFACE2D_LAYERED_WIDTH), (1,SURFACE2D_LAYERED_HEIGHT), (1,SURFACE2D_LAYERED_LAYERS) } Cubemap { (1,TEXTURECUBEMAP_WIDTH), (1,TEXTURECUBEMAP_WIDTH), 6 } { (1,SURFACECUBEMAP_WIDTH), (1,SURFACECUBEMAP_WIDTH), 6 } Cubemap Layered { (1,TEXTURECUBEMAP_LAYERED_WIDTH), (1,TEXTURECUBEMAP_LAYERED_WIDTH), (1,TEXTURECUBEMAP_LAYERED_LAYERS) } { (1,SURFACECUBEMAP_LAYERED_WIDTH), (1,SURFACECUBEMAP_LAYERED_WIDTH), (1,SURFACECUBEMAP_LAYERED_LAYERS) }
Here are examples of CUDA array descriptions:
Description for a CUDA array of 2048 floats: CUDA_ARRAY3D_DESCRIPTORdesc; desc.Format=CU_AD_FORMAT_FLOAT; desc.NumChannels=1; desc.Width=2048; desc.Height=0; desc.Depth=0;
Description for a 64 x 64 CUDA array of floats: CUDA_ARRAY3D_DESCRIPTORdesc; desc.Format=CU_AD_FORMAT_FLOAT; desc.NumChannels=1; desc.Width=64; desc.Height=64; desc.Depth=0;
Description for a width x height x depth CUDA array of 64-bit, 4x16-bit float16’s: CUDA_ARRAY3D_DESCRIPTORdesc; desc.Format=CU_AD_FORMAT_HALF; desc.NumChannels=4; desc.Width=width; desc.Height=height; desc.Depth=depth;
- Parameters
- pAllocateArrayCUDA_ARRAY3D_DESCRIPTOR
3D array descriptor
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_OUT_OF_MEMORY CUDA_ERROR_UNKNOWN
- pHandleCUarray
Returned array
See also
cuArray3DGetDescriptor
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cudaMalloc3DArray
- cuda.cuda.cuArray3DGetDescriptor(hArray)¶
Get a 3D CUDA array descriptor.
Returns in *pArrayDescriptor a descriptor containing information on the format and dimensions of the CUDA array hArray. It is useful for subroutines that have been passed a CUDA array, but need to know the CUDA array parameters for validation or other purposes.
This function may be called on 1D and 2D arrays, in which case the Height and/or Depth members of the descriptor struct will be set to 0.
- Parameters
- hArrayAny
3D array to get descriptor of
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_CONTEXT_IS_DESTROYED
- pArrayDescriptorCUDA_ARRAY3D_DESCRIPTOR
Returned 3D array descriptor
See also
cuArray3DCreate
cuArrayCreate
cuArrayDestroy
cuArrayGetDescriptor
cuMemAlloc
cuMemAllocHost
cuMemAllocPitch
cuMemcpy2D
cuMemcpy2DAsync
cuMemcpy2DUnaligned
cuMemcpy3D
cuMemcpy3DAsync
cuMemcpyAtoA
cuMemcpyAtoD
cuMemcpyAtoH
cuMemcpyAtoHAsync
cuMemcpyDtoA
cuMemcpyDtoD
cuMemcpyDtoDAsync
cuMemcpyDtoH
cuMemcpyDtoHAsync
cuMemcpyHtoA
cuMemcpyHtoAAsync
cuMemcpyHtoD
cuMemcpyHtoDAsync
cuMemFree
cuMemFreeHost
cuMemGetAddressRange
cuMemGetInfo
cuMemHostAlloc
cuMemHostGetDevicePointer
cuMemsetD2D8
cuMemsetD2D16
cuMemsetD2D32
cuMemsetD8
cuMemsetD16
cuMemsetD32
cudaArrayGetInfo
- cuda.cuda.cuMipmappedArrayCreate(CUDA_ARRAY3D_DESCRIPTOR pMipmappedArrayDesc: CUDA_ARRAY3D_DESCRIPTOR, unsigned int numMipmapLevels)¶
Creates a CUDA mipmapped array.
Creates a CUDA mipmapped array according to the CUDA_ARRAY3D_DESCRIPTOR structure pMipmappedArrayDesc and returns a handle to the new CUDA mipmapped array in *pHandle. numMipmapLevels specifies the number of mipmap levels to be allocated. This value is clamped to the range [1, 1 + floor(log2(max(width, height, depth)))].
The CUDA_ARRAY3D_DESCRIPTOR is defined as:
typedefstruct{ unsignedintWidth; unsignedintHeight; unsignedintDepth; CUarray_formatFormat; unsignedintNumChannels; unsignedintFlags; }CUDA_ARRAY3D_DESCRIPTOR; where:
Width, Height, and Depth are the width, height, and depth of
the CUDA array (in elements); the following types of CUDA arrays can be allocated: - A 1D mipmapped array is allocated if Height and Depth extents are both zero. - A 2D mipmapped array is allocated if only Depth extent is zero. - A 3D mipmapped array is allocated if all three extents are non-zero. - A 1D layered CUDA mipmapped array is allocated if only Height is zero and the CUDA_ARRAY3D_LAYERED flag is set. Each layer is a 1D array. The number of layers is determined by the depth extent. - A 2D layered CUDA mipmapped array is allocated if all three extents are non-zero and the CUDA_ARRAY3D_LAYERED flag is set. Each layer is a 2D array. The number of layers is determined by the depth extent. - A cubemap CUDA mipmapped array is allocated if all three extents are non-zero and the CUDA_ARRAY3D_CUBEMAP flag is set. Width must be equal to Height, and Depth must be six. A cubemap is a special type of 2D layered CUDA array, where the six layers represent the six faces of a cube. The order of the six layers in memory is the same as that listed in CUarray_cubemap_face. - A cubemap layered CUDA mipmapped array is allocated if all three extents are non-zero, and both, CUDA_ARRAY3D_CUBEMAP and CUDA_ARRAY3D_LAYERED flags are set. Width must be equal to Height, and Depth must be a multiple of six. A cubemap layered CUDA array is a special type of 2D layered CUDA array that consists of a collection of cubemaps. The first six layers represent the first cubemap, the next six layers form the second cubemap, and so on. - Format specifies the format of the elements; CUarray_format is defined as: typedefenumCUarray_format_enum{ CU_AD_FORMAT_UNSIGNED_INT8=0x01, CU_AD_FORMAT_UNSIGNED_INT16=0x02, CU_AD_FORMAT_UNSIGNED_INT32=0x03, CU_AD_FORMAT_SIGNED_INT8=0x08, CU_AD_FORMAT_SIGNED_INT16=0x09, CU_AD_FORMAT_SIGNED_INT32=0x0a, CU_AD_FORMAT_HALF=0x10, CU_AD_FORMAT_FLOAT=0x20 }CUarray_format; - NumChannels specifies the number of packed components per CUDA array element; it may be 1, 2, or 4; - Flags may be set to - CUDA_ARRAY3D_LAYERED to enable creation of layered CUDA mipmapped arrays. If this flag is set, Depth specifies the number of layers, not the depth of a 3D array. - CUDA_ARRAY3D_SURFACE_LDST to enable surface references to be bound to individual mipmap levels of the CUDA mipmapped array. If this flag is not set, cuSurfRefSetArray will fail when attempting to bind a mipmap level of the CUDA mipmapped array to a surface reference. - CUDA_ARRAY3D_CUBEMAP to enable creation of mipmapped cubemaps. If this flag is set, Width must be equal to Height, and Depth must be six. If the CUDA_ARRAY3D_LAYERED flag is also set, then Depth must be a multiple of six. - CUDA_ARRAY3D_TEXTURE_GATHER to indicate that the CUDA mipmapped array will be used for texture gather. Texture gather can only be performed on 2D CUDA mipmapped arrays.
Width, Height and Depth must meet certain size requirements as listed in the following table. All values are specified in elements. Note that for brevity’s sake, the full name of the device attribute is not specified. For ex., TEXTURE1D_MIPMAPPED_WIDTH refers to the device attribute CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH.
CUDA array type Valid extents that must always be met {(width range in elements), (height range), (depth range)} Valid extents with CUDA_ARRAY3D_SURFACE_LDST set {(width range in elements), (height range), (depth range)} 1D { (1,TEXTURE1D_MIPMAPPED_WIDTH), 0, 0 } { (1,SURFACE1D_WIDTH), 0, 0 } 2D { (1,TEXTURE2D_MIPMAPPED_WIDTH), (1,TEXTURE2D_MIPMAPPED_HEIGHT), 0 } { (1,SURFACE2D_WIDTH), (1,SURFACE2D_HEIGHT), 0 } 3D { (1,TEXTURE3D_WIDTH), (1,TEXTURE3D_HEIGHT), (1,TEXTURE3D_DEPTH) } OR { (1,TEXTURE3D_WIDTH_ALTERNATE), (1,TEXTURE3D_HEIGHT_ALTERNATE), (1,TEXTURE3D_DEPTH_ALTERNATE) } { (1,SURFACE3D_WIDTH), (1,SURFACE3D_HEIGHT), (1,SURFACE3D_DEPTH) } 1D Layered { (1,TEXTURE1D_LAYERED_WIDTH), 0, (1,TEXTURE1D_LAYERED_LAYERS) } { (1,SURFACE1D_LAYERED_WIDTH), 0, (1,SURFACE1D_LAYERED_LAYERS) } 2D Layered { (1,TEXTURE2D_LAYERED_WIDTH), (1,TEXTURE2D_LAYERED_HEIGHT), (1,TEXTURE2D_LAYERED_LAYERS) } { (1,SURFACE2D_LAYERED_WIDTH), (1,SURFACE2D_LAYERED_HEIGHT), (1,SURFACE2D_LAYERED_LAYERS) } Cubemap { (1,TEXTURECUBEMAP_WIDTH), (1,TEXTURECUBEMAP_WIDTH), 6 } { (1,SURFACECUBEMAP_WIDTH), (1,SURFACECUBEMAP_WIDTH), 6 } Cubemap Layered { (1,TEXTURECUBEMAP_LAYERED_WIDTH), (1,TEXTURECUBEMAP_LAYERED_WIDTH), (1,TEXTURECUBEMAP_LAYERED_LAYERS) } { (1,SURFACECUBEMAP_LAYERED_WIDTH), (1,SURFACECUBEMAP_LAYERED_WIDTH), (1,SURFACECUBEMAP_LAYERED_LAYERS) }
- Parameters
- pMipmappedArrayDescCUDA_ARRAY3D_DESCRIPTOR
mipmapped array descriptor
- numMipmapLevelsunsigned int
Number of mipmap levels
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_OUT_OF_MEMORY CUDA_ERROR_UNKNOWN
- pHandleCUmipmappedArray
Returned mipmapped array
See also
cuMipmappedArrayDestroy
cuMipmappedArrayGetLevel
cuArrayCreate
cudaMallocMipmappedArray
- cuda.cuda.cuMipmappedArrayGetLevel(hMipmappedArray, unsigned int level)¶
Gets a mipmap level of a CUDA mipmapped array.
Returns in *pLevelArray a CUDA array that represents a single mipmap level of the CUDA mipmapped array hMipmappedArray.
If level is greater than the maximum number of levels in this mipmapped array, CUDA_ERROR_INVALID_VALUE is returned.
- Parameters
- hMipmappedArrayAny
CUDA mipmapped array
- levelunsigned int
Mipmap level
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE
- pLevelArrayCUarray
Returned mipmap level CUDA array
See also
cuMipmappedArrayCreate
cuMipmappedArrayDestroy
cuArrayCreate
cudaGetMipmappedArrayLevel
- cuda.cuda.cuMipmappedArrayDestroy(hMipmappedArray)¶
Destroys a CUDA mipmapped array.
Destroys the CUDA mipmapped array hMipmappedArray.
- Parameters
- hMipmappedArrayAny
Mipmapped array to destroy
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_ARRAY_IS_MAPPED CUDA_ERROR_CONTEXT_IS_DESTROYED
- None
None
See also
cuMipmappedArrayCreate
cuMipmappedArrayGetLevel
cuArrayCreate
cudaFreeMipmappedArray
Virtual Memory Management¶
This section describes the virtual memory management functions of the low-level CUDA driver application programming interface.
- cuda.cuda.cuMemAddressReserve(size_t size, size_t alignment, addr, unsigned long long flags)¶
Allocate an address range reservation.
Reserves a virtual address range based on the given parameters, giving the starting address of the range in ptr. This API requires a system that supports UVA. The size and address parameters must be a multiple of the host page size and the alignment must be a power of two or zero for default alignment.
- Parameters
- sizesize_t
Size of the reserved virtual address range requested
- alignmentsize_t
Alignment of the reserved virtual address range requested
- addrAny
Fixed starting address range requested
- flagsunsigned long long
Currently unused, must be zero
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_OUT_OF_MEMORY CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_PERMITTED CUDA_ERROR_NOT_SUPPORTED
- ptrCUdeviceptr
Resulting pointer to start of virtual address range allocated
See also
- cuda.cuda.cuMemAddressFree(ptr, size_t size)¶
Free an address range reservation.
Frees a virtual address range reserved by cuMemAddressReserve. The size must match what was given to memAddressReserve and the ptr given must match what was returned from memAddressReserve.
- Parameters
- ptrAny
Starting address of the virtual address range to free
- sizesize_t
Size of the virtual address region to free
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_PERMITTED CUDA_ERROR_NOT_SUPPORTED
- None
None
See also
- cuda.cuda.cuMemCreate(size_t size, CUmemAllocationProp prop: CUmemAllocationProp, unsigned long long flags)¶
Create a CUDA memory handle representing a memory allocation of a given size described by the given properties.
This creates a memory allocation on the target device specified through the prop strcuture. The created allocation will not have any device or host mappings. The generic memory handle for the allocation can be mapped to the address space of calling process via cuMemMap. This handle cannot be transmitted directly to other processes (see cuMemExportToShareableHandle). On Windows, the caller must also pass an LPSECURITYATTRIBUTE in prop to be associated with this handle which limits or allows access to this handle for a recepient process (see CUmemAllocationProp::win32HandleMetaData for more). The size of this allocation must be a multiple of the the value given via cuMemGetAllocationGranularity with the CU_MEM_ALLOC_GRANULARITY_MINIMUM flag. If CUmemAllocationProp::allocFlags::usage contains CU_MEM_CREATE_USAGE_TILE_POOL flag then the memory allocation is intended only to be used as backing tile pool for sparse CUDA arrays and sparse CUDA mipmapped arrays. (see cuMemMapArrayAsync).
- Parameters
- sizesize_t
Size of the allocation requested
- propCUmemAllocationProp
Properties of the allocation to create.
- flagsunsigned long long
flags for future use, must be zero now.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_OUT_OF_MEMORY CUDA_ERROR_INVALID_DEVICE CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_PERMITTED CUDA_ERROR_NOT_SUPPORTED
- handleCUmemGenericAllocationHandle
Value of handle returned. All operations on this allocation are to be performed using this handle.
- cuda.cuda.cuMemRelease(handle)¶
Release a memory handle representing a memory allocation which was previously allocated through cuMemCreate.
Frees the memory that was allocated on a device through cuMemCreate.
The memory allocation will be freed when all outstanding mappings to the memory are unmapped and when all outstanding references to the handle (including it’s shareable counterparts) are also released. The generic memory handle can be freed when there are still outstanding mappings made with this handle. Each time a recepient process imports a shareable handle, it needs to pair it with cuMemRelease for the handle to be freed. If handle is not a valid handle the behavior is undefined.
- Parameters
- handleAny
Value of handle which was returned previously by cuMemCreate.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_PERMITTED CUDA_ERROR_NOT_SUPPORTED
- None
None
See also
- cuda.cuda.cuMemMap(ptr, size_t size, size_t offset, handle, unsigned long long flags)¶
Maps an allocation handle to a reserved virtual address range.
Maps bytes of memory represented by handle starting from byte offset to size to address range [addr, addr + size]. This range must be an address reservation previously reserved with cuMemAddressReserve, and offset + size must be less than the size of the memory allocation. Both ptr, size, and offset must be a multiple of the value given via cuMemGetAllocationGranularity with the CU_MEM_ALLOC_GRANULARITY_MINIMUM flag.
Please note calling cuMemMap does not make the address accessible, the caller needs to update accessibility of a contiguous mapped VA range by calling cuMemSetAccess.
Once a recipient process obtains a shareable memory handle from cuMemImportFromShareableHandle, the process must use cuMemMap to map the memory into its address ranges before setting accessibility with cuMemSetAccess.
cuMemMap can only create mappings on VA range reservations that are not currently mapped.
- Parameters
- ptrAny
Address where memory will be mapped.
- sizesize_t
Size of the memory mapping.
- offsetsize_t
Offset into the memory represented by - handle from which to start mapping - Note: currently must be zero.
- handleAny
Handle to a shareable memory
- flagsunsigned long long
flags for future use, must be zero now.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_DEVICE CUDA_ERROR_OUT_OF_MEMORY CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_PERMITTED CUDA_ERROR_NOT_SUPPORTED
- None
None
- cuda.cuda.cuMemMapArrayAsync(mapInfoList: List[CUarrayMapInfo], unsigned int count, hStream)¶
Maps or unmaps subregions of sparse CUDA arrays and sparse CUDA mipmapped arrays.
Performs map or unmap operations on subregions of sparse CUDA arrays and sparse CUDA mipmapped arrays. Each operation is specified by a CUarrayMapInfo entry in the mapInfoList array of size count. The structure CUarrayMapInfo is defined as follow: typedefstructCUarrayMapInfo_st{ CUresourcetyperesourceType; union{ CUmipmappedArraymipmap; CUarrayarray; }resource; CUarraySparseSubresourceTypesubresourceType; union{ struct{ unsignedintlevel; unsignedintlayer; unsignedintoffsetX; unsignedintoffsetY; unsignedintoffsetZ; unsignedintextentWidth; unsignedintextentHeight; unsignedintextentDepth; }sparseLevel; struct{ unsignedintlayer; unsignedlonglongoffset; unsignedlonglongsize; }miptail; }subresource; CUmemOperationTypememOperationType; CUmemHandleTypememHandleType; union{ CUmemGenericAllocationHandlememHandle; }memHandle; unsignedlonglongoffset; unsignedintdeviceBitMask; unsignedintflags; unsignedintreserved[2]; }CUarrayMapInfo;
where CUarrayMapInfo::resourceType specifies the type of resource to be operated on. If CUarrayMapInfo::resourceType is set to CUresourcetype::CU_RESOURCE_TYPE_ARRAY then CUarrayMapInfo::resource::array must be set to a valid sparse CUDA array handle. The CUDA array must be either a 2D, 2D layered or 3D CUDA array and must have been allocated using cuArrayCreate or cuArray3DCreate with the flag CUDA_ARRAY3D_SPARSE
or CUDA_ARRAY3D_DEFERRED_MAPPING.
For CUDA arrays obtained using cuMipmappedArrayGetLevel, CUDA_ERROR_INVALID_VALUE will be returned. If CUarrayMapInfo::resourceType is set to CUresourcetype::CU_RESOURCE_TYPE_MIPMAPPED_ARRAY then CUarrayMapInfo::resource::mipmap must be set to a valid sparse CUDA mipmapped array handle. The CUDA mipmapped array must be either a 2D, 2D layered or 3D CUDA mipmapped array and must have been allocated using cuMipmappedArrayCreate with the flag CUDA_ARRAY3D_SPARSE
or CUDA_ARRAY3D_DEFERRED_MAPPING.
CUarrayMapInfo::subresourceType specifies the type of subresource within the resource. CUarraySparseSubresourceType_enum is defined as: typedefenumCUarraySparseSubresourceType_enum{ CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_SPARSE_LEVEL=0, CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_MIPTAIL=1 }CUarraySparseSubresourceType;
where CUarraySparseSubresourceType::CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_SP ARSE_LEVEL indicates a sparse-miplevel which spans at least one tile in every dimension. The remaining miplevels which are too small to span at least one tile in any dimension constitute the mip tail region as indicated by CUarraySparseSubresourceType::CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_MIPTAIL subresource type.
If CUarrayMapInfo::subresourceType is set to CUarraySparseSubresourceTy pe::CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_SPARSE_LEVEL then CUarrayMapInfo::subresource::sparseLevel struct must contain valid array subregion offsets and extents. The CUarrayMapInfo::subresource::sparseLevel::offsetX, CUarrayMapInfo::subresource::sparseLevel::offsetY and CUarrayMapInfo::subresource::sparseLevel::offsetZ must specify valid X, Y and Z offsets respectively. The CUarrayMapInfo::subresource::sparseLevel::extentWidth, CUarrayMapInfo::subresource::sparseLevel::extentHeight and CUarrayMapInfo::subresource::sparseLevel::extentDepth must specify valid width, height and depth extents respectively. These offsets and extents must be aligned to the corresponding tile dimension. For CUDA mipmapped arrays CUarrayMapInfo::subresource::sparseLevel::level must specify a valid mip level index. Otherwise, must be zero. For layered CUDA arrays and layered CUDA mipmapped arrays CUarrayMapInfo::subresource::sparseLevel::layer must specify a valid layer index. Otherwise, must be zero. CUarrayMapInfo::subresource::sparseLevel::offsetZ must be zero and CUarrayMapInfo::subresource::sparseLevel::extentDepth must be set to 1 for 2D and 2D layered CUDA arrays and CUDA mipmapped arrays. Tile extents can be obtained by calling cuArrayGetSparseProperties and cuMipmappedArrayGetSparseProperties
If CUarrayMapInfo::subresourceType is set to CUarraySparseSubresourceType::CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_MIPTAIL then CUarrayMapInfo::subresource::miptail struct must contain valid mip tail offset in CUarrayMapInfo::subresource::miptail::offset and size in CUarrayMapInfo::subresource::miptail::size. Both, mip tail offset and mip tail size must be aligned to the tile size. For layered CUDA mipmapped arrays which don’t have the flag CU_ARRAY_SPARSE_PROPERTIES_SINGLE_MIPTAIL set in CUDA_ARRAY_SPARSE_PROPERTIES::flags as returned by cuMipmappedArrayGetSparseProperties, CUarrayMapInfo::subresource::miptail::layer must specify a valid layer index. Otherwise, must be zero.
If CUarrayMapInfo::resource::array or CUarrayMapInfo::resource::mipmap was created with CUDA_ARRAY3D_DEFERRED_MAPPING flag set the CUarrayMapInfo::subresourceType and the contents of CUarrayMapInfo::subresource will be ignored.
CUarrayMapInfo::memOperationType specifies the type of operation. CUmemOperationType is defined as: typedefenumCUmemOperationType_enum{ CU_MEM_OPERATION_TYPE_MAP=1, CU_MEM_OPERATION_TYPE_UNMAP=2 }CUmemOperationType; If CUarrayMapInfo::memOperationType is set to CUmemOperationType::CU_MEM_OPERATION_TYPE_MAP then the subresource will be mapped onto the tile pool memory specified by CUarrayMapInfo::memHandle at offset CUarrayMapInfo::offset. The tile pool allocation has to be created by specifying the CU_MEM_CREATE_USAGE_TILE_POOL flag when calling cuMemCreate. Also, CUarrayMapInfo::memHandleType must be set to CUmemHandleType::CU_MEM_HANDLE_TYPE_GENERIC.
If CUarrayMapInfo::memOperationType is set to CUmemOperationType::CU_MEM_OPERATION_TYPE_UNMAP then an unmapping operation is performed. CUarrayMapInfo::memHandle must be NULL.
CUarrayMapInfo::deviceBitMask specifies the list of devices that must map or unmap physical memory. Currently, this mask must have exactly one bit set, and the corresponding device must match the device associated with the stream. If CUarrayMapInfo::memOperationType is set to CUmemOperationType::CU_MEM_OPERATION_TYPE_MAP, the device must also match the device associated with the tile pool memory allocation as specified by CUarrayMapInfo::memHandle.
CUarrayMapInfo::flags and CUarrayMapInfo::reserved[] are unused and must be set to zero.
- Parameters
- mapInfoListList[CUarrayMapInfo]
List of CUarrayMapInfo
- countunsigned int
Count of CUarrayMapInfo in mapInfoList
- hStreamCUstream or cudaStream_t
Stream identifier for the stream to use for map or unmap operations
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE
- None
None
- cuda.cuda.cuMemUnmap(ptr, size_t size)¶
Unmap the backing memory of a given address range.
The range must be the entire contiguous address range that was mapped to. In other words, cuMemUnmap cannot unmap a sub-range of an address range mapped by cuMemCreate / cuMemMap. Any backing memory allocations will be freed if there are no existing mappings and there are no unreleased memory handles.
When cuMemUnmap returns successfully the address range is converted to an address reservation and can be used for a future calls to cuMemMap. Any new mapping to this virtual address will need to have access granted through cuMemSetAccess, as all mappings start with no accessibility setup.
- Parameters
- ptrAny
Starting address for the virtual address range to unmap
- sizesize_t
Size of the virtual address range to unmap
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_PERMITTED CUDA_ERROR_NOT_SUPPORTED
- None
None
See also
- cuda.cuda.cuMemSetAccess(ptr, size_t size, desc: List[CUmemAccessDesc], size_t count)¶
Set the access flags for each location specified in desc for the given virtual address range.
Given the virtual address range via ptr and size, and the locations in the array given by desc and count, set the access flags for the target locations. The range must be a fully mapped address range containing all allocations created by cuMemMap / cuMemCreate.
- Parameters
- ptrAny
Starting address for the virtual address range
- sizesize_t
Length of the virtual address range
- descList[CUmemAccessDesc]
Array of CUmemAccessDesc that describe how to change the - mapping for each location specified
- countsize_t
Number of CUmemAccessDesc in desc
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_DEVICE CUDA_ERROR_NOT_SUPPORTED
- None
None
See also
- cuda.cuda.cuMemGetAccess(CUmemLocation location: CUmemLocation, ptr)¶
Get the access flags set for the given location and ptr.
- Parameters
- locationCUmemLocation
Location in which to check the flags for
- ptrAny
Address in which to check the access flags for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_DEVICE CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_PERMITTED CUDA_ERROR_NOT_SUPPORTED
- flagsunsigned long long
Flags set for this location
See also
Exports an allocation to a requested shareable handle type.
Given a CUDA memory handle, create a shareable memory allocation handle that can be used to share the memory with other processes. The recipient process can convert the shareable handle back into a CUDA memory handle using cuMemImportFromShareableHandle and map it with cuMemMap. The implementation of what this handle is and how it can be transferred is defined by the requested handle type in handleType
Once all shareable handles are closed and the allocation is released, the allocated memory referenced will be released back to the OS and uses of the CUDA handle afterward will lead to undefined behavior.
This API can also be used in conjunction with other APIs (e.g. Vulkan, OpenGL) that support importing memory from the shareable type
- Parameters
- shareableHandleAny
Pointer to the location in which to store the requested handle type
- handleAny
CUDA handle for the memory allocation
- handleTypeCUmemAllocationHandleType
Type of shareable handle requested (defines type and size of the shareableHandle output parameter)
- flagsunsigned long long
Reserved, must be zero
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_PERMITTED CUDA_ERROR_NOT_SUPPORTED
- None
None
See also
Imports an allocation from a requested shareable handle type.
If the current process cannot support the memory described by this shareable handle, this API will error as CUDA_ERROR_NOT_SUPPORTED.
- Parameters
- osHandleAny
Shareable Handle representing the memory allocation that is to be imported.
- shHandleTypeCUmemAllocationHandleType
handle type of the exported handle CUmemAllocationHandleType.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_PERMITTED CUDA_ERROR_NOT_SUPPORTED
- handleCUmemGenericAllocationHandle
CUDA Memory handle for the memory allocation.
Notes
Importing shareable handles exported from some graphics APIs(VUlkan, OpenGL, etc) created on devices under an SLI group may not be supported, and thus this API will return CUDA_ERROR_NOT_SUPPORTED. There is no guarantee that the contents of handle will be the same CUDA memory handle for the same given OS shareable handle, or the same underlying allocation.
- cuda.cuda.cuMemGetAllocationGranularity(CUmemAllocationProp prop: CUmemAllocationProp, option: CUmemAllocationGranularity_flags)¶
Calculates either the minimal or recommended granularity.
Calculates either the minimal or recommended granularity for a given allocation specification and returns it in granularity. This granularity can be used as a multiple for alignment, size, or address mapping.
- Parameters
- propCUmemAllocationProp
Property for which to determine the granularity for
- optionCUmemAllocationGranularity_flags
Determines which granularity to return
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_PERMITTED CUDA_ERROR_NOT_SUPPORTED
- granularityint
Returned granularity.
See also
- cuda.cuda.cuMemGetAllocationPropertiesFromHandle(handle)¶
Retrieve the contents of the property structure defining properties for this handle.
- Parameters
- handleAny
Handle which to perform the query on
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_PERMITTED CUDA_ERROR_NOT_SUPPORTED
- propCUmemAllocationProp
Pointer to a properties structure which will hold the information about this handle
- cuda.cuda.cuMemRetainAllocationHandle(addr)¶
Given an address addr, returns the allocation handle of the backing memory allocation.
The handle is guaranteed to be the same handle value used to map the memory. If the address requested is not mapped, the function will fail. The returned handle must be released with corresponding number of calls to cuMemRelease.
- Parameters
- addrAny
Memory address to query, that has been mapped previously.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_PERMITTED CUDA_ERROR_NOT_SUPPORTED
- handleCUmemGenericAllocationHandle
CUDA Memory handle for the backing memory allocation.
See also
Notes
The address addr, can be any address in a range previously mapped by cuMemMap, and not necessarily the start address.
Stream Ordered Memory Allocator¶
This section describes the stream ordered memory allocator exposed by the low-level CUDA driver application programming interface.
overview
The asynchronous allocator allows the user to allocate and free in stream order. All asynchronous accesses of the allocation must happen between the stream executions of the allocation and the free. If the memory is accessed outside of the promised stream order, a use before allocation / use after free error will cause undefined behavior. The allocator is free to reallocate the memory as long as it can guarantee that compliant memory accesses will not overlap temporally. The allocator may refer to internal stream ordering as well as inter-stream dependencies (such as CUDA events and null stream dependencies) when establishing the temporal guarantee. The allocator may also insert inter-stream dependencies to establish the temporal guarantee.
Supported Platforms
Whether or not a device supports the integrated stream ordered memory allocator may be queried by calling cuDeviceGetAttribute() with the device attribute CU_DEVICE_ATTRIBUTE_MEMORY_POOLS_SUPPORTED
- cuda.cuda.cuMemFreeAsync(dptr, hStream)¶
Frees memory with stream ordered semantics.
Inserts a free operation into hStream. The allocation must not be accessed after stream execution reaches the free. After this API returns, accessing the memory from any subsequent work launched on the GPU or querying its pointer attributes results in undefined behavior.
- Parameters
- dptrAny
memory to free
- hStreamCUstream or cudaStream_t
The stream establishing the stream ordering contract.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT (default stream specified with no current context) CUDA_ERROR_NOT_SUPPORTED
- None
None
Notes
During stream capture, this function results in the creation of a free node and must therefore be passed the address of a graph allocation.
- cuda.cuda.cuMemAllocAsync(size_t bytesize, hStream)¶
Allocates memory with stream ordered semantics.
Inserts an allocation operation into hStream. A pointer to the allocated memory is returned immediately in *dptr. The allocation must not be accessed until the the allocation operation completes. The allocation comes from the memory pool current to the stream’s device.
- Parameters
- bytesizesize_t
Number of bytes to allocate
- hStreamCUstream or cudaStream_t
The stream establishing the stream ordering contract and the memory pool to allocate from
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT (default stream specified with no current context) CUDA_ERROR_NOT_SUPPORTED CUDA_ERROR_OUT_OF_MEMORY
- dptrCUdeviceptr
Returned device pointer
See also
Notes
During stream capture, this function results in the creation of an allocation node. In this case, the allocation is owned by the graph instead of the memory pool. The memory pool’s properties are used to set the node’s creation parameters.
- cuda.cuda.cuMemPoolTrimTo(pool, size_t minBytesToKeep)¶
Tries to release memory back to the OS.
Releases memory back to the OS until the pool contains fewer than minBytesToKeep reserved bytes, or there is no more memory that the allocator can safely release. The allocator cannot release OS allocations that back outstanding asynchronous allocations. The OS allocations may happen at different granularity from the user allocations.
- Parameters
- poolAny
The memory pool to trim
- minBytesToKeepsize_t
If the pool has less than minBytesToKeep reserved, the TrimTo operation is a no-op. Otherwise the pool will be guaranteed to have at least minBytesToKeep bytes reserved after the operation.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- None
None
See also
Notes
: Allocations that have been asynchronously freed but whose completion has not been observed on the host (eg. by a synchronize) can count as outstanding.
- cuda.cuda.cuMemPoolSetAttribute(pool, attr: CUmemPool_attribute, value)¶
Sets attributes of a memory pool.
Supported attributes are: - CU_MEMPOOL_ATTR_RELEASE_THRESHOLD: (value type = cuuint64_t) Amount of reserved memory in bytes to hold onto before trying to release memory back to the OS. When more than the release threshold bytes of memory are held by the memory pool, the allocator will try to release memory back to the OS on the next call to stream, event or context synchronize. (default 0) - CU_MEMPOOL_ATTR_REUSE_FOLLOW_EVENT_DEPENDENCIES: (value type = int) Allow cuMemAllocAsync to use memory asynchronously freed in another stream as long as a stream ordering dependency of the allocating stream on the free action exists. Cuda events and null stream interactions can create the required stream ordered dependencies. (default enabled) - CU_MEMPOOL_ATTR_REUSE_ALLOW_OPPORTUNISTIC: (value type = int) Allow reuse of already completed frees when there is no dependency between the free and allocation. (default enabled) - CU_MEMPOOL_ATTR_REUSE_ALLOW_INTERNAL_DEPENDENCIES: (value type = int) Allow cuMemAllocAsync to insert new stream dependencies in order to establish the stream ordering required to reuse a piece of memory released by cuMemFreeAsync (default enabled). - CU_MEMPOOL_ATTR_RESERVED_MEM_HIGH: (value type = cuuint64_t) Reset the high watermark that tracks the amount of backing memory that was allocated for the memory pool. It is illegal to set this attribute to a non-zero value. - CU_MEMPOOL_ATTR_USED_MEM_HIGH: (value type = cuuint64_t) Reset the high watermark that tracks the amount of used memory that was allocated for the memory pool.
- Parameters
- poolAny
The memory pool to modify
- attrCUmemPool_attribute
The attribute to modify
- valueAny
Pointer to the value to assign
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- None
None
- cuda.cuda.cuMemPoolGetAttribute(pool, attr: CUmemPool_attribute)¶
Gets attributes of a memory pool.
Supported attributes are: - CU_MEMPOOL_ATTR_RELEASE_THRESHOLD: (value type = cuuint64_t) Amount of reserved memory in bytes to hold onto before trying to release memory back to the OS. When more than the release threshold bytes of memory are held by the memory pool, the allocator will try to release memory back to the OS on the next call to stream, event or context synchronize. (default 0) - CU_MEMPOOL_ATTR_REUSE_FOLLOW_EVENT_DEPENDENCIES: (value type = int) Allow cuMemAllocAsync to use memory asynchronously freed in another stream as long as a stream ordering dependency of the allocating stream on the free action exists. Cuda events and null stream interactions can create the required stream ordered dependencies. (default enabled) - CU_MEMPOOL_ATTR_REUSE_ALLOW_OPPORTUNISTIC: (value type = int) Allow reuse of already completed frees when there is no dependency between the free and allocation. (default enabled) - CU_MEMPOOL_ATTR_REUSE_ALLOW_INTERNAL_DEPENDENCIES: (value type = int) Allow cuMemAllocAsync to insert new stream dependencies in order to establish the stream ordering required to reuse a piece of memory released by cuMemFreeAsync (default enabled). - CU_MEMPOOL_ATTR_RESERVED_MEM_CURRENT: (value type = cuuint64_t) Amount of backing memory currently allocated for the mempool - CU_MEMPOOL_ATTR_RESERVED_MEM_HIGH: (value type = cuuint64_t) High watermark of backing memory allocated for the mempool since the last time it was reset. - CU_MEMPOOL_ATTR_USED_MEM_CURRENT: (value type = cuuint64_t) Amount of memory from the pool that is currently in use by the application. - CU_MEMPOOL_ATTR_USED_MEM_HIGH: (value type = cuuint64_t) High watermark of the amount of memory from the pool that was in use by the application.
- Parameters
- poolAny
The memory pool to get attributes of
- attrCUmemPool_attribute
The attribute to get
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- valueAny
Retrieved value
- cuda.cuda.cuMemPoolSetAccess(pool, map: List[CUmemAccessDesc], size_t count)¶
Controls visibility of pools between devices.
- Parameters
- poolAny
The pool being modified
- mapList[CUmemAccessDesc]
Array of access descriptors. Each descriptor instructs the access to enable for a single gpu.
- countsize_t
Number of descriptors in the map array.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- None
None
- cuda.cuda.cuMemPoolGetAccess(memPool, CUmemLocation location: CUmemLocation)¶
Returns the accessibility of a pool from a device.
Returns the accessibility of the pool’s memory from the specified location.
- Parameters
- memPoolCUmemoryPool or cudaMemPool_t
the pool being queried
- locationCUmemLocation
the location accessing the pool
- Returns
- CUresult
- flagsCUmemAccess_flags
the accessibility of the pool from the specified location
- cuda.cuda.cuMemPoolCreate(CUmemPoolProps poolProps: CUmemPoolProps)¶
Creates a memory pool.
Creates a CUDA memory pool and returns the handle in pool. The poolProps determines the properties of the pool such as the backing device and IPC capabilities.
By default, the pool’s memory will be accessible from the device it is allocated on.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE CUDA_ERROR_OUT_OF_MEMORY CUDA_ERROR_NOT_SUPPORTED
- None
None
See also
Notes
Specifying CU_MEM_HANDLE_TYPE_NONE creates a memory pool that will not support IPC.
- cuda.cuda.cuMemPoolDestroy(pool)¶
Destroys the specified memory pool.
If any pointers obtained from this pool haven’t been freed or the pool has free operations that haven’t completed when cuMemPoolDestroy is invoked, the function will return immediately and the resources associated with the pool will be released automatically once there are no more outstanding allocations.
Destroying the current mempool of a device sets the default mempool of that device as the current mempool for that device.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- None
None
See also
Notes
A device’s default memory pool cannot be destroyed.
- cuda.cuda.cuMemAllocFromPoolAsync(size_t bytesize, pool, hStream)¶
Allocates memory from a specified pool with stream ordered semantics.
Inserts an allocation operation into hStream. A pointer to the allocated memory is returned immediately in *dptr. The allocation must not be accessed until the the allocation operation completes. The allocation comes from the specified memory pool.
- Parameters
- bytesizesize_t
Number of bytes to allocate
- poolAny
The pool to allocate from
- hStreamCUstream or cudaStream_t
The stream establishing the stream ordering semantic
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT (default stream specified with no current context) CUDA_ERROR_NOT_SUPPORTED CUDA_ERROR_OUT_OF_MEMORY
- dptrCUdeviceptr
Returned device pointer
See also
Notes
During stream capture, this function results in the creation of an allocation node. In this case, the allocation is owned by the graph instead of the memory pool. The memory pool’s properties are used to set the node’s creation parameters.
Exports a memory pool to the requested handle type.
Given an IPC capable mempool, create an OS handle to share the pool with another process. A recipient process can convert the shareable handle into a mempool with cuMemPoolImportFromShareableHandle. Individual pointers can then be shared with the cuMemPoolExportPointer and cuMemPoolImportPointer APIs. The implementation of what the shareable handle is and how it can be transferred is defined by the requested handle type.
- Parameters
- poolAny
pool to export
- handleTypeCUmemAllocationHandleType
the type of handle to create
- flagsunsigned long long
must be 0
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_OUT_OF_MEMORY
- handle_outint
Returned OS handle
See also
Notes
: To create an IPC capable mempool, create a mempool with a CUmemAllocationHandleType other than CU_MEM_HANDLE_TYPE_NONE.
imports a memory pool from a shared handle.
Specific allocations can be imported from the imported pool with cuMemPoolImportPointer.
- Parameters
- handleAny
OS handle of the pool to open
- handleTypeCUmemAllocationHandleType
The type of handle being imported
- flagsunsigned long long
must be 0
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_OUT_OF_MEMORY
- pool_outCUmemoryPool
Returned memory pool
Notes
Imported memory pools do not support creating new allocations. As such imported memory pools may not be used in cuDeviceSetMemPool or cuMemAllocFromPoolAsync calls.
- cuda.cuda.cuMemPoolExportPointer(ptr)¶
Export data to share a memory pool allocation between processes.
Constructs shareData_out for sharing a specific allocation from an already shared memory pool. The recipient process can import the allocation with the cuMemPoolImportPointer api. The data is not a handle and may be shared through any IPC mechanism.
- Parameters
- ptrAny
pointer to memory being exported
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_OUT_OF_MEMORY
- shareData_outCUmemPoolPtrExportData
Returned export data
- cuda.cuda.cuMemPoolImportPointer(pool, CUmemPoolPtrExportData shareData: CUmemPoolPtrExportData)¶
Import a memory pool allocation from another process.
Returns in ptr_out a pointer to the imported memory. The imported memory must not be accessed before the allocation operation completes in the exporting process. The imported memory must be freed from all importing processes before being freed in the exporting process. The pointer may be freed with cuMemFree or cuMemFreeAsync. If cuMemFreeAsync is used, the free must be completed on the importing process before the free operation on the exporting process.
- Parameters
- poolAny
pool from which to import
- shareDataCUmemPoolPtrExportData
data specifying the memory to import
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_OUT_OF_MEMORY
- ptr_outCUdeviceptr
pointer to imported memory
Notes
The cuMemFreeAsync api may be used in the exporting process before the cuMemFreeAsync operation completes in its stream as long as the cuMemFreeAsync in the exporting process specifies a stream with a stream dependency on the importing process’s cuMemFreeAsync.
Unified Addressing¶
This section describes the unified addressing functions of the low-level CUDA driver application programming interface.
Overview
CUDA devices can share a unified address space with the host. For these devices there is no distinction between a device pointer and a host pointer – the same pointer value may be used to access memory from the host program and from a kernel running on the device (with exceptions enumerated below).
Supported Platforms
Whether or not a device supports unified addressing may be queried by calling cuDeviceGetAttribute() with the device attribute CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING. Unified addressing is automatically enabled in 64-bit processes
Looking Up Information from Pointer Values
It is possible to look up information about the memory which backs a pointer value. For instance, one may want to know if a pointer points to host or device memory. As another example, in the case of device memory, one may want to know on which CUDA device the memory resides. These properties may be queried using the function cuPointerGetAttribute() Since pointers are unique, it is not necessary to specify information about the pointers specified to the various copy functions in the CUDA API. The function cuMemcpy() may be used to perform a copy between two pointers, ignoring whether they point to host or device memory (making cuMemcpyHtoD(), cuMemcpyDtoD(), and cuMemcpyDtoH() unnecessary for devices supporting unified addressing). For multidimensional copies, the memory type CU_MEMORYTYPE_UNIFIED may be used to specify that the CUDA driver should infer the location of the pointer from its value.
Automatic Mapping of Host Allocated Host Memory
All host memory allocated in all contexts using cuMemAllocHost() and cuMemHostAlloc() is always directly accessible from all contexts on all devices that support unified addressing. This is the case regardless of whether or not the flags CU_MEMHOSTALLOC_PORTABLE and CU_MEMHOSTALLOC_DEVICEMAP are specified. The pointer value through which allocated host memory may be accessed in kernels on all devices that support unified addressing is the same as the pointer value through which that memory is accessed on the host, so it is not necessary to call cuMemHostGetDevicePointer() to get the device pointer for these allocations. Note that this is not the case for memory allocated using the flag CU_MEMHOSTALLOC_WRITECOMBINED, as discussed below.
Automatic Registration of Peer Memory
Upon enabling direct access from a context that supports unified addressing to another peer context that supports unified addressing using cuCtxEnablePeerAccess() all memory allocated in the peer context using cuMemAlloc() and cuMemAllocPitch() will immediately be accessible by the current context. The device pointer value through which any peer memory may be accessed in the current context is the same pointer value through which that memory may be accessed in the peer context.
Exceptions, Disjoint Addressing
Not all memory may be accessed on devices through the same pointer value through which they are accessed on the host. These exceptions are host memory registered using cuMemHostRegister() and host memory allocated using the flag CU_MEMHOSTALLOC_WRITECOMBINED. For these exceptions, there exists a distinct host and device address for the memory. The device address is guaranteed to not overlap any valid host pointer range and is guaranteed to have the same value across all contexts that support unified addressing. This device address may be queried using cuMemHostGetDevicePointer() when a context using unified addressing is current. Either the host or the unified device pointer value may be used to refer to this memory through cuMemcpy() and similar functions using the CU_MEMORYTYPE_UNIFIED memory type.
- cuda.cuda.cuPointerGetAttribute(attribute: CUpointer_attribute, ptr)¶
Returns information about a pointer.
The supported attributes are:
CU_POINTER_ATTRIBUTE_CONTEXT: Returns in *data the CUcontext in
which ptr was allocated or registered. The type of data must be CUcontext . If `ptr` was not allocated by, mapped by, or registered with a CUcontext which uses unified virtual addressing then CUDA_ERROR_INVALID_VALUE is returned. - CU_POINTER_ATTRIBUTE_MEMORY_TYPE: Returns in `*data` the physical memory type of the memory that `ptr` addresses as a CUmemorytype enumerated value. The type of `data` must be unsigned int. If `ptr` addresses device memory then `*data` is set to CU_MEMORYTYPE_DEVICE. The particular CUdevice on which the memory resides is the CUdevice of the CUcontext returned by the CU_POINTER_ATTRIBUTE_CONTEXT attribute of `ptr`. If `ptr` addresses host memory then `*data` is set to CU_MEMORYTYPE_HOST. If `ptr` was not allocated by, mapped by, or registered with a CUcontext which uses unified virtual addressing then CUDA_ERROR_INVALID_VALUE is returned. If the current CUcontext does not support unified virtual addressing then CUDA_ERROR_INVALID_CONTEXT is returned. - CU_POINTER_ATTRIBUTE_DEVICE_POINTER: Returns in `*data` the device pointer value through which `ptr` may be accessed by kernels running in the current CUcontext. The type of `data` must be CUdeviceptr *. If there exists no device pointer value through which kernels running in the current CUcontext may access `ptr` then CUDA_ERROR_INVALID_VALUE is returned. If there is no current CUcontext then CUDA_ERROR_INVALID_CONTEXT is returned. Except in the exceptional disjoint addressing cases discussed below, the value returned in `*data` will equal the input value `ptr`. - CU_POINTER_ATTRIBUTE_HOST_POINTER: Returns in `*data` the host pointer value through which `ptr` may be accessed by by the host program. The type of `data` must be void *. If there exists no host pointer value through which the host program may directly access ptr then CUDA_ERROR_INVALID_VALUE is returned. Except in the exceptional disjoint addressing cases discussed below, the value returned in *data will equal the input value ptr. - CU_POINTER_ATTRIBUTE_P2P_TOKENS: Returns in *data two tokens for use with the nv-p2p.h Linux kernel interface. data must be a struct of type CUDA_POINTER_ATTRIBUTE_P2P_TOKENS. ptr must be a pointer to memory obtained from :cuMemAlloc(). Note that p2pToken and vaSpaceToken are only valid for the lifetime of the source allocation. A subsequent allocation at the same address may return completely different tokens. Querying this attribute has a side effect of setting the attribute CU_POINTER_ATTRIBUTE_SYNC_MEMOPS for the region of memory that ptr points to. - CU_POINTER_ATTRIBUTE_SYNC_MEMOPS: A boolean attribute which when set, ensures that synchronous memory operations initiated on the region of memory that ptr points to will always synchronize. See further documentation in the section titled “API synchronization behavior” to learn more about cases when synchronous memory operations can exhibit asynchronous behavior. - CU_POINTER_ATTRIBUTE_BUFFER_ID: Returns in *data a buffer ID which is guaranteed to be unique within the process. data must point to an unsigned long long. ptr must be a pointer to memory obtained from a CUDA memory allocation API. Every memory allocation from any of the CUDA memory allocation APIs will have a unique ID over a process lifetime. Subsequent allocations do not reuse IDs from previous freed allocations. IDs are only unique within a single process. - CU_POINTER_ATTRIBUTE_IS_MANAGED: Returns in *data a boolean that indicates whether the pointer points to managed memory or not. If ptr is not a valid CUDA pointer then CUDA_ERROR_INVALID_VALUE is returned. - CU_POINTER_ATTRIBUTE_DEVICE_ORDINAL: Returns in *data an integer representing a device ordinal of a device against which the memory was allocated or registered. - CU_POINTER_ATTRIBUTE_IS_LEGACY_CUDA_IPC_CAPABLE: Returns in *data a boolean that indicates if this pointer maps to an allocation that is suitable for cudaIpcGetMemHandle. - CU_POINTER_ATTRIBUTE_RANGE_START_ADDR: Returns in *data the starting address for the allocation referenced by the device pointer ptr. Note that this is not necessarily the address of the mapped region, but the address of the mappable address range ptr references (e.g. from cuMemAddressReserve). - CU_POINTER_ATTRIBUTE_RANGE_SIZE: Returns in *data the size for the allocation referenced by the device pointer ptr. Note that this is not necessarily the size of the mapped region, but the size of the mappable address range ptr references (e.g. from cuMemAddressReserve). To retrieve the size of the mapped region, see cuMemGetAddressRange - CU_POINTER_ATTRIBUTE_MAPPED: Returns in *data a boolean that indicates if this pointer is in a valid address range that is mapped to a backing allocation. - CU_POINTER_ATTRIBUTE_ALLOWED_HANDLE_TYPES: Returns a bitmask of the allowed handle types for an allocation that may be passed to cuMemExportToShareableHandle. - CU_POINTER_ATTRIBUTE_MEMPOOL_HANDLE: Returns in *data the handle to the mempool that the allocation was obtained from.
- Parameters
- attributeCUpointer_attribute
Pointer attribute to query
- ptrAny
Pointer
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_DEVICE
- dataAny
Returned pointer attribute value
See also
cuPointerSetAttribute
cuMemAlloc
cuMemFree
cuMemAllocHost
cuMemFreeHost
cuMemHostAlloc
cuMemHostRegister
cuMemHostUnregister
cudaPointerGetAttributes
- cuda.cuda.cuMemPrefetchAsync(devPtr, size_t count, dstDevice, hStream)¶
Prefetches memory to the specified destination device.
Prefetches memory to the specified destination device. devPtr is the base device pointer of the memory to be prefetched and dstDevice is the destination device. count specifies the number of bytes to copy. hStream is the stream in which the operation is enqueued. The memory range must refer to managed memory allocated via cuMemAllocManaged or declared via managed variables.
Passing in CU_DEVICE_CPU for dstDevice will prefetch the data to host memory. If dstDevice is a GPU, then the device attribute CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS must be non-zero. Additionally, hStream must be associated with a device that has a non-zero value for the device attribute CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS.
The start address and end address of the memory range will be rounded down and rounded up respectively to be aligned to CPU page size before the prefetch operation is enqueued in the stream.
If no physical memory has been allocated for this region, then this memory region will be populated and mapped on the destination device. If there’s insufficient memory to prefetch the desired region, the Unified Memory driver may evict pages from other cuMemAllocManaged allocations to host memory in order to make room. Device memory allocated using cuMemAlloc or cuArrayCreate will not be evicted.
By default, any mappings to the previous location of the migrated pages are removed and mappings for the new location are only setup on dstDevice. The exact behavior however also depends on the settings applied to this memory range via cuMemAdvise as described below:
If CU_MEM_ADVISE_SET_READ_MOSTLY was set on any subset of this memory range, then that subset will create a read-only copy of the pages on dstDevice.
If CU_MEM_ADVISE_SET_PREFERRED_LOCATION was called on any subset of this memory range, then the pages will be migrated to dstDevice even if dstDevice is not the preferred location of any pages in the memory range.
If CU_MEM_ADVISE_SET_ACCESSED_BY was called on any subset of this memory range, then mappings to those pages from all the appropriate processors are updated to refer to the new location if establishing such a mapping is possible. Otherwise, those mappings are cleared.
Note that this API is not required for functionality and only serves to improve performance by allowing the application to migrate data to a suitable location before it is accessed. Memory accesses to this range are always coherent and are allowed even when the data is actively being migrated.
Note that this function is asynchronous with respect to the host and all work on other devices.
- Parameters
- devPtrAny
Pointer to be prefetched
- countsize_t
Size in bytes
- dstDeviceAny
Destination device to prefetch to
- hStreamCUstream or cudaStream_t
Stream to enqueue prefetch operation
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_DEVICE
- None
None
See also
cuMemcpy
cuMemcpyPeer
cuMemcpyAsync
cuMemcpy3DPeerAsync
cuMemAdvise
cudaMemPrefetchAsync
- cuda.cuda.cuMemAdvise(devPtr, size_t count, advice: CUmem_advise, device)¶
Advise about the usage of a given memory range.
Advise the Unified Memory subsystem about the usage pattern for the memory range starting at devPtr with a size of count bytes. The start address and end address of the memory range will be rounded down and rounded up respectively to be aligned to CPU page size before the advice is applied. The memory range must refer to managed memory allocated via cuMemAllocManaged or declared via managed variables. The memory range could also refer to system-allocated pageable memory provided it represents a valid, host-accessible region of memory and all additional constraints imposed by advice as outlined below are also satisfied. Specifying an invalid system-allocated pageable memory range results in an error being returned.
The advice parameter can take the following values: - CU_MEM_ADVISE_SET_READ_MOSTLY: This implies that the data is mostly going to be read from and only occasionally written to. Any read accesses from any processor to this region will create a read-only copy of at least the accessed pages in that processor’s memory. Additionally, if cuMemPrefetchAsync is called on this region, it will create a read-only copy of the data on the destination processor. If any processor writes to this region, all copies of the corresponding page will be invalidated except for the one where the write occurred. The device argument is ignored for this advice. Note that for a page to be read-duplicated, the accessing processor must either be the CPU or a GPU that has a non-zero value for the device attribute CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS. Also, if a context is created on a device that does not have the device attribute CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS set, then read- duplication will not occur until all such contexts are destroyed. If the memory region refers to valid system-allocated pageable memory, then the accessing device must have a non-zero value for the device attribute CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS for a read-only copy to be created on that device. Note however that if the accessing device also has a non-zero value for the device attribute CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES, then setting this advice will not create a read-only copy when that device accesses this memory region. - CU_MEM_ADVISE_UNSET_READ_MOSTLY: Undoes the effect of CU_MEM_ADVISE_SET_READ_MOSTLY and also prevents the Unified Memory driver from attempting heuristic read-duplication on the memory range. Any read-duplicated copies of the data will be collapsed into a single copy. The location for the collapsed copy will be the preferred location if the page has a preferred location and one of the read-duplicated copies was resident at that location. Otherwise, the location chosen is arbitrary. - CU_MEM_ADVISE_SET_PREFERRED_LOCATION: This advice sets the preferred location for the data to be the memory belonging to device. Passing in CU_DEVICE_CPU for device sets the preferred location as host memory. If device is a GPU, then it must have a non-zero value for the device attribute CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS. Setting the preferred location does not cause data to migrate to that location immediately. Instead, it guides the migration policy when a fault occurs on that memory region. If the data is already in its preferred location and the faulting processor can establish a mapping without requiring the data to be migrated, then data migration will be avoided. On the other hand, if the data is not in its preferred location or if a direct mapping cannot be established, then it will be migrated to the processor accessing it. It is important to note that setting the preferred location does not prevent data prefetching done using cuMemPrefetchAsync. Having a preferred location can override the page thrash detection and resolution logic in the Unified Memory driver. Normally, if a page is detected to be constantly thrashing between for example host and device memory, the page may eventually be pinned to host memory by the Unified Memory driver. But if the preferred location is set as device memory, then the page will continue to thrash indefinitely. If CU_MEM_ADVISE_SET_READ_MOSTLY is also set on this memory region or any subset of it, then the policies associated with that advice will override the policies of this advice, unless read accesses from device will not result in a read-only copy being created on that device as outlined in description for the advice CU_MEM_ADVISE_SET_READ_MOSTLY. If the memory region refers to valid system-allocated pageable memory, then device must have a non-zero value for the device attribute CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS. Additionally, if device has a non-zero value for the device attribute CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES, then this call has no effect. Note however that this behavior may change in the future. - CU_MEM_ADVISE_UNSET_PREFERRED_LOCATION: Undoes the effect of CU_MEM_ADVISE_SET_PREFERRED_LOCATION and changes the preferred location to none. - CU_MEM_ADVISE_SET_ACCESSED_BY: This advice implies that the data will be accessed by device. Passing in CU_DEVICE_CPU for device will set the advice for the CPU. If device is a GPU, then the device attribute CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS must be non-zero. This advice does not cause data migration and has no impact on the location of the data per se. Instead, it causes the data to always be mapped in the specified processor’s page tables, as long as the location of the data permits a mapping to be established. If the data gets migrated for any reason, the mappings are updated accordingly. This advice is recommended in scenarios where data locality is not important, but avoiding faults is. Consider for example a system containing multiple GPUs with peer-to-peer access enabled, where the data located on one GPU is occasionally accessed by peer GPUs. In such scenarios, migrating data over to the other GPUs is not as important because the accesses are infrequent and the overhead of migration may be too high. But preventing faults can still help improve performance, and so having a mapping set up in advance is useful. Note that on CPU access of this data, the data may be migrated to host memory because the CPU typically cannot access device memory directly. Any GPU that had the CU_MEM_ADVISE_SET_ACCESSED_BY flag set for this data will now have its mapping updated to point to the page in host memory. If CU_MEM_ADVISE_SET_READ_MOSTLY is also set on this memory region or any subset of it, then the policies associated with that advice will override the policies of this advice. Additionally, if the preferred location of this memory region or any subset of it is also device, then the policies associated with CU_MEM_ADVISE_SET_PREFERRED_LOCATION will override the policies of this advice. If the memory region refers to valid system-allocated pageable memory, then device must have a non-zero value for the device attribute CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS. Additionally, if device has a non-zero value for the device attribute CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES, then this call has no effect. - CU_MEM_ADVISE_UNSET_ACCESSED_BY: Undoes the effect of CU_MEM_ADVISE_SET_ACCESSED_BY. Any mappings to the data from device may be removed at any time causing accesses to result in non-fatal page faults. If the memory region refers to valid system- allocated pageable memory, then device must have a non-zero value for the device attribute CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS. Additionally, if device has a non-zero value for the device attribute CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES, then this call has no effect.
- Parameters
- devPtrAny
Pointer to memory to set the advice for
- countsize_t
Size in bytes of the memory range
- adviceCUmem_advise
Advice to be applied for the specified memory range
- deviceAny
Device to apply the advice for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_DEVICE
- None
None
See also
cuMemcpy
cuMemcpyPeer
cuMemcpyAsync
cuMemcpy3DPeerAsync
cuMemPrefetchAsync
cudaMemAdvise
- cuda.cuda.cuMemRangeGetAttribute(size_t dataSize, attribute: CUmem_range_attribute, devPtr, size_t count)¶
Query an attribute of a given memory range.
Query an attribute about the memory range starting at devPtr with a size of count bytes. The memory range must refer to managed memory allocated via cuMemAllocManaged or declared via managed variables.
The attribute parameter can take the following values: - CU_MEM_RANGE_ATTRIBUTE_READ_MOSTLY: If this attribute is specified, data will be interpreted as a 32-bit integer, and dataSize must be 4. The result returned will be 1 if all pages in the given memory range have read-duplication enabled, or 0 otherwise. - CU_MEM_RANGE_ATTRIBUTE_PREFERRED_LOCATION: If this attribute is specified, data will be interpreted as a 32-bit integer, and dataSize must be 4. The result returned will be a GPU device id if all pages in the memory range have that GPU as their preferred location, or it will be CU_DEVICE_CPU if all pages in the memory range have the CPU as their preferred location, or it will be CU_DEVICE_INVALID if either all the pages don’t have the same preferred location or some of the pages don’t have a preferred location at all. Note that the actual location of the pages in the memory range at the time of the query may be different from the preferred location. - CU_MEM_RANGE_ATTRIBUTE_ACCESSED_BY: If this attribute is specified, data will be interpreted as an array of 32-bit integers, and dataSize must be a non-zero multiple of 4. The result returned will be a list of device ids that had CU_MEM_ADVISE_SET_ACCESSED_BY set for that entire memory range. If any device does not have that advice set for the entire memory range, that device will not be included. If data is larger than the number of devices that have that advice set for that memory range, CU_DEVICE_INVALID will be returned in all the extra space provided. For ex., if dataSize is 12 (i.e. data has 3 elements) and only device 0 has the advice set, then the result returned will be { 0, CU_DEVICE_INVALID, CU_DEVICE_INVALID }. If data is smaller than the number of devices that have that advice set, then only as many devices will be returned as can fit in the array. There is no guarantee on which specific devices will be returned, however. - CU_MEM_RANGE_ATTRIBUTE_LAST_PREFETCH_LOCATION: If this attribute is specified, data will be interpreted as a 32-bit integer, and dataSize must be 4. The result returned will be the last location to which all pages in the memory range were prefetched explicitly via cuMemPrefetchAsync. This will either be a GPU id or CU_DEVICE_CPU depending on whether the last location for prefetch was a GPU or the CPU respectively. If any page in the memory range was never explicitly prefetched or if all pages were not prefetched to the same location, CU_DEVICE_INVALID will be returned. Note that this simply returns the last location that the applicaton requested to prefetch the memory range to. It gives no indication as to whether the prefetch operation to that location has completed or even begun.
- Parameters
- dataSizesize_t
Array containing the size of data
- attributeCUmem_range_attribute
The attribute to query
- devPtrAny
Start of the range to query
- countsize_t
Size of the range to query
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_DEVICE
- dataAny
A pointers to a memory location where the result of each attribute query will be written to.
See also
cuMemRangeGetAttributes
cuMemPrefetchAsync
cuMemAdvise
cudaMemRangeGetAttribute
- cuda.cuda.cuMemRangeGetAttributes(dataSizes: List[int], attributes: List[CUmem_range_attribute], size_t numAttributes, devPtr, size_t count)¶
Query attributes of a given memory range.
Query attributes of the memory range starting at devPtr with a size of count bytes. The memory range must refer to managed memory allocated via cuMemAllocManaged or declared via managed variables. The attributes array will be interpreted to have numAttributes entries. The dataSizes array will also be interpreted to have numAttributes entries. The results of the query will be stored in data.
The list of supported attributes are given below. Please refer to cuMemRangeGetAttribute for attribute descriptions and restrictions.
CU_MEM_RANGE_ATTRIBUTE_READ_MOSTLY -
CU_MEM_RANGE_ATTRIBUTE_PREFERRED_LOCATION - CU_MEM_RANGE_ATTRIBUTE_ACCESSED_BY - CU_MEM_RANGE_ATTRIBUTE_LAST_PREFETCH_LOCATION
- Parameters
- dataSizesList[int]
Array containing the sizes of each result
- attributesList[CUmem_range_attribute]
An array of attributes to query (numAttributes and the number of attributes in this array should match)
- numAttributessize_t
Number of attributes to query
- devPtrAny
Start of the range to query
- countsize_t
Size of the range to query
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_DEVICE
- dataList[Any]
A two-dimensional array containing pointers to memory locations where the result of each attribute query will be written to.
See also
cuMemRangeGetAttribute
cuMemAdvise
cuMemPrefetchAsync
cudaMemRangeGetAttributes
- cuda.cuda.cuPointerSetAttribute(value, attribute: CUpointer_attribute, ptr)¶
Set attributes on a previously allocated memory region.
The supported attributes are:
CU_POINTER_ATTRIBUTE_SYNC_MEMOPS: A boolean attribute that can either
be set (1) or unset (0). When set, the region of memory that ptr points to is guaranteed to always synchronize memory operations that are synchronous. If there are some previously initiated synchronous memory operations that are pending when this attribute is set, the function does not return until those memory operations are complete. See further documentation in the section titled “API synchronization behavior” to learn more about cases when synchronous memory operations can exhibit asynchronous behavior. value will be considered as a pointer to an unsigned integer to which this attribute is to be set.
- Parameters
- valueAny
Pointer to memory containing the value to be set
- attributeCUpointer_attribute
Pointer attribute to set
- ptrAny
Pointer to a memory region allocated using CUDA memory allocation APIs
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_DEVICE
- None
None
- cuda.cuda.cuPointerGetAttributes(unsigned int numAttributes, attributes: List[CUpointer_attribute], ptr)¶
Returns information about a pointer.
The supported attributes are (refer to cuPointerGetAttribute for attribute descriptions and restrictions):
CU_POINTER_ATTRIBUTE_CONTEXT - CU_POINTER_ATTRIBUTE_MEMORY_TYPE
CU_POINTER_ATTRIBUTE_DEVICE_POINTER -
CU_POINTER_ATTRIBUTE_HOST_POINTER - CU_POINTER_ATTRIBUTE_SYNC_MEMOPS - CU_POINTER_ATTRIBUTE_BUFFER_ID - CU_POINTER_ATTRIBUTE_IS_MANAGED - CU_POINTER_ATTRIBUTE_DEVICE_ORDINAL - CU_POINTER_ATTRIBUTE_RANGE_START_ADDR - CU_POINTER_ATTRIBUTE_RANGE_SIZE - CU_POINTER_ATTRIBUTE_MAPPED - CU_POINTER_ATTRIBUTE_IS_LEGACY_CUDA_IPC_CAPABLE - CU_POINTER_ATTRIBUTE_ALLOWED_HANDLE_TYPES - CU_POINTER_ATTRIBUTE_MEMPOOL_HANDLE
If ptr was not allocated by, mapped by, or registered with a CUcontext which uses UVA (Unified Virtual Addressing), CUDA_ERROR_INVALID_CONTEXT is returned.
- Parameters
- numAttributesunsigned int
Number of attributes to query
- attributesList[CUpointer_attribute]
An array of attributes to query (numAttributes and the number of attributes in this array should match)
- ptrAny
Pointer to query
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_DEVICE
- dataList[Any]
A two-dimensional array containing pointers to memory locations where the result of each attribute query will be written to.
See also
cuPointerGetAttribute
cuPointerSetAttribute
cudaPointerGetAttributes
Stream Management¶
This section describes the stream management functions of the low-level CUDA driver application programming interface.
- cuda.cuda.cuStreamCreate(unsigned int Flags)¶
Create a stream.
Creates a stream and returns a handle in phStream. The Flags argument determines behaviors of the stream.
Valid values for Flags are: - CU_STREAM_DEFAULT: Default stream creation flag. - CU_STREAM_NON_BLOCKING: Specifies that work running in the created stream may run concurrently with work in stream 0 (the NULL stream), and that the created stream should perform no implicit synchronization with stream 0.
- Parameters
- Flagsunsigned int
Parameters for stream creation
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_OUT_OF_MEMORY
- phStreamCUstream
Returned newly created stream
See also
cuStreamDestroy
cuStreamCreateWithPriority
cuStreamGetPriority
cuStreamGetFlags
cuStreamWaitEvent
cuStreamQuery
cuStreamSynchronize
cuStreamAddCallback
cudaStreamCreate
cudaStreamCreateWithFlags
- cuda.cuda.cuStreamCreateWithPriority(unsigned int flags, int priority)¶
Create a stream with the given priority.
Creates a stream with the specified priority and returns a handle in phStream. This API alters the scheduler priority of work in the stream. Work in a higher priority stream may preempt work already executing in a low priority stream.
priority follows a convention where lower numbers represent higher priorities. ‘0’ represents default priority. The range of meaningful numerical priorities can be queried using cuCtxGetStreamPriorityRange. If the specified priority is outside the numerical range returned by cuCtxGetStreamPriorityRange, it will automatically be clamped to the lowest or the highest number in the range.
- Parameters
- flagsunsigned int
Flags for stream creation. See cuStreamCreate for a list of valid flags
- priorityint
Stream priority. Lower numbers represent higher priorities. See cuCtxGetStreamPriorityRange for more information about meaningful stream priorities that can be passed.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_OUT_OF_MEMORY
- phStreamCUstream
Returned newly created stream
See also
Notes
In the current implementation, only compute kernels launched in priority streams are affected by the stream’s priority. Stream priorities have no effect on host-to-device and device-to-host memory operations.
- cuda.cuda.cuStreamGetPriority(hStream)¶
Query the priority of a given stream.
Query the priority of a stream created using cuStreamCreate or cuStreamCreateWithPriority and return the priority in priority. Note that if the stream was created with a priority outside the numerical range returned by cuCtxGetStreamPriorityRange, this function returns the clamped priority. See cuStreamCreateWithPriority for details about priority clamping.
- Parameters
- hStreamCUstream or cudaStream_t
Handle to the stream to be queried
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_OUT_OF_MEMORY
- priorityint
Pointer to a signed integer in which the stream’s priority is returned
See also
cuStreamDestroy
cuStreamCreate
cuStreamCreateWithPriority
cuCtxGetStreamPriorityRange
cuStreamGetFlags
cudaStreamGetPriority
- cuda.cuda.cuStreamGetFlags(hStream)¶
Query the flags of a given stream.
Query the flags of a stream created using cuStreamCreate or cuStreamCreateWithPriority and return the flags in flags.
- Parameters
- hStreamCUstream or cudaStream_t
Handle to the stream to be queried
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_OUT_OF_MEMORY
- flagsunsigned int
Pointer to an unsigned integer in which the stream’s flags are returned The value returned in flags is a logical ‘OR’ of all flags that were used while creating this stream. See cuStreamCreate for the list of valid flags
See also
cuStreamDestroy
cuStreamCreate
cuStreamGetPriority
cudaStreamGetFlags
- cuda.cuda.cuStreamGetCtx(hStream)¶
Query the context associated with a stream.
Returns the CUDA context that the stream is associated with.
The stream handle hStream can refer to any of the following: - a stream created via any of the CUDA driver APIs such as cuStreamCreate and cuStreamCreateWithPriority, or their runtime API equivalents such as cudaStreamCreate, cudaStreamCreateWithFlags and cudaStreamCreateWithPriority. The returned context is the context that was active in the calling thread when the stream was created. Passing an invalid handle will result in undefined behavior. - any of the special streams such as the NULL stream, CU_STREAM_LEGACY and CU_STREAM_PER_THREAD. The runtime API equivalents of these are also accepted, which are NULL, cudaStreamLegacy and cudaStreamPerThread respectively. Specifying any of the special handles will return the context current to the calling thread. If no context is current to the calling thread, CUDA_ERROR_INVALID_CONTEXT is returned.
- Parameters
- hStreamCUstream or cudaStream_t
Handle to the stream to be queried
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_HANDLE
- pctxCUcontext
Returned context associated with the stream
See also
cuStreamDestroy
cuStreamCreateWithPriority
cuStreamGetPriority
cuStreamGetFlags
cuStreamWaitEvent
cuStreamQuery
cuStreamSynchronize
cuStreamAddCallback
cudaStreamCreate
cudaStreamCreateWithFlags
- cuda.cuda.cuStreamWaitEvent(hStream, hEvent, unsigned int Flags)¶
Make a compute stream wait on an event.
Makes all future work submitted to hStream wait for all work captured in hEvent. See cuEventRecord() for details on what is captured by an event. The synchronization will be performed efficiently on the device when applicable. hEvent may be from a different context or device than hStream.
flags include: - CU_EVENT_WAIT_DEFAULT: Default event creation flag. - CU_EVENT_WAIT_EXTERNAL: Event is captured in the graph as an external event node when performing stream capture. This flag is invalid outside of stream capture.
- Parameters
- hStreamCUstream or cudaStream_t
Stream to wait
- hEventAny
Event to wait on (may not be NULL)
- Flagsunsigned int
See CUevent_capture_flags
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_HANDLE
- None
None
See also
cuStreamCreate
cuEventRecord
cuStreamQuery
cuStreamSynchronize
cuStreamAddCallback
cuStreamDestroy
cudaStreamWaitEvent
- cuda.cuda.cuStreamAddCallback(hStream, callback, userData, unsigned int flags)¶
Add a callback to a compute stream.
The callback may be passed CUDA_SUCCESS or an error code. In the event of a device error, all subsequently executed callbacks will receive an appropriate CUresult.
Callbacks must not make any CUDA API calls. Attempting to use a CUDA API will result in CUDA_ERROR_NOT_PERMITTED. Callbacks must not perform any synchronization that may depend on outstanding device work or other callbacks that are not mandated to run earlier. Callbacks without a mandated order (in independent streams) execute in undefined order and may be serialized.
For the purposes of Unified Memory, callback execution makes a number of guarantees: - The callback stream is considered idle for the duration of the callback. Thus, for example, a callback may always use memory attached to the callback stream. - The start of execution of a callback has the same effect as synchronizing an event recorded in the same stream immediately prior to the callback. It thus synchronizes streams which have been “joined” prior to the callback. - Adding device work to any stream does not have the effect of making the stream active until all preceding host functions and stream callbacks have executed. Thus, for example, a callback might use global attached memory even if work has been added to another stream, if the work has been ordered behind the callback with an event. - Completion of a callback does not cause a stream to become active except as described above. The callback stream will remain idle if no device work follows the callback, and will remain idle across consecutive callbacks without device work in between. Thus, for example, stream synchronization can be done by signaling from a callback at the end of the stream.
- Parameters
- hStreamCUstream or cudaStream_t
Stream to add callback to
- callbackAny
The function to call once preceding stream operations are complete
- userDataAny
User specified data to be passed to the callback function
- flagsunsigned int
Reserved for future use, must be 0
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_NOT_SUPPORTED
- None
None
See also
cuStreamCreate
cuStreamQuery
cuStreamSynchronize
cuStreamWaitEvent
cuStreamDestroy
cuMemAllocManaged
cuStreamAttachMemAsync
cuStreamLaunchHostFunc
cudaStreamAddCallback
Notes
This function is slated for eventual deprecation and removal. If you do not require the callback to execute in case of a device error, consider using cuLaunchHostFunc. Additionally, this function is not supported with cuStreamBeginCapture and cuStreamEndCapture, unlike cuLaunchHostFunc.
- cuda.cuda.cuStreamBeginCapture(hStream, mode: CUstreamCaptureMode)¶
Begins graph capture on a stream.
Begin graph capture on hStream. When a stream is in capture mode, all operations pushed into the stream will not be executed, but will instead be captured into a graph, which will be returned via cuStreamEndCapture. Capture may not be initiated if stream is CU_STREAM_LEGACY. Capture must be ended on the same stream in which it was initiated, and it may only be initiated if the stream is not already in capture mode. The capture mode may be queried via cuStreamIsCapturing. A unique id representing the capture sequence may be queried via cuStreamGetCaptureInfo.
If mode is not CU_STREAM_CAPTURE_MODE_RELAXED, cuStreamEndCapture must be called on this stream from the same thread.
- Parameters
- hStreamCUstream or cudaStream_t
Stream in which to initiate capture
- modeCUstreamCaptureMode
Controls the interaction of this capture sequence with other API calls that are potentially unsafe. For more details see cuThreadExchangeStreamCaptureMode.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- None
None
Notes
Kernels captured using this API must not use texture and surface references. Reading or writing through any texture or surface reference is undefined behavior. This restriction does not apply to texture and surface objects.
- cuda.cuda.cuThreadExchangeStreamCaptureMode(mode: CUstreamCaptureMode)¶
Swaps the stream capture interaction mode for a thread.
Sets the calling thread’s stream capture interaction mode to the value contained in *mode, and overwrites *mode with the previous mode for the thread. To facilitate deterministic behavior across function or module boundaries, callers are encouraged to use this API in a push-pop fashion:CUstreamCaptureModemode=desiredMode; cuThreadExchangeStreamCaptureMode(&mode); … cuThreadExchangeStreamCaptureMode(&mode);//restorepreviousmode
During stream capture (see cuStreamBeginCapture), some actions, such as a call to cudaMalloc, may be unsafe. In the case of cudaMalloc, the operation is not enqueued asynchronously to a stream, and is not observed by stream capture. Therefore, if the sequence of operations captured via cuStreamBeginCapture depended on the allocation being replayed whenever the graph is launched, the captured graph would be invalid.
Therefore, stream capture places restrictions on API calls that can be made within or concurrently to a cuStreamBeginCapture- cuStreamEndCapture sequence. This behavior can be controlled via this API and flags to cuStreamBeginCapture.
A thread’s mode is one of the following: - CU_STREAM_CAPTURE_MODE_GLOBAL: This is the default mode. If the local thread has an ongoing capture sequence that was not initiated with CU_STREAM_CAPTURE_MODE_RELAXED at cuStreamBeginCapture, or if any other thread has a concurrent capture sequence initiated with CU_STREAM_CAPTURE_MODE_GLOBAL, this thread is prohibited from potentially unsafe API calls. - CU_STREAM_CAPTURE_MODE_THREAD_LOCAL: If the local thread has an ongoing capture sequence not initiated with CU_STREAM_CAPTURE_MODE_RELAXED, it is prohibited from potentially unsafe API calls. Concurrent capture sequences in other threads are ignored. - CU_STREAM_CAPTURE_MODE_RELAXED: The local thread is not prohibited from potentially unsafe API calls. Note that the thread is still prohibited from API calls which necessarily conflict with stream capture, for example, attempting cuEventQuery on an event that was last recorded inside a capture sequence.
- Parameters
- modeCUstreamCaptureMode
Pointer to mode value to swap with the current mode
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- None
None
See also
- cuda.cuda.cuStreamEndCapture(hStream)¶
Ends capture on a stream, returning the captured graph.
End capture on hStream, returning the captured graph via phGraph. Capture must have been initiated on hStream via a call to cuStreamBeginCapture. If capture was invalidated, due to a violation of the rules of stream capture, then a NULL graph will be returned.
If the mode argument to cuStreamBeginCapture was not CU_STREAM_CAPTURE_MODE_RELAXED, this call must be from the same thread as cuStreamBeginCapture.
- Parameters
- hStreamCUstream or cudaStream_t
Stream to query
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD
- phGraphCUgraph
The captured graph
- cuda.cuda.cuStreamIsCapturing(hStream)¶
Returns a stream’s capture status.
Return the capture status of hStream via captureStatus. After a successful call, *captureStatus will contain one of the following: - CU_STREAM_CAPTURE_STATUS_NONE: The stream is not capturing. - CU_STREAM_CAPTURE_STATUS_ACTIVE: The stream is capturing. - CU_STREAM_CAPTURE_STATUS_INVALIDATED: The stream was capturing but an error has invalidated the capture sequence. The capture sequence must be terminated with cuStreamEndCapture on the stream where it was initiated in order to continue using hStream.
Note that, if this is called on CU_STREAM_LEGACY (the “null stream”) while a blocking stream in the same context is capturing, it will return CUDA_ERROR_STREAM_CAPTURE_IMPLICIT and *captureStatus is unspecified after the call. The blocking stream capture is not invalidated.
When a blocking stream is capturing, the legacy stream is in an unusable state until the blocking stream capture is terminated. The legacy stream is not supported for stream capture, but attempted use would have an implicit dependency on the capturing stream(s).
- Parameters
- hStreamCUstream or cudaStream_t
Stream to query
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE CUDA_ERROR_STREAM_CAPTURE_IMPLICIT
- captureStatusCUstreamCaptureStatus
Returns the stream’s capture status
- cuda.cuda.cuStreamGetCaptureInfo(hStream)¶
Query capture status of a stream.
Note there is a later version of this API, cuStreamGetCaptureInfo_v2. It will supplant this version in 12.0, which is retained for minor version compatibility.
Query the capture status of a stream and and get an id for the capture sequence, which is unique over the lifetime of the process.
If called on CU_STREAM_LEGACY (the “null stream”) while a stream not created with CU_STREAM_NON_BLOCKING is capturing, returns CUDA_ERROR_STREAM_CAPTURE_IMPLICIT.
A valid id is returned only if both of the following are true: - the call returns CUDA_SUCCESS - captureStatus is set to CU_STREAM_CAPTURE_STATUS_ACTIVE
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_STREAM_CAPTURE_IMPLICIT
- None
None
- cuda.cuda.cuStreamGetCaptureInfo_v2(hStream) Query a stream's capture state (11.3+)¶
Query a stream’s capture state (11.3+)
Query stream state related to stream capture.
If called on CU_STREAM_LEGACY (the “null stream”) while a stream not created with CU_STREAM_NON_BLOCKING is capturing, returns CUDA_ERROR_STREAM_CAPTURE_IMPLICIT.
Valid data (other than capture status) is returned only if both of the following are true: - the call returns CUDA_SUCCESS - the returned capture status is CU_STREAM_CAPTURE_STATUS_ACTIVE
This version of cuStreamGetCaptureInfo is introduced in CUDA 11.3 and will supplant the previous version in 12.0. Developers requiring compatibility across minor versions to CUDA 11.0 (driver version 445) should use cuStreamGetCaptureInfo or include a fallback path.
- Parameters
- hStreamCUstream or cudaStream_t
The stream to query
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_STREAM_CAPTURE_IMPLICIT
- captureStatus_outCUstreamCaptureStatus
Location to return the capture status of the stream; required
- id_outcuuint64_t
Optional location to return an id for the capture sequence, which is unique over the lifetime of the process
- graph_outCUgraph
Optional location to return the graph being captured into. All operations other than destroy and node removal are permitted on the graph while the capture sequence is in progress. This API does not transfer ownership of the graph, which is transferred or destroyed at cuStreamEndCapture. Note that the graph handle may be invalidated before end of capture for certain errors. Nodes that are or become unreachable from the original stream at cuStreamEndCapture due to direct actions on the graph do not trigger CUDA_ERROR_STREAM_CAPTURE_UNJOINED.
- dependencies_outList[CUgraphNode]
Optional location to store a pointer to an array of nodes. The next node to be captured in the stream will depend on this set of nodes, absent operations such as event wait which modify this set. The array pointer is valid until the next API call which operates on the stream or until end of capture. The node handles may be copied out and are valid until they or the graph is destroyed. The driver- owned array may also be passed directly to APIs that operate on the graph (not the stream) without copying.
- numDependencies_outint
Optional location to store the size of the array returned in dependencies_out.
- cuda.cuda.cuStreamUpdateCaptureDependencies(hStream, dependencies: List[CUgraphNode], size_t numDependencies, unsigned int flags) Update the set of dependencies in a capturing stream (11.3+)¶
Update the set of dependencies in a capturing stream (11.3+)
Modifies the dependency set of a capturing stream. The dependency set is the set of nodes that the next captured node in the stream will depend on.
Valid flags are CU_STREAM_ADD_CAPTURE_DEPENDENCIES and CU_STREAM_SET_CAPTURE_DEPENDENCIES. These control whether the set passed to the API is added to the existing set or replaces it. A flags value of 0 defaults to CU_STREAM_ADD_CAPTURE_DEPENDENCIES.
Nodes that are removed from the dependency set via this API do not result in CUDA_ERROR_STREAM_CAPTURE_UNJOINED if they are unreachable from the stream at cuStreamEndCapture.
Returns CUDA_ERROR_ILLEGAL_STATE if the stream is not capturing.
This API is new in CUDA 11.3. Developers requiring compatibility across minor versions to CUDA 11.0 should not use this API or provide a fallback.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_ILLEGAL_STATE
- None
None
- cuda.cuda.cuStreamAttachMemAsync(hStream, dptr, size_t length, unsigned int flags)¶
Attach memory to a stream asynchronously.
Enqueues an operation in hStream to specify stream association of length bytes of memory starting from dptr. This function is a stream-ordered operation, meaning that it is dependent on, and will only take effect when, previous work in stream has completed. Any previous association is automatically replaced.
dptr must point to one of the following types of memories: - managed memory declared using the managed keyword or allocated with cuMemAllocManaged. - a valid host-accessible region of system- allocated pageable memory. This type of memory may only be specified if the device associated with the stream reports a non-zero value for the device attribute CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS.
For managed allocations, length must be either zero or the entire allocation’s size. Both indicate that the entire allocation’s stream association is being changed. Currently, it is not possible to change stream association for a portion of a managed allocation.
For pageable host allocations, length must be non-zero.
The stream association is specified using flags which must be one of CUmemAttach_flags. If the CU_MEM_ATTACH_GLOBAL flag is specified, the memory can be accessed by any stream on any device. If the CU_MEM_ATTACH_HOST flag is specified, the program makes a guarantee that it won’t access the memory on the device from any stream on a device that has a zero value for the device attribute CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS. If the CU_MEM_ATTACH_SINGLE flag is specified and hStream is associated with a device that has a zero value for the device attribute CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS, the program makes a guarantee that it will only access the memory on the device from hStream. It is illegal to attach singly to the NULL stream, because the NULL stream is a virtual global stream and not a specific stream. An error will be returned in this case.
When memory is associated with a single stream, the Unified Memory system will allow CPU access to this memory region so long as all operations in hStream have completed, regardless of whether other streams are active. In effect, this constrains exclusive ownership of the managed memory region by an active GPU to per-stream activity instead of whole-GPU activity.
Accessing memory on the device from streams that are not associated with it will produce undefined results. No error checking is performed by the Unified Memory system to ensure that kernels launched into other streams do not access this region.
It is a program’s responsibility to order calls to cuStreamAttachMemAsync via events, synchronization or other means to ensure legal access to memory at all times. Data visibility and coherency will be changed appropriately for all kernels which follow a stream-association change.
If hStream is destroyed while data is associated with it, the association is removed and the association reverts to the default visibility of the allocation as specified at cuMemAllocManaged. For managed variables, the default association is always CU_MEM_ATTACH_GLOBAL. Note that destroying a stream is an asynchronous operation, and as a result, the change to default association won’t happen until all work in the stream has completed.
- Parameters
- hStreamCUstream or cudaStream_t
Stream in which to enqueue the attach operation
- dptrAny
Pointer to memory (must be a pointer to managed memory or to a valid host-accessible region of system-allocated pageable memory)
- lengthsize_t
Length of memory
- flagsunsigned int
Must be one of CUmemAttach_flags
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_NOT_SUPPORTED
- None
None
See also
cuStreamCreate
cuStreamQuery
cuStreamSynchronize
cuStreamWaitEvent
cuStreamDestroy
cuMemAllocManaged
cudaStreamAttachMemAsync
- cuda.cuda.cuStreamQuery(hStream)¶
Determine status of a compute stream.
Returns CUDA_SUCCESS if all operations in the stream specified by hStream have completed, or CUDA_ERROR_NOT_READY if not.
For the purposes of Unified Memory, a return value of CUDA_SUCCESS is equivalent to having called cuStreamSynchronize().
- Parameters
- hStreamCUstream or cudaStream_t
Stream to query status of
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_NOT_READY
- None
None
See also
- cuda.cuda.cuStreamSynchronize(hStream)¶
Wait until a stream’s tasks are completed.
Waits until the device has completed all operations in the stream specified by hStream. If the context was created with the CU_CTX_SCHED_BLOCKING_SYNC flag, the CPU thread will block until the stream is finished with all of its tasks.
- Parameters
- hStreamCUstream or cudaStream_t
Stream to wait for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_HANDLE
- None
None
See also
cuStreamCreate
cuStreamDestroy
cuStreamWaitEvent
cuStreamQuery
cuStreamAddCallback
cudaStreamSynchronize
- cuda.cuda.cuStreamDestroy(hStream)¶
Destroys a stream.
Destroys the stream specified by hStream.
In case the device is still doing work in the stream hStream when cuStreamDestroy() is called, the function will return immediately and the resources associated with hStream will be released automatically once the device has completed all work in hStream.
- Parameters
- hStreamCUstream or cudaStream_t
Stream to destroy
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE
- None
None
See also
cuStreamCreate
cuStreamWaitEvent
cuStreamQuery
cuStreamSynchronize
cuStreamAddCallback
cudaStreamDestroy
- cuda.cuda.cuStreamCopyAttributes(dst, src)¶
Copies attributes from source stream to destination stream.
Copies attributes from source stream src to destination stream dst. Both streams must have the same context.
- Parameters
- dstAny
Destination stream
- srcAny
Source stream For list of attributes see CUstreamAttrID
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- None
None
- cuda.cuda.cuStreamGetAttribute(hStream, attr: CUstreamAttrID)¶
Queries stream attribute.
Queries attribute attr from hStream and stores it in corresponding member of value_out.
- Parameters
- hStreamCUstream or cudaStream_t
- attrCUstreamAttrID
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE
- value_outCUstreamAttrValue
- cuda.cuda.cuStreamSetAttribute(hStream, attr: CUstreamAttrID, CUstreamAttrValue value: CUstreamAttrValue)¶
Sets stream attribute.
Sets attribute attr on hStream from corresponding attribute of value. The updated attribute will be applied to subsequent work submitted to the stream. It will not affect previously submitted work.
- Parameters
- hStreamCUstream or cudaStream_t
- attrCUstreamAttrID
- valueCUstreamAttrValue
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE
- None
None
Event Management¶
This section describes the event management functions of the low-level CUDA driver application programming interface.
- cuda.cuda.cuEventCreate(unsigned int Flags)¶
Creates an event.
Creates an event *phEvent for the current context with the flags specified via Flags. Valid flags include: - CU_EVENT_DEFAULT: Default event creation flag. - CU_EVENT_BLOCKING_SYNC: Specifies that the created event should use blocking synchronization. A CPU thread that uses cuEventSynchronize() to wait on an event created with this flag will block until the event has actually been recorded. - CU_EVENT_DISABLE_TIMING: Specifies that the created event does not need to record timing data. Events created with this flag specified and the CU_EVENT_BLOCKING_SYNC flag not specified will provide the best performance when used with cuStreamWaitEvent() and cuEventQuery(). - CU_EVENT_INTERPROCESS: Specifies that the created event may be used as an interprocess event by cuIpcGetEventHandle(). CU_EVENT_INTERPROCESS must be specified along with CU_EVENT_DISABLE_TIMING.
- Parameters
- Flagsunsigned int
Event creation flags
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_OUT_OF_MEMORY
- phEventCUevent
Returns newly created event
See also
cuEventRecord
cuEventQuery
cuEventSynchronize
cuEventDestroy
cuEventElapsedTime
cudaEventCreate
cudaEventCreateWithFlags
- cuda.cuda.cuEventRecord(hEvent, hStream)¶
Records an event.
Captures in hEvent the contents of hStream at the time of this call. hEvent and hStream must be from the same context. Calls such as cuEventQuery() or cuStreamWaitEvent() will then examine or wait for completion of the work that was captured. Uses of hStream after this call do not modify hEvent. See note on default stream behavior for what is captured in the default case.
cuEventRecord() can be called multiple times on the same event and will overwrite the previously captured state. Other APIs such as cuStreamWaitEvent() use the most recently captured state at the time of the API call, and are not affected by later calls to cuEventRecord(). Before the first call to cuEventRecord(), an event represents an empty set of work, so for example cuEventQuery() would return CUDA_SUCCESS.
- Parameters
- hEventAny
Event to record
- hStreamCUstream or cudaStream_t
Stream to record event for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_INVALID_VALUE
- None
None
See also
- cuda.cuda.cuEventRecordWithFlags(hEvent, hStream, unsigned int flags)¶
Records an event.
Captures in hEvent the contents of hStream at the time of this call. hEvent and hStream must be from the same context. Calls such as cuEventQuery() or cuStreamWaitEvent() will then examine or wait for completion of the work that was captured. Uses of hStream after this call do not modify hEvent. See note on default stream behavior for what is captured in the default case.
cuEventRecordWithFlags() can be called multiple times on the same event and will overwrite the previously captured state. Other APIs such as cuStreamWaitEvent() use the most recently captured state at the time of the API call, and are not affected by later calls to cuEventRecordWithFlags(). Before the first call to cuEventRecordWithFlags(), an event represents an empty set of work, so for example cuEventQuery() would return CUDA_SUCCESS.
flags include: - CU_EVENT_RECORD_DEFAULT: Default event creation flag. - CU_EVENT_RECORD_EXTERNAL: Event is captured in the graph as an external event node when performing stream capture. This flag is invalid outside of stream capture.
- Parameters
- hEventAny
Event to record
- hStreamCUstream or cudaStream_t
Stream to record event for
- flagsunsigned int
See CUevent_capture_flags
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_INVALID_VALUE
- None
None
See also
- cuda.cuda.cuEventQuery(hEvent)¶
Queries an event’s status.
Queries the status of all work currently captured by hEvent. See cuEventRecord() for details on what is captured by an event.
Returns CUDA_SUCCESS if all captured work has been completed, or CUDA_ERROR_NOT_READY if any captured work is incomplete.
For the purposes of Unified Memory, a return value of CUDA_SUCCESS is equivalent to having called cuEventSynchronize().
- Parameters
- hEventAny
Event to query
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_READY
- None
None
See also
cuEventCreate
cuEventRecord
cuEventSynchronize
cuEventDestroy
cuEventElapsedTime
cudaEventQuery
- cuda.cuda.cuEventSynchronize(hEvent)¶
Waits for an event to complete.
Waits until the completion of all work currently captured in hEvent. See cuEventRecord() for details on what is captured by an event.
Waiting for an event that was created with the CU_EVENT_BLOCKING_SYNC flag will cause the calling CPU thread to block until the event has been completed by the device. If the CU_EVENT_BLOCKING_SYNC flag has not been set, then the CPU thread will busy-wait until the event has been completed by the device.
- Parameters
- hEventAny
Event to wait for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_HANDLE
- None
None
See also
cuEventCreate
cuEventRecord
cuEventQuery
cuEventDestroy
cuEventElapsedTime
cudaEventSynchronize
- cuda.cuda.cuEventDestroy(hEvent)¶
Destroys an event.
Destroys the event specified by hEvent.
An event may be destroyed before it is complete (i.e., while cuEventQuery() would return CUDA_ERROR_NOT_READY). In this case, the call does not block on completion of the event, and any associated resources will automatically be released asynchronously at completion.
- Parameters
- hEventAny
Event to destroy
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_HANDLE
- None
None
See also
cuEventCreate
cuEventRecord
cuEventQuery
cuEventSynchronize
cuEventElapsedTime
cudaEventDestroy
- cuda.cuda.cuEventElapsedTime(hStart, hEnd)¶
Computes the elapsed time between two events.
Computes the elapsed time between two events (in milliseconds with a resolution of around 0.5 microseconds).
If either event was last recorded in a non-NULL stream, the resulting time may be greater than expected (even if both used the same stream handle). This happens because the cuEventRecord() operation takes place asynchronously and there is no guarantee that the measured latency is actually just between the two events. Any number of other different stream operations could execute in between the two measured events, thus altering the timing in a significant way.
If cuEventRecord() has not been called on either event then CUDA_ERROR_INVALID_HANDLE is returned. If cuEventRecord() has been called on both events but one or both of them has not yet been completed (that is, cuEventQuery() would return CUDA_ERROR_NOT_READY on at least one of the events), CUDA_ERROR_NOT_READY is returned. If either event was created with the CU_EVENT_DISABLE_TIMING flag, then this function will return CUDA_ERROR_INVALID_HANDLE.
- Parameters
- hStartAny
Starting event
- hEndAny
Ending event
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_NOT_READY
- pMillisecondsfloat
Time between hStart and hEnd in ms
See also
cuEventCreate
cuEventRecord
cuEventQuery
cuEventSynchronize
cuEventDestroy
cudaEventElapsedTime
External Resource Interoperability¶
This section describes the external resource interoperability functions of the low-level CUDA driver application programming interface.
- cuda.cuda.cuImportExternalMemory(CUDA_EXTERNAL_MEMORY_HANDLE_DESC memHandleDesc: CUDA_EXTERNAL_MEMORY_HANDLE_DESC)¶
Imports an external memory object.
Imports an externally allocated memory object and returns a handle to that in extMem_out.
The properties of the handle being imported must be described in memHandleDesc. The CUDA_EXTERNAL_MEMORY_HANDLE_DESC structure is defined as follows:
typedefstructCUDA_EXTERNAL_MEMORY_HANDLE_DESC_st{ CUexternalMemoryHandleTypetype; union{ intfd; struct{ void*handle; constvoid*name; }win32; constvoid*nvSciBufObject; }handle; unsignedlonglongsize; unsignedintflags; }CUDA_EXTERNAL_MEMORY_HANDLE_DESC;
where CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type specifies the type of handle being imported. CUexternalMemoryHandleType is defined as:
typedefenumCUexternalMemoryHandleType_enum{ CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_FD=1, CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32=2, CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32_KMT=3, CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_HEAP=4, CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_RESOURCE=5, CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE=6, CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE_KMT=7, CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF=8 }CUexternalMemoryHandleType;
If CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type is CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_FD, then CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::fd must be a valid file descriptor referencing a memory object. Ownership of the file descriptor is transferred to the CUDA driver when the handle is imported successfully. Performing any operations on the file descriptor after it is imported results in undefined behavior.
If CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type is CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32, then exactly one of CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle and CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name must not be NULL. If CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle is not NULL, then it must represent a valid shared NT handle that references a memory object. Ownership of this handle is not transferred to CUDA after the import operation, so the application must release the handle using the appropriate system call. If CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name is not NULL, then it must point to a NULL-terminated array of UTF-16 characters that refers to a memory object.
If CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type is CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32_KMT, then CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle must be non- NULL and CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name must be NULL. The handle specified must be a globally shared KMT handle. This handle does not hold a reference to the underlying object, and thus will be invalid when all references to the memory object are destroyed.
If CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type is CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_HEAP, then exactly one of CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle and CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name must not be NULL. If CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle is not NULL, then it must represent a valid shared NT handle that is returned by ID3D12Device::CreateSharedHandle when referring to a ID3D12Heap object. This handle holds a reference to the underlying object. If CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name is not NULL, then it must point to a NULL-terminated array of UTF-16 characters that refers to a ID3D12Heap object.
If CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type is CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_RESOURCE, then exactly one of CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle and CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name must not be NULL. If CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle is not NULL, then it must represent a valid shared NT handle that is returned by ID3D12Device::CreateSharedHandle when referring to a ID3D12Resource object. This handle holds a reference to the underlying object. If CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name is not NULL, then it must point to a NULL-terminated array of UTF-16 characters that refers to a ID3D12Resource object.
If CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type is CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE, then CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle must represent a valid shared NT handle that is returned by IDXGIResource1::CreateSharedHandle when referring to a ID3D11Resource object. If CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name is not NULL, then it must point to a NULL-terminated array of UTF-16 characters that refers to a ID3D11Resource object.
If CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type is CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE_KMT, then CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle must represent a valid shared KMT handle that is returned by IDXGIResource::GetSharedHandle when referring to a ID3D11Resource object and CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name must be NULL.
If CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type is CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF, then CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::nvSciBufObject must be non- NULL and reference a valid NvSciBuf object. If the NvSciBuf object imported into CUDA is also mapped by other drivers, then the application must use cuWaitExternalSemaphoresAsync or cuSignalExternalSemaphoresAsync as appropriate barriers to maintain coherence between CUDA and the other drivers. See CUDA_EXTERNAL_SEMAPHORE_SIGNAL_SKIP_NVSCIBUF_MEMSYNC and CUDA_EXTERNAL_SEMAPHORE_WAIT_SKIP_NVSCIBUF_MEMSYNC for memory synchronization.
The size of the memory object must be specified in CUDA_EXTERNAL_MEMORY_HANDLE_DESC::size.
Specifying the flag CUDA_EXTERNAL_MEMORY_DEDICATED in CUDA_EXTERNAL_MEMORY_HANDLE_DESC::flags indicates that the resource is a dedicated resource. The definition of what a dedicated resource is outside the scope of this extension. This flag must be set if CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type is one of the following: CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_RESOURCE CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE_KMT
- Parameters
- memHandleDescCUDA_EXTERNAL_MEMORY_HANDLE_DESC
Memory import handle descriptor
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE
- extMem_outCUexternalMemory
Returned handle to an external memory object
See also
Notes
If the Vulkan memory imported into CUDA is mapped on the CPU then the application must use vkInvalidateMappedMemoryRanges/vkFlushMappedMemoryRanges as well as appropriate Vulkan pipeline barriers to maintain coherence between CPU and GPU. For more information on these APIs, please refer to “Synchronization and Cache Control” chapter from Vulkan specification.
- cuda.cuda.cuExternalMemoryGetMappedBuffer(extMem, CUDA_EXTERNAL_MEMORY_BUFFER_DESC bufferDesc: CUDA_EXTERNAL_MEMORY_BUFFER_DESC)¶
Maps a buffer onto an imported memory object.
Maps a buffer onto an imported memory object and returns a device pointer in devPtr.
The properties of the buffer being mapped must be described in bufferDesc. The CUDA_EXTERNAL_MEMORY_BUFFER_DESC structure is defined as follows:
typedefstructCUDA_EXTERNAL_MEMORY_BUFFER_DESC_st{ unsignedlonglongoffset; unsignedlonglongsize; unsignedintflags; }CUDA_EXTERNAL_MEMORY_BUFFER_DESC;
where CUDA_EXTERNAL_MEMORY_BUFFER_DESC::offset is the offset in the memory object where the buffer’s base address is. CUDA_EXTERNAL_MEMORY_BUFFER_DESC::size is the size of the buffer. CUDA_EXTERNAL_MEMORY_BUFFER_DESC::flags must be zero.
The offset and size have to be suitably aligned to match the requirements of the external API. Mapping two buffers whose ranges overlap may or may not result in the same virtual address being returned for the overlapped portion. In such cases, the application must ensure that all accesses to that region from the GPU are volatile. Otherwise writes made via one address are not guaranteed to be visible via the other address, even if they’re issued by the same thread. It is recommended that applications map the combined range instead of mapping separate buffers and then apply the appropriate offsets to the returned pointer to derive the individual buffers.
The returned pointer devPtr must be freed using cuMemFree.
- Parameters
- extMemAny
Handle to external memory object
- bufferDescCUDA_EXTERNAL_MEMORY_BUFFER_DESC
Buffer descriptor
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE
- devPtrCUdeviceptr
Returned device pointer to buffer
- cuda.cuda.cuExternalMemoryGetMappedMipmappedArray(extMem, CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC mipmapDesc: CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC)¶
Maps a CUDA mipmapped array onto an external memory object.
Maps a CUDA mipmapped array onto an external object and returns a handle to it in mipmap.
The properties of the CUDA mipmapped array being mapped must be described in mipmapDesc. The structure CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC is defined as follows:
typedefstructCUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC_st{ unsignedlonglongoffset; CUDA_ARRAY3D_DESCRIPTORarrayDesc; unsignedintnumLevels; }CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC;
where CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC::offset is the offset in the memory object where the base level of the mipmap chain is. CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC::arrayDesc describes the format, dimensions and type of the base level of the mipmap chain. For further details on these parameters, please refer to the documentation for cuMipmappedArrayCreate. Note that if the mipmapped array is bound as a color target in the graphics API, then the flag CUDA_ARRAY3D_COLOR_ATTACHMENT must be specified in CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC::arrayDesc::Flags. CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC::numLevels specifies the total number of levels in the mipmap chain.
If extMem was imported from a handle of type CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF, then CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC::numLevels must be equal to 1.
The returned CUDA mipmapped array must be freed using cuMipmappedArrayDestroy.
- Parameters
- extMemAny
Handle to external memory object
- mipmapDescCUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC
CUDA array descriptor
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE
- mipmapCUmipmappedArray
Returned CUDA mipmapped array
- cuda.cuda.cuDestroyExternalMemory(extMem)¶
Destroys an external memory object.
Destroys the specified external memory object. Any existing buffers and CUDA mipmapped arrays mapped onto this object must no longer be used and must be explicitly freed using cuMemFree and cuMipmappedArrayDestroy respectively.
- Parameters
- extMemAny
External memory object to be destroyed
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_HANDLE
- None
None
- cuda.cuda.cuImportExternalSemaphore(CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC semHandleDesc: CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC)¶
Imports an external semaphore.
Imports an externally allocated synchronization object and returns a handle to that in extSem_out.
The properties of the handle being imported must be described in semHandleDesc. The CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC is defined as follows:
typedefstructCUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_st{ CUexternalSemaphoreHandleTypetype; union{ intfd; struct{ void*handle; constvoid*name; }win32; constvoid*NvSciSyncObj; }handle; unsignedintflags; }CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC;
where CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type specifies the type of handle being imported. CUexternalSemaphoreHandleType is defined as:
typedefenumCUexternalSemaphoreHandleType_enum{ CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_FD=1, CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32=2, CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32_KMT=3, CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D12_FENCE=4, CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_FENCE=5, CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC=6, CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX=7, CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX_KMT=8, CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_FD=9, CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_WIN32=10 }CUexternalSemaphoreHandleType;
If CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_FD, then CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::fd must be a valid file descriptor referencing a synchronization object. Ownership of the file descriptor is transferred to the CUDA driver when the handle is imported successfully. Performing any operations on the file descriptor after it is imported results in undefined behavior.
If CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32, then exactly one of CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle and CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name must not be NULL. If CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle is not NULL, then it must represent a valid shared NT handle that references a synchronization object. Ownership of this handle is not transferred to CUDA after the import operation, so the application must release the handle using the appropriate system call. If CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name is not NULL, then it must name a valid synchronization object.
If CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32_KMT, then CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle must be non- NULL and CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name must be NULL. The handle specified must be a globally shared KMT handle. This handle does not hold a reference to the underlying object, and thus will be invalid when all references to the synchronization object are destroyed.
If CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D12_FENCE, then exactly one of CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle and CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name must not be NULL. If CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle is not NULL, then it must represent a valid shared NT handle that is returned by ID3D12Device::CreateSharedHandle when referring to a ID3D12Fence object. This handle holds a reference to the underlying object. If CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name is not NULL, then it must name a valid synchronization object that refers to a valid ID3D12Fence object.
If CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_FENCE, then CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle represents a valid shared NT handle that is returned by ID3D11Fence::CreateSharedHandle. If CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name is not NULL, then it must name a valid synchronization object that refers to a valid ID3D11Fence object.
If CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC, then CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::nvSciSyncObj represents a valid NvSciSyncObj.
CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX, then CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle represents a valid shared NT handle that is returned by IDXGIResource1::CreateSharedHandle when referring to a IDXGIKeyedMutex object. If CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name is not NULL, then it must name a valid synchronization object that refers to a valid IDXGIKeyedMutex object.
If CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX_KMT, then CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle represents a valid shared KMT handle that is returned by IDXGIResource::GetSharedHandle when referring to a IDXGIKeyedMutex object and CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name must be NULL.
If CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_FD, then CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::fd must be a valid file descriptor referencing a synchronization object. Ownership of the file descriptor is transferred to the CUDA driver when the handle is imported successfully. Performing any operations on the file descriptor after it is imported results in undefined behavior.
If CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_WIN32, then exactly one of CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle and CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name must not be NULL. If CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle is not NULL, then it must represent a valid shared NT handle that references a synchronization object. Ownership of this handle is not transferred to CUDA after the import operation, so the application must release the handle using the appropriate system call. If CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name is not NULL, then it must name a valid synchronization object.
- Parameters
- semHandleDescCUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC
Semaphore import handle descriptor
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_NOT_SUPPORTED CUDA_ERROR_INVALID_HANDLE
- extSem_outCUexternalSemaphore
Returned handle to an external semaphore
- cuda.cuda.cuSignalExternalSemaphoresAsync(extSemArray: List[CUexternalSemaphore], paramsArray: List[CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS], unsigned int numExtSems, stream)¶
Signals a set of external semaphore objects.
Enqueues a signal operation on a set of externally allocated semaphore object in the specified stream. The operations will be executed when all prior operations in the stream complete.
The exact semantics of signaling a semaphore depends on the type of the object.
If the semaphore object is any one of the following types: CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_FD, CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32, CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32_KMT then signaling the semaphore will set it to the signaled state.
If the semaphore object is any one of the following types: CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D12_FENCE, CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_FENCE, CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_FD, CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_WIN32 then the semaphore will be set to the value specified in CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS::params::fence::value.
If the semaphore object is of the type CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC this API sets CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS::params::nvSciSync::fence to a value that can be used by subsequent waiters of the same NvSciSync object to order operations with those currently submitted in stream. Such an update will overwrite previous contents of CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS::params::nvSciSync::fence. By default, signaling such an external semaphore object causes appropriate memory synchronization operations to be performed over all external memory objects that are imported as CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF. This ensures that any subsequent accesses made by other importers of the same set of NvSciBuf memory object(s) are coherent. These operations can be skipped by specifying the flag CUDA_EXTERNAL_SEMAPHORE_SIGNAL_SKIP_NVSCIBUF_MEMSYNC, which can be used as a performance optimization when data coherency is not required. But specifying this flag in scenarios where data coherency is required results in undefined behavior. Also, for semaphore object of the type CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC, if the NvSciSyncAttrList used to create the NvSciSyncObj had not set the flags in cuDeviceGetNvSciSyncAttributes to CUDA_NVSCISYNC_ATTR_SIGNAL, this API will return CUDA_ERROR_NOT_SUPPORTED.
If the semaphore object is any one of the following types: CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX, CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX_KMT then the keyed mutex will be released with the key specified in CUDA_EXTERNAL_SEMAPHORE_PARAMS::params::keyedmutex::key.
- Parameters
- extSemArrayList[CUexternalSemaphore]
Set of external semaphores to be signaled
- paramsArrayList[CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS]
Array of semaphore parameters
- numExtSemsunsigned int
Number of semaphores to signal
- streamCUstream or cudaStream_t
Stream to enqueue the signal operations in
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_NOT_SUPPORTED
- None
None
- cuda.cuda.cuWaitExternalSemaphoresAsync(extSemArray: List[CUexternalSemaphore], paramsArray: List[CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS], unsigned int numExtSems, stream)¶
Waits on a set of external semaphore objects.
Enqueues a wait operation on a set of externally allocated semaphore object in the specified stream. The operations will be executed when all prior operations in the stream complete.
The exact semantics of waiting on a semaphore depends on the type of the object.
If the semaphore object is any one of the following types: CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_FD, CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32, CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32_KMT then waiting on the semaphore will wait until the semaphore reaches the signaled state. The semaphore will then be reset to the unsignaled state. Therefore for every signal operation, there can only be one wait operation.
If the semaphore object is any one of the following types: CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D12_FENCE, CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_FENCE, CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_FD, CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_WIN32 then waiting on the semaphore will wait until the value of the semaphore is greater than or equal to CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS::params::fence::value.
If the semaphore object is of the type CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC then, waiting on the semaphore will wait until the CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS::params::nvSciSync::fence is signaled by the signaler of the NvSciSyncObj that was associated with this semaphore object. By default, waiting on such an external semaphore object causes appropriate memory synchronization operations to be performed over all external memory objects that are imported as CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF. This ensures that any subsequent accesses made by other importers of the same set of NvSciBuf memory object(s) are coherent. These operations can be skipped by specifying the flag CUDA_EXTERNAL_SEMAPHORE_WAIT_SKIP_NVSCIBUF_MEMSYNC, which can be used as a performance optimization when data coherency is not required. But specifying this flag in scenarios where data coherency is required results in undefined behavior. Also, for semaphore object of the type CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC, if the NvSciSyncAttrList used to create the NvSciSyncObj had not set the flags in cuDeviceGetNvSciSyncAttributes to CUDA_NVSCISYNC_ATTR_WAIT, this API will return CUDA_ERROR_NOT_SUPPORTED.
If the semaphore object is any one of the following types: CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX, CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX_KMT then the keyed mutex will be acquired when it is released with the key specified in CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS::params::keyedmutex::key or until the timeout specified by CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS::params::keyedmutex::timeoutMs has lapsed. The timeout interval can either be a finite value specified in milliseconds or an infinite value. In case an infinite value is specified the timeout never elapses. The windows INFINITE macro must be used to specify infinite timeout.
- Parameters
- extSemArrayList[CUexternalSemaphore]
External semaphores to be waited on
- paramsArrayList[CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS]
Array of semaphore parameters
- numExtSemsunsigned int
Number of semaphores to wait on
- streamCUstream or cudaStream_t
Stream to enqueue the wait operations in
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_NOT_SUPPORTED CUDA_ERROR_TIMEOUT
- None
None
- cuda.cuda.cuDestroyExternalSemaphore(extSem)¶
Destroys an external semaphore.
Destroys an external semaphore object and releases any references to the underlying resource. Any outstanding signals or waits must have completed before the semaphore is destroyed.
- Parameters
- extSemAny
External semaphore to be destroyed
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_HANDLE
- None
None
Stream memory operations¶
This section describes the stream memory operations of the low-level CUDA driver application programming interface.
The whole set of operations is disabled by default. Users are required to explicitly enable them, e.g. on Linux by passing the kernel module parameter shown below: modprobe nvidia NVreg_EnableStreamMemOPs=1 There is currently no way to enable these operations on other operating systems.
Users can programmatically query whether the device supports these operations with cuDeviceGetAttribute() and CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS.
Support for the CU_STREAM_WAIT_VALUE_NOR flag can be queried with CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR.
Support for the cuStreamWriteValue64() and cuStreamWaitValue64() functions, as well as for the CU_STREAM_MEM_OP_WAIT_VALUE_64 and CU_STREAM_MEM_OP_WRITE_VALUE_64 flags, can be queried with CU_DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS.
Support for both CU_STREAM_WAIT_VALUE_FLUSH and CU_STREAM_MEM_OP_FLUSH_REMOTE_WRITES requires dedicated platform hardware features and can be queried with cuDeviceGetAttribute() and CU_DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES.
Note that all memory pointers passed as parameters to these operations are device pointers. Where necessary a device pointer should be obtained, for example with cuMemHostGetDevicePointer().
None of the operations accepts pointers to managed memory buffers (cuMemAllocManaged).
- cuda.cuda.cuStreamWaitValue32(stream, addr, value, unsigned int flags)¶
Wait on a memory location.
Enqueues a synchronization of the stream on the given memory location. Work ordered after the operation will block until the given condition on the memory is satisfied. By default, the condition is to wait for (int32_t)(*addr - value) >= 0, a cyclic greater-or-equal. Other condition types can be specified via flags.
If the memory was registered via cuMemHostRegister(), the device pointer should be obtained with cuMemHostGetDevicePointer(). This function cannot be used with managed memory (cuMemAllocManaged).
Support for this can be queried with cuDeviceGetAttribute() and CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS.
Support for CU_STREAM_WAIT_VALUE_NOR can be queried with cuDeviceGetAttribute() and CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR.
- Parameters
- streamCUstream or cudaStream_t
The stream to synchronize on the memory location.
- addrAny
The memory location to wait on.
- valueAny
The value to compare with the memory location.
- flagsunsigned int
See CUstreamWaitValue_flags.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_SUPPORTED
- None
None
- cuda.cuda.cuStreamWaitValue64(stream, addr, value, unsigned int flags)¶
Wait on a memory location.
Enqueues a synchronization of the stream on the given memory location. Work ordered after the operation will block until the given condition on the memory is satisfied. By default, the condition is to wait for (int64_t)(*addr - value) >= 0, a cyclic greater-or-equal. Other condition types can be specified via flags.
If the memory was registered via cuMemHostRegister(), the device pointer should be obtained with cuMemHostGetDevicePointer().
Support for this can be queried with cuDeviceGetAttribute() and CU_DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS.
- Parameters
- streamCUstream or cudaStream_t
The stream to synchronize on the memory location.
- addrAny
The memory location to wait on.
- valueAny
The value to compare with the memory location.
- flagsunsigned int
See CUstreamWaitValue_flags.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_SUPPORTED
- None
None
- cuda.cuda.cuStreamWriteValue32(stream, addr, value, unsigned int flags)¶
Write a value to memory.
Write a value to memory. Unless the CU_STREAM_WRITE_VALUE_NO_MEMORY_BARRIER flag is passed, the write is preceded by a system-wide memory fence, equivalent to a __threadfence_system() but scoped to the stream rather than a CUDA thread.
If the memory was registered via cuMemHostRegister(), the device pointer should be obtained with cuMemHostGetDevicePointer(). This function cannot be used with managed memory (cuMemAllocManaged).
Support for this can be queried with cuDeviceGetAttribute() and CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS.
- Parameters
- streamCUstream or cudaStream_t
The stream to do the write in.
- addrAny
The device address to write to.
- valueAny
The value to write.
- flagsunsigned int
See CUstreamWriteValue_flags.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_SUPPORTED
- None
None
- cuda.cuda.cuStreamWriteValue64(stream, addr, value, unsigned int flags)¶
Write a value to memory.
Write a value to memory. Unless the CU_STREAM_WRITE_VALUE_NO_MEMORY_BARRIER flag is passed, the write is preceded by a system-wide memory fence, equivalent to a __threadfence_system() but scoped to the stream rather than a CUDA thread.
If the memory was registered via cuMemHostRegister(), the device pointer should be obtained with cuMemHostGetDevicePointer().
Support for this can be queried with cuDeviceGetAttribute() and CU_DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS.
- Parameters
- streamCUstream or cudaStream_t
The stream to do the write in.
- addrAny
The device address to write to.
- valueAny
The value to write.
- flagsunsigned int
See CUstreamWriteValue_flags.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_SUPPORTED
- None
None
- cuda.cuda.cuStreamBatchMemOp(stream, unsigned int count, paramArray: List[CUstreamBatchMemOpParams], unsigned int flags)¶
Batch operations to synchronize the stream via memory operations.
This is a batch version of cuStreamWaitValue32() and cuStreamWriteValue32(). Batching operations may avoid some performance overhead in both the API call and the device execution versus adding them to the stream in separate API calls. The operations are enqueued in the order they appear in the array.
See CUstreamBatchMemOpType for the full set of supported operations, and cuStreamWaitValue32(), cuStreamWaitValue64(), cuStreamWriteValue32(), and cuStreamWriteValue64() for details of specific operations.
Basic support for this can be queried with cuDeviceGetAttribute() and CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS. See related APIs for details on querying support for specific operations.
- Parameters
- streamCUstream or cudaStream_t
The stream to enqueue the operations in.
- countunsigned int
The number of operations in the array. Must be less than 256.
- paramArrayList[CUstreamBatchMemOpParams]
The types and parameters of the individual operations.
- flagsunsigned int
Reserved for future expansion; must be 0.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_SUPPORTED
- None
None
Execution Control¶
This section describes the execution control functions of the low-level CUDA driver application programming interface.
- cuda.cuda.cuFuncGetAttribute(attrib: CUfunction_attribute, hfunc)¶
Returns information about a function.
Returns in *pi the integer value of the attribute attrib on the kernel given by hfunc. The supported attributes are: - CU_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. - CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES: The size in bytes of statically-allocated shared memory per block required by this function. This does not include dynamically-allocated shared memory requested by the user at runtime. - CU_FUNC_ATTRIBUTE_CONST_SIZE_BYTES: The size in bytes of user-allocated constant memory required by this function. - CU_FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES: The size in bytes of local memory used by each thread of this function. - CU_FUNC_ATTRIBUTE_NUM_REGS: The number of registers used by each thread of this function. - CU_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. - CU_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. - CU_FUNC_CACHE_MODE_CA: The attribute to indicate whether the function has been compiled with user specified option “-Xptxas –dlcm=ca” set . - CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES: The maximum size in bytes of dynamically-allocated shared memory. - CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT: Preferred shared memory-L1 cache split ratio in percent of total shared memory.
- Parameters
- attribCUfunction_attribute
Attribute requested
- hfuncAny
Function to query attribute of
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_INVALID_VALUE
- piint
Returned attribute value
See also
cuCtxGetCacheConfig
cuCtxSetCacheConfig
cuFuncSetCacheConfig
cuLaunchKernel
cudaFuncGetAttributes
cudaFuncSetAttribute
- cuda.cuda.cuFuncSetAttribute(hfunc, attrib: CUfunction_attribute, int value)¶
Sets information about a function.
This call sets the value of a specified attribute attrib on the kernel given by hfunc to an integer value specified by val This function returns CUDA_SUCCESS if the new value of the attribute could be successfully set. If the set fails, this call will return an error. Not all attributes can have values set. Attempting to set a value on a read-only attribute will result in an error (CUDA_ERROR_INVALID_VALUE)
Supported attributes for the cuFuncSetAttribute call are: - CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES: This maximum size in bytes of dynamically-allocated shared memory. The value should contain the requested maximum size of dynamically-allocated shared memory. The sum of this value and the function attribute CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES cannot exceed the device attribute CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN. The maximal size of requestable dynamic shared memory may differ by GPU architecture. - CU_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. See CU_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.
- Parameters
- hfuncAny
Function to query attribute of
- attribCUfunction_attribute
Attribute requested
- valueint
The value to set
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuCtxGetCacheConfig
cuCtxSetCacheConfig
cuFuncSetCacheConfig
cuLaunchKernel
cudaFuncGetAttributes
cudaFuncSetAttribute
- cuda.cuda.cuFuncSetCacheConfig(hfunc, config: CUfunc_cache)¶
Sets the preferred cache configuration for a device function.
On devices where the L1 cache and shared memory use the same hardware resources, this sets through config the preferred cache configuration for the device function hfunc. This is only a preference. The driver will use the requested configuration if possible, but it is free to choose a different configuration if required to execute hfunc. Any context-wide preference set via cuCtxSetCacheConfig() will be overridden by this per-function setting unless the per-function setting is CU_FUNC_CACHE_PREFER_NONE. In that case, the current context-wide setting will be used.
This setting does nothing on devices where the size of the L1 cache and shared memory are fixed.
Launching a kernel with a different preference than the most recent preference setting may insert a device-side synchronization point.
The supported cache configurations are: - CU_FUNC_CACHE_PREFER_NONE: no preference for shared memory or L1 (default) - CU_FUNC_CACHE_PREFER_SHARED: prefer larger shared memory and smaller L1 cache - CU_FUNC_CACHE_PREFER_L1: prefer larger L1 cache and smaller shared memory - CU_FUNC_CACHE_PREFER_EQUAL: prefer equal sized L1 cache and shared memory
- Parameters
- hfuncAny
Kernel to configure cache for
- configCUfunc_cache
Requested cache configuration
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT
- None
None
See also
cuCtxGetCacheConfig
cuCtxSetCacheConfig
cuFuncGetAttribute
cuLaunchKernel
cudaFuncSetCacheConfig
Sets the shared memory configuration for a device function.
On devices with configurable shared memory banks, this function will force all subsequent launches of the specified device function to have the given shared memory bank size configuration. On any given launch of the function, the shared memory configuration of the device will be temporarily changed if needed to suit the function’s preferred configuration. Changes in shared memory configuration between subsequent launches of functions, may introduce a device side synchronization point.
Any per-function setting of shared memory bank size set via cuFuncSetSharedMemConfig will override the context wide setting set with cuCtxSetSharedMemConfig.
Changing the shared memory bank size will not increase shared memory usage or affect occupancy of kernels, but may have major effects on performance. Larger bank sizes will allow for greater potential bandwidth to shared memory, but will change what kinds of accesses to shared memory will result in bank conflicts.
This function will do nothing on devices with fixed shared memory bank size.
The supported bank configurations are: - CU_SHARED_MEM_CONFIG_DEFAULT_BANK_SIZE: use the context’s shared memory configuration when launching this function. - CU_SHARED_MEM_CONFIG_FOUR_BYTE_BANK_SIZE: set shared memory bank width to be natively four bytes when launching this function. - CU_SHARED_MEM_CONFIG_EIGHT_BYTE_BANK_SIZE: set shared memory bank width to be natively eight bytes when launching this function.
- Parameters
- hfuncAny
kernel to be given a shared memory config
- configCUsharedconfig
requested shared memory configuration
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT
- None
None
See also
cuCtxGetCacheConfig
cuCtxSetCacheConfig
cuCtxGetSharedMemConfig
cuCtxSetSharedMemConfig
cuFuncGetAttribute
cuLaunchKernel
cudaFuncSetSharedMemConfig
- cuda.cuda.cuFuncGetModule(hfunc)¶
Returns a module handle.
Returns in *hmod the handle of the module that function hfunc is located in. The lifetime of the module corresponds to the lifetime of the context it was loaded in or until the module is explicitly unloaded.
The CUDA runtime manages its own modules loaded into the primary context. If the handle returned by this API refers to a module loaded by the CUDA runtime, calling cuModuleUnload() on that module will result in undefined behavior.
- Parameters
- hfuncAny
Function to retrieve module for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_FOUND
- hmodCUmodule
Returned module handle
- cuda.cuda.cuLaunchKernel(f, unsigned int gridDimX, unsigned int gridDimY, unsigned int gridDimZ, unsigned int blockDimX, unsigned int blockDimY, unsigned int blockDimZ, unsigned int sharedMemBytes, hStream, kernelParams, void_ptr extra)¶
Launches a CUDA function.
Invokes the kernel f on a gridDimX x gridDimY x gridDimZ grid of blocks. Each block contains blockDimX x blockDimY x blockDimZ threads.
sharedMemBytes sets the amount of dynamic shared memory that will be available to each thread block.
Kernel parameters to f can be specified in one of two ways:
1) Kernel parameters can be specified via kernelParams. If f has N parameters, then kernelParams needs to be an array of N pointers. Each of `kernelParams`[0] through `kernelParams`[N-1] must point to a region of memory from which the actual kernel parameter will be copied. The number of kernel parameters and their offsets and sizes do not need to be specified as that information is retrieved directly from the kernel’s image.
2) Kernel parameters can also be packaged by the application into a single buffer that is passed in via the extra parameter. This places the burden on the application of knowing each kernel parameter’s size and alignment/padding within the buffer. Here is an example of using the extra parameter in this manner: size_targBufferSize; charargBuffer[256]; //populateargBufferandargBufferSize void*config[]={ CU_LAUNCH_PARAM_BUFFER_POINTER,argBuffer, CU_LAUNCH_PARAM_BUFFER_SIZE,&argBufferSize, CU_LAUNCH_PARAM_END }; status=cuLaunchKernel(f,gx,gy,gz,bx,by,bz,sh,s,NULL,config);
The extra parameter exists to allow cuLaunchKernel to take additional less commonly used arguments. extra specifies a list of names of extra settings and their corresponding values. Each extra setting name is immediately followed by the corresponding value. The list must be terminated with either NULL or CU_LAUNCH_PARAM_END.
CU_LAUNCH_PARAM_END, which indicates the end of the extra array;
CU_LAUNCH_PARAM_BUFFER_POINTER, which specifies that the next value
in extra will be a pointer to a buffer containing all the kernel parameters for launching kernel f; - CU_LAUNCH_PARAM_BUFFER_SIZE, which specifies that the next value in extra will be a pointer to a size_t containing the size of the buffer specified with CU_LAUNCH_PARAM_BUFFER_POINTER;
The error CUDA_ERROR_INVALID_VALUE will be returned if kernel parameters are specified with both kernelParams and extra (i.e. both kernelParams and extra are non-NULL).
Calling cuLaunchKernel() invalidates the persistent function state set through the following deprecated APIs: cuFuncSetBlockShape(), cuFuncSetSharedSize(), cuParamSetSize(), cuParamSeti(), cuParamSetf(), cuParamSetv().
Note that to use cuLaunchKernel(), the kernel f must either have been compiled with toolchain version 3.2 or later so that it will contain kernel parameter information, or have no kernel parameters. If either of these conditions is not met, then cuLaunchKernel() will return CUDA_ERROR_INVALID_IMAGE.
- Parameters
- fAny
Kernel to launch
- gridDimXunsigned int
Width of grid in blocks
- gridDimYunsigned int
Height of grid in blocks
- gridDimZunsigned int
Depth of grid in blocks
- blockDimXunsigned int
X dimension of each thread block
- blockDimYunsigned int
Y dimension of each thread block
- blockDimZunsigned int
Z dimension of each thread block
- sharedMemBytesunsigned int
Dynamic shared-memory size per thread block in bytes
- hStreamCUstream or cudaStream_t
Stream identifier
- kernelParamsAny
Array of pointers to kernel parameters
- extravoid_ptr
Extra options
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_INVALID_IMAGE CUDA_ERROR_INVALID_VALUE CUDA_ERROR_LAUNCH_FAILED CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES CUDA_ERROR_LAUNCH_TIMEOUT CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING CUDA_ERROR_SHARED_OBJECT_INIT_FAILED
- None
None
See also
cuCtxGetCacheConfig
cuCtxSetCacheConfig
cuFuncSetCacheConfig
cuFuncGetAttribute
cudaLaunchKernel
- cuda.cuda.cuLaunchCooperativeKernel(f, unsigned int gridDimX, unsigned int gridDimY, unsigned int gridDimZ, unsigned int blockDimX, unsigned int blockDimY, unsigned int blockDimZ, unsigned int sharedMemBytes, hStream, kernelParams)¶
Launches a CUDA function where thread blocks can cooperate and synchronize as they execute.
Invokes the kernel f on a gridDimX x gridDimY x gridDimZ grid of blocks. Each block contains blockDimX x blockDimY x blockDimZ threads.
sharedMemBytes sets the amount of dynamic shared memory that will be available to each thread block.
The device on which this kernel is invoked must have a non-zero value for the device attribute CU_DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH.
The total number of blocks launched cannot exceed the maximum number of blocks per multiprocessor as returned by cuOccupancyMaxActiveBlocksPerMultiprocessor (or cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags) times the number of multiprocessors as specified by the device attribute CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
The kernel cannot make use of CUDA dynamic parallelism.
Kernel parameters must be specified via kernelParams. If f has N parameters, then kernelParams needs to be an array of N pointers. Each of `kernelParams`[0] through `kernelParams`[N-1] must point to a region of memory from which the actual kernel parameter will be copied. The number of kernel parameters and their offsets and sizes do not need to be specified as that information is retrieved directly from the kernel’s image.
Calling cuLaunchCooperativeKernel() sets persistent function state that is the same as function state set through cuLaunchKernel API
When the kernel f is launched via cuLaunchCooperativeKernel(), the previous block shape, shared size and parameter info associated with f is overwritten.
Note that to use cuLaunchCooperativeKernel(), the kernel f must either have been compiled with toolchain version 3.2 or later so that it will contain kernel parameter information, or have no kernel parameters. If either of these conditions is not met, then cuLaunchCooperativeKernel() will return CUDA_ERROR_INVALID_IMAGE.
- Parameters
- fAny
Kernel to launch
- gridDimXunsigned int
Width of grid in blocks
- gridDimYunsigned int
Height of grid in blocks
- gridDimZunsigned int
Depth of grid in blocks
- blockDimXunsigned int
X dimension of each thread block
- blockDimYunsigned int
Y dimension of each thread block
- blockDimZunsigned int
Z dimension of each thread block
- sharedMemBytesunsigned int
Dynamic shared-memory size per thread block in bytes
- hStreamCUstream or cudaStream_t
Stream identifier
- kernelParamsAny
Array of pointers to kernel parameters
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_INVALID_IMAGE CUDA_ERROR_INVALID_VALUE CUDA_ERROR_LAUNCH_FAILED CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES CUDA_ERROR_LAUNCH_TIMEOUT CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE CUDA_ERROR_SHARED_OBJECT_INIT_FAILED
- None
None
See also
cuCtxGetCacheConfig
cuCtxSetCacheConfig
cuFuncSetCacheConfig
cuFuncGetAttribute
cuLaunchCooperativeKernelMultiDevice
cudaLaunchCooperativeKernel
- cuda.cuda.cuLaunchCooperativeKernelMultiDevice(launchParamsList: List[CUDA_LAUNCH_PARAMS], unsigned int numDevices, unsigned int flags)¶
Launches CUDA functions on multiple devices where thread blocks can cooperate and synchronize as they execute.
DeprecatedThis function is deprecated as of CUDA 11.3.
Invokes kernels as specified in the launchParamsList array where each element of the array specifies all the parameters required to perform a single kernel launch. These kernels can cooperate and synchronize as they execute. The size of the array is specified by numDevices.
No two kernels can be launched on the same device. All the devices targeted by this multi-device launch must be identical. All devices must have a non-zero value for the device attribute CU_DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH.
All kernels launched must be identical with respect to the compiled code. Note that any device, constant or managed variables present in the module that owns the kernel launched on each device, are independently instantiated on every device. It is the application’s responsiblity to ensure these variables are initialized and used appropriately.
The size of the grids as specified in blocks, the size of the blocks themselves and the amount of shared memory used by each thread block must also match across all launched kernels.
The streams used to launch these kernels must have been created via either cuStreamCreate or cuStreamCreateWithPriority. The NULL stream or CU_STREAM_LEGACY or CU_STREAM_PER_THREAD cannot be used.
The total number of blocks launched per kernel cannot exceed the maximum number of blocks per multiprocessor as returned by cuOccupancyMaxActiveBlocksPerMultiprocessor (or cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags) times the number of multiprocessors as specified by the device attribute CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT. Since the total number of blocks launched per device has to match across all devices, the maximum number of blocks that can be launched per device will be limited by the device with the least number of multiprocessors.
The kernels cannot make use of CUDA dynamic parallelism.
The CUDA_LAUNCH_PARAMS structure is defined as: typedefstructCUDA_LAUNCH_PARAMS_st { CUfunctionfunction; unsignedintgridDimX; unsignedintgridDimY; unsignedintgridDimZ; unsignedintblockDimX; unsignedintblockDimY; unsignedintblockDimZ; unsignedintsharedMemBytes; CUstreamhStream; void**kernelParams; }CUDA_LAUNCH_PARAMS; where: - CUDA_LAUNCH_PARAMS::function specifies the kernel to be launched. All functions must be identical with respect to the compiled code. - CUDA_LAUNCH_PARAMS::gridDimX is the width of the grid in blocks. This must match across all kernels launched. - CUDA_LAUNCH_PARAMS::gridDimY is the height of the grid in blocks. This must match across all kernels launched. - CUDA_LAUNCH_PARAMS::gridDimZ is the depth of the grid in blocks. This must match across all kernels launched. - CUDA_LAUNCH_PARAMS::blockDimX is the X dimension of each thread block. This must match across all kernels launched. - CUDA_LAUNCH_PARAMS::blockDimX is the Y dimension of each thread block. This must match across all kernels launched. - CUDA_LAUNCH_PARAMS::blockDimZ is the Z dimension of each thread block. This must match across all kernels launched. - CUDA_LAUNCH_PARAMS::sharedMemBytes is the dynamic shared-memory size per thread block in bytes. This must match across all kernels launched. - CUDA_LAUNCH_PARAMS::hStream is the handle to the stream to perform the launch in. This cannot be the NULL stream or CU_STREAM_LEGACY or CU_STREAM_PER_THREAD. The CUDA context associated with this stream must match that associated with CUDA_LAUNCH_PARAMS::function. - CUDA_LAUNCH_PARAMS::kernelParams is an array of pointers to kernel parameters. If CUDA_LAUNCH_PARAMS::function has N parameters, then CUDA_LAUNCH_PARAMS::kernelParams needs to be an array of N pointers. Each of CUDA_LAUNCH_PARAMS::kernelParams[0] through CUDA_LAUNCH_PARAMS::kernelParams[N-1] must point to a region of memory from which the actual kernel parameter will be copied. The number of kernel parameters and their offsets and sizes do not need to be specified as that information is retrieved directly from the kernel’s image.
By default, the kernel won’t begin execution on any GPU until all prior work in all the specified streams has completed. This behavior can be overridden by specifying the flag CUDA_COOPERATIVE_LAUNCH_MULTI_DEVICE_NO_PRE_LAUNCH_SYNC. When this flag is specified, each kernel will only wait for prior work in the stream corresponding to that GPU to complete before it begins execution.
Similarly, by default, any subsequent work pushed in any of the specified streams will not begin execution until the kernels on all GPUs have completed. This behavior can be overridden by specifying the flag CUDA_COOPERATIVE_LAUNCH_MULTI_DEVICE_NO_POST_LAUNCH_SYNC. When this flag is specified, any subsequent work pushed in any of the specified streams will only wait for the kernel launched on the GPU corresponding to that stream to complete before it begins execution.
Calling cuLaunchCooperativeKernelMultiDevice() sets persistent function state that is the same as function state set through cuLaunchKernel API when called individually for each element in launchParamsList.
When kernels are launched via cuLaunchCooperativeKernelMultiDevice(), the previous block shape, shared size and parameter info associated with each CUDA_LAUNCH_PARAMS::function in launchParamsList is overwritten.
Note that to use cuLaunchCooperativeKernelMultiDevice(), the kernels must either have been compiled with toolchain version 3.2 or later so that it will contain kernel parameter information, or have no kernel parameters. If either of these conditions is not met, then cuLaunchCooperativeKernelMultiDevice() will return CUDA_ERROR_INVALID_IMAGE.
- Parameters
- launchParamsListList[CUDA_LAUNCH_PARAMS]
List of launch parameters, one per device
- numDevicesunsigned int
Size of the launchParamsList array
- flagsunsigned int
Flags to control launch behavior
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_INVALID_IMAGE CUDA_ERROR_INVALID_VALUE CUDA_ERROR_LAUNCH_FAILED CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES CUDA_ERROR_LAUNCH_TIMEOUT CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE CUDA_ERROR_SHARED_OBJECT_INIT_FAILED
- None
None
See also
cuCtxGetCacheConfig
cuCtxSetCacheConfig
cuFuncSetCacheConfig
cuFuncGetAttribute
cuLaunchCooperativeKernel
cudaLaunchCooperativeKernelMultiDevice
- cuda.cuda.cuLaunchHostFunc(hStream, fn, userData)¶
Enqueues a host function call in a stream.
Enqueues a host function to run in a stream. The function will be called after currently enqueued work and will block work added after it.
The host function must not make any CUDA API calls. Attempting to use a CUDA API may result in CUDA_ERROR_NOT_PERMITTED, but this is not required. The host function must not perform any synchronization that may depend on outstanding CUDA work not mandated to run earlier. Host functions without a mandated order (such as in independent streams) execute in undefined order and may be serialized.
For the purposes of Unified Memory, execution makes a number of guarantees: - The stream is considered idle for the duration of the function’s execution. Thus, for example, the function may always use memory attached to the stream it was enqueued in. - The start of execution of the function has the same effect as synchronizing an event recorded in the same stream immediately prior to the function. It thus synchronizes streams which have been “joined” prior to the function. - Adding device work to any stream does not have the effect of making the stream active until all preceding host functions and stream callbacks have executed. Thus, for example, a function might use global attached memory even if work has been added to another stream, if the work has been ordered behind the function call with an event. - Completion of the function does not cause a stream to become active except as described above. The stream will remain idle if no device work follows the function, and will remain idle across consecutive host functions or stream callbacks without device work in between. Thus, for example, stream synchronization can be done by signaling from a host function at the end of the stream.
Note that, in contrast to cuStreamAddCallback, the function will not be called in the event of an error in the CUDA context.
- Parameters
- hStreamCUstream or cudaStream_t
Stream to enqueue function call in
- fnAny
The function to call once preceding stream operations are complete
- userDataAny
User-specified data to be passed to the function
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_NOT_SUPPORTED
- None
None
Graph Management¶
This section describes the graph management functions of the low-level CUDA driver application programming interface.
- cuda.cuda.cuGraphCreate(unsigned int flags)¶
Creates a graph.
Creates an empty graph, which is returned via phGraph.
- Parameters
- flagsunsigned int
Graph creation flags, must be 0
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE CUDA_ERROR_OUT_OF_MEMORY
- phGraphCUgraph
Returns newly created graph
- cuda.cuda.cuGraphAddKernelNode(hGraph, dependencies: List[CUgraphNode], size_t numDependencies, CUDA_KERNEL_NODE_PARAMS nodeParams: CUDA_KERNEL_NODE_PARAMS)¶
Creates a kernel execution node and adds it to a graph.
Creates a new kernel execution node and adds it to hGraph with numDependencies dependencies specified via dependencies and arguments specified in nodeParams. It is possible for numDependencies to be 0, in which case the node will be placed at the root of the graph. dependencies may not have any duplicate entries. A handle to the new node will be returned in phGraphNode.
The CUDA_KERNEL_NODE_PARAMS structure is defined as:
typedefstructCUDA_KERNEL_NODE_PARAMS_st{ CUfunctionfunc; unsignedintgridDimX; unsignedintgridDimY; unsignedintgridDimZ; unsignedintblockDimX; unsignedintblockDimY; unsignedintblockDimZ; unsignedintsharedMemBytes; void**kernelParams; void**extra; }CUDA_KERNEL_NODE_PARAMS;
When the graph is launched, the node will invoke kernel func on a (gridDimX x gridDimY x gridDimZ) grid of blocks. Each block contains (blockDimX x blockDimY x blockDimZ) threads.
sharedMemBytes sets the amount of dynamic shared memory that will be available to each thread block.
Kernel parameters to func can be specified in one of two ways:
1) Kernel parameters can be specified via kernelParams. If the kernel has N parameters, then kernelParams needs to be an array of N pointers. Each pointer, from `kernelParams`[0] to `kernelParams`[N-1], points to the region of memory from which the actual parameter will be copied. The number of kernel parameters and their offsets and sizes do not need to be specified as that information is retrieved directly from the kernel’s image.
2) Kernel parameters for non-cooperative kernels can also be packaged by the application into a single buffer that is passed in via extra. This places the burden on the application of knowing each kernel parameter’s size and alignment/padding within the buffer. The extra parameter exists to allow this function to take additional less commonly used arguments. extra specifies a list of names of extra settings and their corresponding values. Each extra setting name is immediately followed by the corresponding value. The list must be terminated with either NULL or CU_LAUNCH_PARAM_END.
CU_LAUNCH_PARAM_END, which indicates the end of the extra array;
CU_LAUNCH_PARAM_BUFFER_POINTER, which specifies that the next value
in extra will be a pointer to a buffer containing all the kernel parameters for launching kernel func; - CU_LAUNCH_PARAM_BUFFER_SIZE, which specifies that the next value in extra will be a pointer to a size_t containing the size of the buffer specified with CU_LAUNCH_PARAM_BUFFER_POINTER;
The error CUDA_ERROR_INVALID_VALUE will be returned if kernel parameters are specified with both kernelParams and extra (i.e. both kernelParams and extra are non-NULL). CUDA_ERROR_INVALID_VALUE will be returned if extra is used for a cooperative kernel.
The kernelParams or extra array, as well as the argument values it points to, are copied during this call.
- Parameters
- hGraphAny
Graph to which to add the node
- dependenciesList[CUgraphNode]
Dependencies of the node
- numDependenciessize_t
Number of dependencies
- nodeParamsCUDA_KERNEL_NODE_PARAMS
Parameters for the GPU execution node
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- phGraphNodeCUgraphNode
Returns newly created node
See also
Notes
Kernels launched using graphs must not use texture and surface references. Reading or writing through any texture or surface reference is undefined behavior. This restriction does not apply to texture and surface objects.
- cuda.cuda.cuGraphKernelNodeGetParams(hNode)¶
Returns a kernel node’s parameters.
Returns the parameters of kernel node hNode in nodeParams. The kernelParams or extra array returned in nodeParams, as well as the argument values it points to, are owned by the node. This memory remains valid until the node is destroyed or its parameters are modified, and should not be modified directly. Use cuGraphKernelNodeSetParams to update the parameters of this node.
The params will contain either kernelParams or extra, according to which of these was most recently set on the node.
- Parameters
- hNodeCUgraphNode or cudaGraphNode_t
Node to get the parameters for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- nodeParamsCUDA_KERNEL_NODE_PARAMS
Pointer to return the parameters
- cuda.cuda.cuGraphKernelNodeSetParams(hNode, CUDA_KERNEL_NODE_PARAMS nodeParams: CUDA_KERNEL_NODE_PARAMS)¶
Sets a kernel node’s parameters.
Sets the parameters of kernel node hNode to nodeParams.
- Parameters
- hNodeCUgraphNode or cudaGraphNode_t
Node to set the parameters for
- nodeParamsCUDA_KERNEL_NODE_PARAMS
Parameters to copy
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_OUT_OF_MEMORY
- None
None
- cuda.cuda.cuGraphAddMemcpyNode(hGraph, dependencies: List[CUgraphNode], size_t numDependencies, CUDA_MEMCPY3D copyParams: CUDA_MEMCPY3D, ctx)¶
Creates a memcpy node and adds it to a graph.
Creates a new memcpy node and adds it to hGraph with numDependencies dependencies specified via dependencies. It is possible for numDependencies to be 0, in which case the node will be placed at the root of the graph. dependencies may not have any duplicate entries. A handle to the new node will be returned in phGraphNode.
When the graph is launched, the node will perform the memcpy described by copyParams. See cuMemcpy3D() for a description of the structure and its restrictions.
Memcpy nodes have some additional restrictions with regards to managed memory, if the system contains at least one device which has a zero value for the device attribute CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS. If one or more of the operands refer to managed memory, then using the memory type CU_MEMORYTYPE_UNIFIED is disallowed for those operand(s). The managed memory will be treated as residing on either the host or the device, depending on which memory type is specified.
- Parameters
- hGraphAny
Graph to which to add the node
- dependenciesList[CUgraphNode]
Dependencies of the node
- numDependenciessize_t
Number of dependencies
- copyParamsCUDA_MEMCPY3D
Parameters for the memory copy
- ctxAny
Context on which to run the node
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- phGraphNodeCUgraphNode
Returns newly created node
- cuda.cuda.cuGraphMemcpyNodeGetParams(hNode)¶
Returns a memcpy node’s parameters.
Returns the parameters of memcpy node hNode in nodeParams.
- Parameters
- hNodeCUgraphNode or cudaGraphNode_t
Node to get the parameters for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- nodeParamsCUDA_MEMCPY3D
Pointer to return the parameters
- cuda.cuda.cuGraphMemcpyNodeSetParams(hNode, CUDA_MEMCPY3D nodeParams: CUDA_MEMCPY3D)¶
Sets a memcpy node’s parameters.
Sets the parameters of memcpy node hNode to nodeParams.
- Parameters
- hNodeCUgraphNode or cudaGraphNode_t
Node to set the parameters for
- nodeParamsCUDA_MEMCPY3D
Parameters to copy
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- None
None
- cuda.cuda.cuGraphAddMemsetNode(hGraph, dependencies: List[CUgraphNode], size_t numDependencies, CUDA_MEMSET_NODE_PARAMS memsetParams: CUDA_MEMSET_NODE_PARAMS, ctx)¶
Creates a memset node and adds it to a graph.
Creates a new memset node and adds it to hGraph with numDependencies dependencies specified via dependencies. It is possible for numDependencies to be 0, in which case the node will be placed at the root of the graph. dependencies may not have any duplicate entries. A handle to the new node will be returned in phGraphNode.
The element size must be 1, 2, or 4 bytes. When the graph is launched, the node will perform the memset described by memsetParams.
- Parameters
- hGraphAny
Graph to which to add the node
- dependenciesList[CUgraphNode]
Dependencies of the node
- numDependenciessize_t
Number of dependencies
- memsetParamsCUDA_MEMSET_NODE_PARAMS
Parameters for the memory set
- ctxAny
Context on which to run the node
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_CONTEXT
- phGraphNodeCUgraphNode
Returns newly created node
- cuda.cuda.cuGraphMemsetNodeGetParams(hNode)¶
Returns a memset node’s parameters.
Returns the parameters of memset node hNode in nodeParams.
- Parameters
- hNodeCUgraphNode or cudaGraphNode_t
Node to get the parameters for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- nodeParamsCUDA_MEMSET_NODE_PARAMS
Pointer to return the parameters
- cuda.cuda.cuGraphMemsetNodeSetParams(hNode, CUDA_MEMSET_NODE_PARAMS nodeParams: CUDA_MEMSET_NODE_PARAMS)¶
Sets a memset node’s parameters.
Sets the parameters of memset node hNode to nodeParams.
- Parameters
- hNodeCUgraphNode or cudaGraphNode_t
Node to set the parameters for
- nodeParamsCUDA_MEMSET_NODE_PARAMS
Parameters to copy
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- None
None
- cuda.cuda.cuGraphAddHostNode(hGraph, dependencies: List[CUgraphNode], size_t numDependencies, CUDA_HOST_NODE_PARAMS nodeParams: CUDA_HOST_NODE_PARAMS)¶
Creates a host execution node and adds it to a graph.
Creates a new CPU execution node and adds it to hGraph with numDependencies dependencies specified via dependencies and arguments specified in nodeParams. It is possible for numDependencies to be 0, in which case the node will be placed at the root of the graph. dependencies may not have any duplicate entries. A handle to the new node will be returned in phGraphNode.
When the graph is launched, the node will invoke the specified CPU function. Host nodes are not supported under MPS with pre-Volta GPUs.
- Parameters
- hGraphAny
Graph to which to add the node
- dependenciesList[CUgraphNode]
Dependencies of the node
- numDependenciessize_t
Number of dependencies
- nodeParamsCUDA_HOST_NODE_PARAMS
Parameters for the host node
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_NOT_SUPPORTED CUDA_ERROR_INVALID_VALUE
- phGraphNodeCUgraphNode
Returns newly created node
- cuda.cuda.cuGraphHostNodeGetParams(hNode)¶
Returns a host node’s parameters.
Returns the parameters of host node hNode in nodeParams.
- Parameters
- hNodeCUgraphNode or cudaGraphNode_t
Node to get the parameters for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- nodeParamsCUDA_HOST_NODE_PARAMS
Pointer to return the parameters
- cuda.cuda.cuGraphHostNodeSetParams(hNode, CUDA_HOST_NODE_PARAMS nodeParams: CUDA_HOST_NODE_PARAMS)¶
Sets a host node’s parameters.
Sets the parameters of host node hNode to nodeParams.
- Parameters
- hNodeCUgraphNode or cudaGraphNode_t
Node to set the parameters for
- nodeParamsCUDA_HOST_NODE_PARAMS
Parameters to copy
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- None
None
- cuda.cuda.cuGraphAddChildGraphNode(hGraph, dependencies: List[CUgraphNode], size_t numDependencies, childGraph)¶
Creates a child graph node and adds it to a graph.
Creates a new node which executes an embedded graph, and adds it to hGraph with numDependencies dependencies specified via dependencies. It is possible for numDependencies to be 0, in which case the node will be placed at the root of the graph. dependencies may not have any duplicate entries. A handle to the new node will be returned in phGraphNode.
If hGraph contains allocation or free nodes, this call will return an error.
The node executes an embedded child graph. The child graph is cloned in this call.
- Parameters
- hGraphAny
Graph to which to add the node
- dependenciesList[CUgraphNode]
Dependencies of the node
- numDependenciessize_t
Number of dependencies
- childGraphCUgraph or cudaGraph_t
The graph to clone into this node
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- phGraphNodeCUgraphNode
Returns newly created node
- cuda.cuda.cuGraphChildGraphNodeGetGraph(hNode)¶
Gets a handle to the embedded graph of a child graph node.
Gets a handle to the embedded graph in a child graph node. This call does not clone the graph. Changes to the graph will be reflected in the node, and the node retains ownership of the graph.
Allocation and free nodes cannot be added to the returned graph. Attempting to do so will return an error.
- Parameters
- hNodeCUgraphNode or cudaGraphNode_t
Node to get the embedded graph for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- phGraphCUgraph
Location to store a handle to the graph
- cuda.cuda.cuGraphAddEmptyNode(hGraph, dependencies: List[CUgraphNode], size_t numDependencies)¶
Creates an empty node and adds it to a graph.
Creates a new node which performs no operation, and adds it to hGraph with numDependencies dependencies specified via dependencies. It is possible for numDependencies to be 0, in which case the node will be placed at the root of the graph. dependencies may not have any duplicate entries. A handle to the new node will be returned in phGraphNode.
An empty node performs no operation during execution, but can be used for transitive ordering. For example, a phased execution graph with 2 groups of n nodes with a barrier between them can be represented using an empty node and 2*n dependency edges, rather than no empty node and n^2 dependency edges.
- Parameters
- hGraphAny
Graph to which to add the node
- dependenciesList[CUgraphNode]
Dependencies of the node
- numDependenciessize_t
Number of dependencies
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- phGraphNodeCUgraphNode
Returns newly created node
- cuda.cuda.cuGraphAddEventRecordNode(hGraph, dependencies: List[CUgraphNode], size_t numDependencies, event)¶
Creates an event record node and adds it to a graph.
Creates a new event record node and adds it to hGraph with numDependencies dependencies specified via dependencies and event specified in event. It is possible for numDependencies to be 0, in which case the node will be placed at the root of the graph. dependencies may not have any duplicate entries. A handle to the new node will be returned in phGraphNode.
Each launch of the graph will record event to capture execution of the node’s dependencies.
- Parameters
- hGraphAny
Graph to which to add the node
- dependenciesList[CUgraphNode]
Dependencies of the node
- numDependenciessize_t
Number of dependencies
- eventCUevent or cudaEvent_t
Event for the node
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_NOT_SUPPORTED CUDA_ERROR_INVALID_VALUE
- phGraphNodeCUgraphNode
Returns newly created node
- cuda.cuda.cuGraphEventRecordNodeGetEvent(hNode)¶
Returns the event associated with an event record node.
Returns the event of event record node hNode in event_out.
- Parameters
- hNodeCUgraphNode or cudaGraphNode_t
Node to get the event for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- event_outCUevent
Pointer to return the event
- cuda.cuda.cuGraphEventRecordNodeSetEvent(hNode, event)¶
Sets an event record node’s event.
Sets the event of event record node hNode to event.
- Parameters
- hNodeCUgraphNode or cudaGraphNode_t
Node to set the event for
- eventCUevent or cudaEvent_t
Event to use
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_OUT_OF_MEMORY
- None
None
- cuda.cuda.cuGraphAddEventWaitNode(hGraph, dependencies: List[CUgraphNode], size_t numDependencies, event)¶
Creates an event wait node and adds it to a graph.
Creates a new event wait node and adds it to hGraph with numDependencies dependencies specified via dependencies and event specified in event. It is possible for numDependencies to be 0, in which case the node will be placed at the root of the graph. dependencies may not have any duplicate entries. A handle to the new node will be returned in phGraphNode.
The graph node will wait for all work captured in event. See cuEventRecord() for details on what is captured by an event. event may be from a different context or device than the launch stream.
- Parameters
- hGraphAny
Graph to which to add the node
- dependenciesList[CUgraphNode]
Dependencies of the node
- numDependenciessize_t
Number of dependencies
- eventCUevent or cudaEvent_t
Event for the node
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_NOT_SUPPORTED CUDA_ERROR_INVALID_VALUE
- phGraphNodeCUgraphNode
Returns newly created node
- cuda.cuda.cuGraphEventWaitNodeGetEvent(hNode)¶
Returns the event associated with an event wait node.
Returns the event of event wait node hNode in event_out.
- Parameters
- hNodeCUgraphNode or cudaGraphNode_t
Node to get the event for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- event_outCUevent
Pointer to return the event
- cuda.cuda.cuGraphEventWaitNodeSetEvent(hNode, event)¶
Sets an event wait node’s event.
Sets the event of event wait node hNode to event.
- Parameters
- hNodeCUgraphNode or cudaGraphNode_t
Node to set the event for
- eventCUevent or cudaEvent_t
Event to use
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_OUT_OF_MEMORY
- None
None
- cuda.cuda.cuGraphAddExternalSemaphoresSignalNode(hGraph, dependencies: List[CUgraphNode], size_t numDependencies, CUDA_EXT_SEM_SIGNAL_NODE_PARAMS nodeParams: CUDA_EXT_SEM_SIGNAL_NODE_PARAMS)¶
Creates an external semaphore signal node and adds it to a graph.
Creates a new external semaphore signal node and adds it to hGraph with numDependencies dependencies specified via dependencies and arguments specified in nodeParams. It is possible for numDependencies to be 0, in which case the node will be placed at the root of the graph. dependencies may not have any duplicate entries. A handle to the new node will be returned in phGraphNode.
Performs a signal operation on a set of externally allocated semaphore objects when the node is launched. The operation(s) will occur after all of the node’s dependencies have completed.
- Parameters
- hGraphAny
Graph to which to add the node
- dependenciesList[CUgraphNode]
Dependencies of the node
- numDependenciessize_t
Number of dependencies
- nodeParamsCUDA_EXT_SEM_SIGNAL_NODE_PARAMS
Parameters for the node
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_NOT_SUPPORTED CUDA_ERROR_INVALID_VALUE
- phGraphNodeCUgraphNode
Returns newly created node
See also
cuGraphExternalSemaphoresSignalNodeGetParams
cuGraphExternalSemaphoresSignalNodeSetParams
cuGraphExecExternalSemaphoresSignalNodeSetParams
cuGraphAddExternalSemaphoresWaitNode
cuImportExternalSemaphore
cuSignalExternalSemaphoresAsync
cuWaitExternalSemaphoresAsync
cuGraphCreate
cuGraphDestroyNode
cuGraphAddEventRecordNode
cuGraphAddEventWaitNode
cuGraphAddChildGraphNode
cuGraphAddEmptyNode
cuGraphAddKernelNode
cuGraphAddMemcpyNode
cuGraphAddMemsetNode
- cuda.cuda.cuGraphExternalSemaphoresSignalNodeGetParams(hNode)¶
Returns an external semaphore signal node’s parameters.
Returns the parameters of an external semaphore signal node hNode in params_out. The extSemArray and paramsArray returned in params_out, are owned by the node. This memory remains valid until the node is destroyed or its parameters are modified, and should not be modified directly. Use cuGraphExternalSemaphoresSignalNodeSetParams to update the parameters of this node.
- Parameters
- hNodeCUgraphNode or cudaGraphNode_t
Node to get the parameters for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- params_outCUDA_EXT_SEM_SIGNAL_NODE_PARAMS
Pointer to return the parameters
- cuda.cuda.cuGraphExternalSemaphoresSignalNodeSetParams(hNode, CUDA_EXT_SEM_SIGNAL_NODE_PARAMS nodeParams: CUDA_EXT_SEM_SIGNAL_NODE_PARAMS)¶
Sets an external semaphore signal node’s parameters.
Sets the parameters of an external semaphore signal node hNode to nodeParams.
- Parameters
- hNodeCUgraphNode or cudaGraphNode_t
Node to set the parameters for
- nodeParamsCUDA_EXT_SEM_SIGNAL_NODE_PARAMS
Parameters to copy
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_OUT_OF_MEMORY
- None
None
- cuda.cuda.cuGraphAddExternalSemaphoresWaitNode(hGraph, dependencies: List[CUgraphNode], size_t numDependencies, CUDA_EXT_SEM_WAIT_NODE_PARAMS nodeParams: CUDA_EXT_SEM_WAIT_NODE_PARAMS)¶
Creates an external semaphore wait node and adds it to a graph.
Creates a new external semaphore wait node and adds it to hGraph with numDependencies dependencies specified via dependencies and arguments specified in nodeParams. It is possible for numDependencies to be 0, in which case the node will be placed at the root of the graph. dependencies may not have any duplicate entries. A handle to the new node will be returned in phGraphNode.
Performs a wait operation on a set of externally allocated semaphore objects when the node is launched. The node’s dependencies will not be launched until the wait operation has completed.
- Parameters
- hGraphAny
Graph to which to add the node
- dependenciesList[CUgraphNode]
Dependencies of the node
- numDependenciessize_t
Number of dependencies
- nodeParamsCUDA_EXT_SEM_WAIT_NODE_PARAMS
Parameters for the node
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_NOT_SUPPORTED CUDA_ERROR_INVALID_VALUE
- phGraphNodeCUgraphNode
Returns newly created node
See also
cuGraphExternalSemaphoresWaitNodeGetParams
cuGraphExternalSemaphoresWaitNodeSetParams
cuGraphExecExternalSemaphoresWaitNodeSetParams
cuGraphAddExternalSemaphoresSignalNode
cuImportExternalSemaphore
cuSignalExternalSemaphoresAsync
cuWaitExternalSemaphoresAsync
cuGraphCreate
cuGraphDestroyNode
cuGraphAddEventRecordNode
cuGraphAddEventWaitNode
cuGraphAddChildGraphNode
cuGraphAddEmptyNode
cuGraphAddKernelNode
cuGraphAddMemcpyNode
cuGraphAddMemsetNode
- cuda.cuda.cuGraphExternalSemaphoresWaitNodeGetParams(hNode)¶
Returns an external semaphore wait node’s parameters.
Returns the parameters of an external semaphore wait node hNode in params_out. The extSemArray and paramsArray returned in params_out, are owned by the node. This memory remains valid until the node is destroyed or its parameters are modified, and should not be modified directly. Use cuGraphExternalSemaphoresSignalNodeSetParams to update the parameters of this node.
- Parameters
- hNodeCUgraphNode or cudaGraphNode_t
Node to get the parameters for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- params_outCUDA_EXT_SEM_WAIT_NODE_PARAMS
Pointer to return the parameters
- cuda.cuda.cuGraphExternalSemaphoresWaitNodeSetParams(hNode, CUDA_EXT_SEM_WAIT_NODE_PARAMS nodeParams: CUDA_EXT_SEM_WAIT_NODE_PARAMS)¶
Sets an external semaphore wait node’s parameters.
Sets the parameters of an external semaphore wait node hNode to nodeParams.
- Parameters
- hNodeCUgraphNode or cudaGraphNode_t
Node to set the parameters for
- nodeParamsCUDA_EXT_SEM_WAIT_NODE_PARAMS
Parameters to copy
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_OUT_OF_MEMORY
- None
None
- cuda.cuda.cuGraphAddMemAllocNode(hGraph, dependencies: List[CUgraphNode], size_t numDependencies, CUDA_MEM_ALLOC_NODE_PARAMS nodeParams: CUDA_MEM_ALLOC_NODE_PARAMS)¶
Creates an allocation node and adds it to a graph.
Creates a new allocation node and adds it to hGraph with numDependencies dependencies specified via dependencies and arguments specified in nodeParams. It is possible for numDependencies to be 0, in which case the node will be placed at the root of the graph. dependencies may not have any duplicate entries. A handle to the new node will be returned in phGraphNode.
If the allocation is freed in the same graph, by creating a free node using cuGraphAddMemFreeNode, the allocation can be accessed by nodes ordered after the allocation node but before the free node. These allocations cannot be freed outside the owning graph, and they can only be freed once in the owning graph.
If the allocation is not freed in the same graph, then it can be accessed not only by nodes in the graph which are ordered after the allocation node, but also by stream operations ordered after the graph’s execution but before the allocation is freed.
Allocations which are not freed in the same graph can be freed by: - passing the allocation to cuMemFreeAsync or cuMemFree; - launching a graph with a free node for that allocation; or - specifying CUDA_GRAPH_INSTANTIATE_FLAG_AUTO_FREE_ON_LAUNCH during instantiation, which makes each launch behave as though it called cuMemFreeAsync for every unfreed allocation.
It is not possible to free an allocation in both the owning graph and another graph. If the allocation is freed in the same graph, a free node cannot be added to another graph. If the allocation is freed in another graph, a free node can no longer be added to the owning graph.
The following restrictions apply to graphs which contain allocation and/or memory free nodes: - Nodes and edges of the graph cannot be deleted. - The graph cannot be used in a child node. - Only one instantiation of the graph may exist at any point in time. - The graph cannot be cloned.
- Parameters
- hGraphAny
Graph to which to add the node
- dependenciesList[CUgraphNode]
Dependencies of the node
- numDependenciessize_t
Number of dependencies
- nodeParamsCUDA_MEM_ALLOC_NODE_PARAMS
Parameters for the node
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_NOT_SUPPORTED CUDA_ERROR_INVALID_VALUE
- phGraphNodeCUgraphNode
Returns newly created node
See also
cuGraphAddMemFreeNode
cuGraphMemAllocNodeGetParams
cuDeviceGraphMemTrim
cuDeviceGetGraphMemAttribute
cuDeviceSetGraphMemAttribute
cuMemAllocAsync
cuMemFreeAsync
cuGraphCreate
cuGraphDestroyNode
cuGraphAddChildGraphNode
cuGraphAddEmptyNode
cuGraphAddEventRecordNode
cuGraphAddEventWaitNode
cuGraphAddExternalSemaphoresSignalNode
cuGraphAddExternalSemaphoresWaitNode
cuGraphAddKernelNode
cuGraphAddMemcpyNode
cuGraphAddMemsetNode
- cuda.cuda.cuGraphMemAllocNodeGetParams(hNode)¶
Returns a memory alloc node’s parameters.
Returns the parameters of a memory alloc node hNode in params_out. The poolProps and accessDescs returned in params_out, are owned by the node. This memory remains valid until the node is destroyed. The returned parameters must not be modified.
- Parameters
- hNodeCUgraphNode or cudaGraphNode_t
Node to get the parameters for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- params_outCUDA_MEM_ALLOC_NODE_PARAMS
Pointer to return the parameters
- cuda.cuda.cuGraphAddMemFreeNode(hGraph, dependencies: List[CUgraphNode], size_t numDependencies, dptr)¶
Creates a memory free node and adds it to a graph.
Creates a new memory free node and adds it to hGraph with numDependencies dependencies specified via dependencies and arguments specified in nodeParams. It is possible for numDependencies to be 0, in which case the node will be placed at the root of the graph. dependencies may not have any duplicate entries. A handle to the new node will be returned in phGraphNode.
The following restrictions apply to graphs which contain allocation and/or memory free nodes: - Nodes and edges of the graph cannot be deleted. - The graph cannot be used in a child node. - Only one instantiation of the graph may exist at any point in time. - The graph cannot be cloned.
- Parameters
- hGraphAny
Graph to which to add the node
- dependenciesList[CUgraphNode]
Dependencies of the node
- numDependenciessize_t
Number of dependencies
- dptrAny
Address of memory to free
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_NOT_SUPPORTED CUDA_ERROR_INVALID_VALUE
- phGraphNodeCUgraphNode
Returns newly created node
See also
cuGraphAddMemAllocNode
cuGraphMemFreeNodeGetParams
cuDeviceGraphMemTrim
cuDeviceGetGraphMemAttribute
cuDeviceSetGraphMemAttribute
cuMemAllocAsync
cuMemFreeAsync
cuGraphCreate
cuGraphDestroyNode
cuGraphAddChildGraphNode
cuGraphAddEmptyNode
cuGraphAddEventRecordNode
cuGraphAddEventWaitNode
cuGraphAddExternalSemaphoresSignalNode
cuGraphAddExternalSemaphoresWaitNode
cuGraphAddKernelNode
cuGraphAddMemcpyNode
cuGraphAddMemsetNode
- cuda.cuda.cuGraphMemFreeNodeGetParams(hNode)¶
Returns a memory free node’s parameters.
Returns the address of a memory free node hNode in dptr_out.
- Parameters
- hNodeCUgraphNode or cudaGraphNode_t
Node to get the parameters for
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- dptr_outCUdeviceptr
Pointer to return the device address
- cuda.cuda.cuDeviceGraphMemTrim(device)¶
Free unused memory that was cached on the specified device for use with graphs back to the OS.
Blocks which are not in use by a graph that is either currently executing or scheduled to execute are freed back to the operating system.
- Parameters
- deviceAny
The device for which cached memory should be freed.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_DEVICE
- None
None
- cuda.cuda.cuDeviceGetGraphMemAttribute(device, attr: CUgraphMem_attribute)¶
Query asynchronous allocation attributes related to graphs.
Valid attributes are:
CU_GRAPH_MEM_ATTR_USED_MEM_CURRENT: Amount of memory, in bytes,
currently associated with graphs - CU_GRAPH_MEM_ATTR_USED_MEM_HIGH: High watermark of memory, in bytes, associated with graphs since the last time it was reset. High watermark can only be reset to zero. - CU_GRAPH_MEM_ATTR_RESERVED_MEM_CURRENT: Amount of memory, in bytes, currently allocated for use by the CUDA graphs asynchronous allocator. - CU_GRAPH_MEM_ATTR_RESERVED_MEM_HIGH: High watermark of memory, in bytes, currently allocated for use by the CUDA graphs asynchronous allocator.
- Parameters
- deviceAny
Specifies the scope of the query
- attrCUgraphMem_attribute
attribute to get
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_DEVICE
- valueAny
retrieved value
- cuda.cuda.cuDeviceSetGraphMemAttribute(device, attr: CUgraphMem_attribute, value)¶
Set asynchronous allocation attributes related to graphs.
Valid attributes are:
CU_GRAPH_MEM_ATTR_USED_MEM_HIGH: High watermark of memory, in bytes,
associated with graphs since the last time it was reset. High watermark can only be reset to zero. - CU_GRAPH_MEM_ATTR_RESERVED_MEM_HIGH: High watermark of memory, in bytes, currently allocated for use by the CUDA graphs asynchronous allocator.
- Parameters
- deviceAny
Specifies the scope of the query
- attrCUgraphMem_attribute
attribute to get
- valueAny
pointer to value to set
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_DEVICE
- None
None
- cuda.cuda.cuGraphClone(originalGraph)¶
Clones a graph.
This function creates a copy of originalGraph and returns it in phGraphClone. All parameters are copied into the cloned graph. The original graph may be modified after this call without affecting the clone.
Child graph nodes in the original graph are recursively copied into the clone.
- Parameters
- originalGraphCUgraph or cudaGraph_t
Graph to clone
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_OUT_OF_MEMORY
- phGraphCloneCUgraph
Returns newly created cloned graph
See also
- cuda.cuda.cuGraphNodeFindInClone(hOriginalNode, hClonedGraph)¶
Finds a cloned version of a node.
This function returns the node in hClonedGraph corresponding to hOriginalNode in the original graph.
hClonedGraph must have been cloned from hOriginalGraph via cuGraphClone. hOriginalNode must have been in hOriginalGraph at the time of the call to cuGraphClone, and the corresponding cloned node in hClonedGraph must not have been removed. The cloned node is then returned via phClonedNode.
- Parameters
- hOriginalNodeAny
Handle to the original node
- hClonedGraphAny
Cloned graph to query
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- phNodeCUgraphNode
Returns handle to the cloned node
See also
- cuda.cuda.cuGraphNodeGetType(hNode)¶
Returns a node’s type.
Returns the node type of hNode in typename.
- Parameters
- hNodeCUgraphNode or cudaGraphNode_t
Node to query
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- typenameCUgraphNodeType
Pointer to return the node type
- cuda.cuda.cuGraphGetNodes(hGraph, size_t numNodes=0)¶
Returns a graph’s nodes.
Returns a list of hGraph’s nodes. nodes may be NULL, in which case this function will return the number of nodes in numNodes. Otherwise, numNodes entries will be filled in. If numNodes is higher than the actual number of nodes, the remaining entries in nodes will be set to NULL, and the number of nodes actually obtained will be returned in numNodes.
- Parameters
- hGraphAny
Graph to query
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- nodesList[CUgraphNode]
Pointer to return the nodes
- numNodesint
See description
- cuda.cuda.cuGraphGetRootNodes(hGraph, size_t numRootNodes=0)¶
Returns a graph’s root nodes.
Returns a list of hGraph’s root nodes. rootNodes may be NULL, in which case this function will return the number of root nodes in numRootNodes. Otherwise, numRootNodes entries will be filled in. If numRootNodes is higher than the actual number of root nodes, the remaining entries in rootNodes will be set to NULL, and the number of nodes actually obtained will be returned in numRootNodes.
- Parameters
- hGraphAny
Graph to query
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- rootNodesList[CUgraphNode]
Pointer to return the root nodes
- numRootNodesint
See description
- cuda.cuda.cuGraphGetEdges(hGraph, size_t numEdges=0)¶
Returns a graph’s dependency edges.
Returns a list of hGraph’s dependency edges. Edges are returned via corresponding indices in from and to; that is, the node in to`[i] has a dependency on the node in `from`[i]. `from and to may both be NULL, in which case this function only returns the number of edges in numEdges. Otherwise, numEdges entries will be filled in. If numEdges is higher than the actual number of edges, the remaining entries in from and to will be set to NULL, and the number of edges actually returned will be written to numEdges.
- Parameters
- hGraphAny
Graph to get the edges from
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- fromList[CUgraphNode]
Location to return edge endpoints
- toList[CUgraphNode]
Location to return edge endpoints
- numEdgesint
See description
- cuda.cuda.cuGraphNodeGetDependencies(hNode, size_t numDependencies=0)¶
Returns a node’s dependencies.
Returns a list of node’s dependencies. dependencies may be NULL, in which case this function will return the number of dependencies in numDependencies. Otherwise, numDependencies entries will be filled in. If numDependencies is higher than the actual number of dependencies, the remaining entries in dependencies will be set to NULL, and the number of nodes actually obtained will be returned in numDependencies.
- Parameters
- hNodeCUgraphNode or cudaGraphNode_t
Node to query
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- dependenciesList[CUgraphNode]
Pointer to return the dependencies
- numDependenciesint
See description
- cuda.cuda.cuGraphNodeGetDependentNodes(hNode, size_t numDependentNodes=0)¶
Returns a node’s dependent nodes.
Returns a list of node’s dependent nodes. dependentNodes may be NULL, in which case this function will return the number of dependent nodes in numDependentNodes. Otherwise, numDependentNodes entries will be filled in. If numDependentNodes is higher than the actual number of dependent nodes, the remaining entries in dependentNodes will be set to NULL, and the number of nodes actually obtained will be returned in numDependentNodes.
- Parameters
- hNodeCUgraphNode or cudaGraphNode_t
Node to query
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- dependentNodesList[CUgraphNode]
Pointer to return the dependent nodes
- numDependentNodesint
See description
- cuda.cuda.cuGraphAddDependencies(hGraph, from_: List[CUgraphNode], to: List[CUgraphNode], size_t numDependencies)¶
Adds dependency edges to a graph.
The number of dependencies to be added is defined by numDependencies Elements in from and to at corresponding indices define a dependency. Each node in from and to must belong to hGraph.
If numDependencies is 0, elements in from and to will be ignored. Specifying an existing dependency will return an error.
- Parameters
- hGraphAny
Graph to which dependencies are added
- from_List[CUgraphNode]
Array of nodes that provide the dependencies
- toList[CUgraphNode]
Array of dependent nodes
- numDependenciessize_t
Number of dependencies to be added
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- None
None
- cuda.cuda.cuGraphRemoveDependencies(hGraph, from_: List[CUgraphNode], to: List[CUgraphNode], size_t numDependencies)¶
Removes dependency edges from a graph.
The number of dependencies to be removed is defined by numDependencies. Elements in from and to at corresponding indices define a dependency. Each node in from and to must belong to hGraph.
If numDependencies is 0, elements in from and to will be ignored. Specifying a non-existing dependency will return an error.
Dependencies cannot be removed from graphs which contain allocation or free nodes. Any attempt to do so will return an error.
- Parameters
- hGraphAny
Graph from which to remove dependencies
- from_List[CUgraphNode]
Array of nodes that provide the dependencies
- toList[CUgraphNode]
Array of dependent nodes
- numDependenciessize_t
Number of dependencies to be removed
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- None
None
- cuda.cuda.cuGraphDestroyNode(hNode)¶
Remove a node from the graph.
Removes hNode from its graph. This operation also severs any dependencies of other nodes on hNode and vice versa.
Nodes which belong to a graph which contains allocation or free nodes cannot be destroyed. Any attempt to do so will return an error.
- Parameters
- hNodeCUgraphNode or cudaGraphNode_t
Node to remove
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- None
None
- cuda.cuda.cuGraphInstantiate(hGraph, char *logBuffer, size_t bufferSize)¶
Creates an executable graph from a graph.
Instantiates hGraph as an executable graph. The graph is validated for any structural constraints or intra-node constraints which were not previously validated. If instantiation is successful, a handle to the instantiated graph is returned in phGraphExec.
If there are any errors, diagnostic information may be returned in errorNode and logBuffer. This is the primary way to inspect instantiation errors. The output will be null terminated unless the diagnostics overflow the buffer. In this case, they will be truncated, and the last byte can be inspected to determine if truncation occurred.
- Parameters
- hGraphAny
Graph to instantiate
- logBufferbytes
A character buffer to store diagnostic messages
- bufferSizesize_t
Size of the log buffer in bytes
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- phGraphExecCUgraphExec
Returns instantiated graph
- phErrorNodeCUgraphNode
In case of an instantiation error, this may be modified to indicate a node contributing to the error
- cuda.cuda.cuGraphInstantiateWithFlags(hGraph, unsigned long long flags)¶
Creates an executable graph from a graph.
Instantiates hGraph as an executable graph. The graph is validated for any structural constraints or intra-node constraints which were not previously validated. If instantiation is successful, a handle to the instantiated graph is returned in phGraphExec.
The flags parameter controls the behavior of instantiation and subsequent graph launches. Valid flags are:
CUDA_GRAPH_INSTANTIATE_FLAG_AUTO_FREE_ON_LAUNCH, which configures a
graph containing memory allocation nodes to automatically free any unfreed memory allocations before the graph is relaunched.
If hGraph contains any allocation or free nodes, there can be at most one executable graph in existence for that graph at a time.
An attempt to instantiate a second executable graph before destroying the first with cuGraphExecDestroy will result in an error.
- Parameters
- hGraphAny
Graph to instantiate
- flagsunsigned long long
Flags to control instantiation. See CUgraphInstantiate_flags.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- phGraphExecCUgraphExec
Returns instantiated graph
- cuda.cuda.cuGraphExecKernelNodeSetParams(hGraphExec, hNode, CUDA_KERNEL_NODE_PARAMS nodeParams: CUDA_KERNEL_NODE_PARAMS)¶
Sets the parameters for a kernel node in the given graphExec.
Sets the parameters of a kernel node in an executable graph hGraphExec. The node is identified by the corresponding node hNode in the non-executable graph, from which the executable graph was instantiated.
hNode must not have been removed from the original graph. All nodeParams fields may change, but the following restrictions apply to func updates:
The owning context of the function cannot change. - A node whose
function originally did not use CUDA dynamic parallelism cannot be updated to a function which uses CDP
The modifications only affect future launches of hGraphExec. Already enqueued or running launches of hGraphExec are not affected by this call. hNode is also not modified by this call.
- Parameters
- hGraphExecCUgraphExec or cudaGraphExec_t
The executable graph in which to set the specified node
- hNodeCUgraphNode or cudaGraphNode_t
kernel node from the graph from which graphExec was instantiated
- nodeParamsCUDA_KERNEL_NODE_PARAMS
Updated Parameters to set
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuGraphAddKernelNode
cuGraphKernelNodeSetParams
cuGraphExecMemcpyNodeSetParams
cuGraphExecMemsetNodeSetParams
cuGraphExecHostNodeSetParams
cuGraphExecChildGraphNodeSetParams
cuGraphExecEventRecordNodeSetEvent
cuGraphExecEventWaitNodeSetEvent
cuGraphExecExternalSemaphoresSignalNodeSetParams
cuGraphExecExternalSemaphoresWaitNodeSetParams
cuGraphExecUpdate
cuGraphInstantiate
- cuda.cuda.cuGraphExecMemcpyNodeSetParams(hGraphExec, hNode, CUDA_MEMCPY3D copyParams: CUDA_MEMCPY3D, ctx)¶
Sets the parameters for a memcpy node in the given graphExec.
Updates the work represented by hNode in hGraphExec as though hNode had contained copyParams at instantiation. hNode must remain in the graph which was used to instantiate hGraphExec. Changed edges to and from hNode are ignored.
The source and destination memory in copyParams must be allocated from the same contexts as the original source and destination memory. Both the instantiation-time memory operands and the memory operands in copyParams must be 1-dimensional. Zero-length operations are not supported.
The modifications only affect future launches of hGraphExec. Already enqueued or running launches of hGraphExec are not affected by this call. hNode is also not modified by this call.
Returns CUDA_ERROR_INVALID_VALUE if the memory operands’ mappings changed or either the original or new memory operands are multidimensional.
- Parameters
- hGraphExecCUgraphExec or cudaGraphExec_t
The executable graph in which to set the specified node
- hNodeCUgraphNode or cudaGraphNode_t
Memcpy node from the graph which was used to instantiate graphExec
- copyParamsCUDA_MEMCPY3D
The updated parameters to set
- ctxAny
Context on which to run the node
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuGraphAddMemcpyNode
cuGraphMemcpyNodeSetParams
cuGraphExecKernelNodeSetParams
cuGraphExecMemsetNodeSetParams
cuGraphExecHostNodeSetParams
cuGraphExecChildGraphNodeSetParams
cuGraphExecEventRecordNodeSetEvent
cuGraphExecEventWaitNodeSetEvent
cuGraphExecExternalSemaphoresSignalNodeSetParams
cuGraphExecExternalSemaphoresWaitNodeSetParams
cuGraphExecUpdate
cuGraphInstantiate
- cuda.cuda.cuGraphExecMemsetNodeSetParams(hGraphExec, hNode, CUDA_MEMSET_NODE_PARAMS memsetParams: CUDA_MEMSET_NODE_PARAMS, ctx)¶
Sets the parameters for a memset node in the given graphExec.
Updates the work represented by hNode in hGraphExec as though hNode had contained memsetParams at instantiation. hNode must remain in the graph which was used to instantiate hGraphExec. Changed edges to and from hNode are ignored.
The destination memory in memsetParams must be allocated from the same contexts as the original destination memory. Both the instantiation-time memory operand and the memory operand in memsetParams must be 1-dimensional. Zero-length operations are not supported.
The modifications only affect future launches of hGraphExec. Already enqueued or running launches of hGraphExec are not affected by this call. hNode is also not modified by this call.
Returns CUDA_ERROR_INVALID_VALUE if the memory operand’s mappings changed or either the original or new memory operand are multidimensional.
- Parameters
- hGraphExecCUgraphExec or cudaGraphExec_t
The executable graph in which to set the specified node
- hNodeCUgraphNode or cudaGraphNode_t
Memset node from the graph which was used to instantiate graphExec
- memsetParamsCUDA_MEMSET_NODE_PARAMS
The updated parameters to set
- ctxAny
Context on which to run the node
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuGraphAddMemsetNode
cuGraphMemsetNodeSetParams
cuGraphExecKernelNodeSetParams
cuGraphExecMemcpyNodeSetParams
cuGraphExecHostNodeSetParams
cuGraphExecChildGraphNodeSetParams
cuGraphExecEventRecordNodeSetEvent
cuGraphExecEventWaitNodeSetEvent
cuGraphExecExternalSemaphoresSignalNodeSetParams
cuGraphExecExternalSemaphoresWaitNodeSetParams
cuGraphExecUpdate
cuGraphInstantiate
- cuda.cuda.cuGraphExecHostNodeSetParams(hGraphExec, hNode, CUDA_HOST_NODE_PARAMS nodeParams: CUDA_HOST_NODE_PARAMS)¶
Sets the parameters for a host node in the given graphExec.
Updates the work represented by hNode in hGraphExec as though hNode had contained nodeParams at instantiation. hNode must remain in the graph which was used to instantiate hGraphExec. Changed edges to and from hNode are ignored.
The modifications only affect future launches of hGraphExec. Already enqueued or running launches of hGraphExec are not affected by this call. hNode is also not modified by this call.
- Parameters
- hGraphExecCUgraphExec or cudaGraphExec_t
The executable graph in which to set the specified node
- hNodeCUgraphNode or cudaGraphNode_t
Host node from the graph which was used to instantiate graphExec
- nodeParamsCUDA_HOST_NODE_PARAMS
The updated parameters to set
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuGraphAddHostNode
cuGraphHostNodeSetParams
cuGraphExecKernelNodeSetParams
cuGraphExecMemcpyNodeSetParams
cuGraphExecMemsetNodeSetParams
cuGraphExecChildGraphNodeSetParams
cuGraphExecEventRecordNodeSetEvent
cuGraphExecEventWaitNodeSetEvent
cuGraphExecExternalSemaphoresSignalNodeSetParams
cuGraphExecExternalSemaphoresWaitNodeSetParams
cuGraphExecUpdate
cuGraphInstantiate
- cuda.cuda.cuGraphExecChildGraphNodeSetParams(hGraphExec, hNode, childGraph)¶
Updates node parameters in the child graph node in the given graphExec.
Updates the work represented by hNode in hGraphExec as though the nodes contained in hNode’s graph had the parameters contained in childGraph’s nodes at instantiation. hNode must remain in the graph which was used to instantiate hGraphExec. Changed edges to and from hNode are ignored.
The modifications only affect future launches of hGraphExec. Already enqueued or running launches of hGraphExec are not affected by this call. hNode is also not modified by this call.
The topology of childGraph, as well as the node insertion order, must match that of the graph contained in hNode. See cuGraphExecUpdate() for a list of restrictions on what can be updated in an instantiated graph. The update is recursive, so child graph nodes contained within the top level child graph will also be updated.
- Parameters
- hGraphExecCUgraphExec or cudaGraphExec_t
The executable graph in which to set the specified node
- hNodeCUgraphNode or cudaGraphNode_t
Host node from the graph which was used to instantiate graphExec
- childGraphCUgraph or cudaGraph_t
The graph supplying the updated parameters
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuGraphAddChildGraphNode
cuGraphChildGraphNodeGetGraph
cuGraphExecKernelNodeSetParams
cuGraphExecMemcpyNodeSetParams
cuGraphExecMemsetNodeSetParams
cuGraphExecHostNodeSetParams
cuGraphExecEventRecordNodeSetEvent
cuGraphExecEventWaitNodeSetEvent
cuGraphExecExternalSemaphoresSignalNodeSetParams
cuGraphExecExternalSemaphoresWaitNodeSetParams
cuGraphExecUpdate
cuGraphInstantiate
- cuda.cuda.cuGraphExecEventRecordNodeSetEvent(hGraphExec, hNode, event)¶
Sets the event for an event record node in the given graphExec.
Sets the event of an event record node in an executable graph hGraphExec. The node is identified by the corresponding node hNode in the non-executable graph, from which the executable graph was instantiated.
The modifications only affect future launches of hGraphExec. Already enqueued or running launches of hGraphExec are not affected by this call. hNode is also not modified by this call.
- Parameters
- hGraphExecCUgraphExec or cudaGraphExec_t
The executable graph in which to set the specified node
- hNodeCUgraphNode or cudaGraphNode_t
event record node from the graph from which graphExec was instantiated
- eventCUevent or cudaEvent_t
Updated event to use
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuGraphAddEventRecordNode
cuGraphEventRecordNodeGetEvent
cuGraphEventWaitNodeSetEvent
cuEventRecordWithFlags
cuStreamWaitEvent
cuGraphExecKernelNodeSetParams
cuGraphExecMemcpyNodeSetParams
cuGraphExecMemsetNodeSetParams
cuGraphExecHostNodeSetParams
cuGraphExecChildGraphNodeSetParams
cuGraphExecEventWaitNodeSetEvent
cuGraphExecExternalSemaphoresSignalNodeSetParams
cuGraphExecExternalSemaphoresWaitNodeSetParams
cuGraphExecUpdate
cuGraphInstantiate
- cuda.cuda.cuGraphExecEventWaitNodeSetEvent(hGraphExec, hNode, event)¶
Sets the event for an event wait node in the given graphExec.
Sets the event of an event wait node in an executable graph hGraphExec. The node is identified by the corresponding node hNode in the non-executable graph, from which the executable graph was instantiated.
The modifications only affect future launches of hGraphExec. Already enqueued or running launches of hGraphExec are not affected by this call. hNode is also not modified by this call.
- Parameters
- hGraphExecCUgraphExec or cudaGraphExec_t
The executable graph in which to set the specified node
- hNodeCUgraphNode or cudaGraphNode_t
event wait node from the graph from which graphExec was instantiated
- eventCUevent or cudaEvent_t
Updated event to use
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuGraphAddEventWaitNode
cuGraphEventWaitNodeGetEvent
cuGraphEventRecordNodeSetEvent
cuEventRecordWithFlags
cuStreamWaitEvent
cuGraphExecKernelNodeSetParams
cuGraphExecMemcpyNodeSetParams
cuGraphExecMemsetNodeSetParams
cuGraphExecHostNodeSetParams
cuGraphExecChildGraphNodeSetParams
cuGraphExecEventRecordNodeSetEvent
cuGraphExecExternalSemaphoresSignalNodeSetParams
cuGraphExecExternalSemaphoresWaitNodeSetParams
cuGraphExecUpdate
cuGraphInstantiate
- cuda.cuda.cuGraphExecExternalSemaphoresSignalNodeSetParams(hGraphExec, hNode, CUDA_EXT_SEM_SIGNAL_NODE_PARAMS nodeParams: CUDA_EXT_SEM_SIGNAL_NODE_PARAMS)¶
Sets the parameters for an external semaphore signal node in the given graphExec.
Sets the parameters of an external semaphore signal node in an executable graph hGraphExec. The node is identified by the corresponding node hNode in the non-executable graph, from which the executable graph was instantiated.
hNode must not have been removed from the original graph.
The modifications only affect future launches of hGraphExec. Already enqueued or running launches of hGraphExec are not affected by this call. hNode is also not modified by this call.
Changing nodeParams->numExtSems is not supported.
- Parameters
- hGraphExecCUgraphExec or cudaGraphExec_t
The executable graph in which to set the specified node
- hNodeCUgraphNode or cudaGraphNode_t
semaphore signal node from the graph from which graphExec was instantiated
- nodeParamsCUDA_EXT_SEM_SIGNAL_NODE_PARAMS
Updated Parameters to set
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuGraphAddExternalSemaphoresSignalNode
cuImportExternalSemaphore
cuSignalExternalSemaphoresAsync
cuWaitExternalSemaphoresAsync
cuGraphExecKernelNodeSetParams
cuGraphExecMemcpyNodeSetParams
cuGraphExecMemsetNodeSetParams
cuGraphExecHostNodeSetParams
cuGraphExecChildGraphNodeSetParams
cuGraphExecEventRecordNodeSetEvent
cuGraphExecEventWaitNodeSetEvent
cuGraphExecExternalSemaphoresWaitNodeSetParams
cuGraphExecUpdate
cuGraphInstantiate
- cuda.cuda.cuGraphExecExternalSemaphoresWaitNodeSetParams(hGraphExec, hNode, CUDA_EXT_SEM_WAIT_NODE_PARAMS nodeParams: CUDA_EXT_SEM_WAIT_NODE_PARAMS)¶
Sets the parameters for an external semaphore wait node in the given graphExec.
Sets the parameters of an external semaphore wait node in an executable graph hGraphExec. The node is identified by the corresponding node hNode in the non-executable graph, from which the executable graph was instantiated.
hNode must not have been removed from the original graph.
The modifications only affect future launches of hGraphExec. Already enqueued or running launches of hGraphExec are not affected by this call. hNode is also not modified by this call.
Changing nodeParams->numExtSems is not supported.
- Parameters
- hGraphExecCUgraphExec or cudaGraphExec_t
The executable graph in which to set the specified node
- hNodeCUgraphNode or cudaGraphNode_t
semaphore wait node from the graph from which graphExec was instantiated
- nodeParamsCUDA_EXT_SEM_WAIT_NODE_PARAMS
Updated Parameters to set
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuGraphAddExternalSemaphoresWaitNode
cuImportExternalSemaphore
cuSignalExternalSemaphoresAsync
cuWaitExternalSemaphoresAsync
cuGraphExecKernelNodeSetParams
cuGraphExecMemcpyNodeSetParams
cuGraphExecMemsetNodeSetParams
cuGraphExecHostNodeSetParams
cuGraphExecChildGraphNodeSetParams
cuGraphExecEventRecordNodeSetEvent
cuGraphExecEventWaitNodeSetEvent
cuGraphExecExternalSemaphoresSignalNodeSetParams
cuGraphExecUpdate
cuGraphInstantiate
- cuda.cuda.cuGraphNodeSetEnabled(hGraphExec, hNode, unsigned int isEnabled)¶
Enables or disables the specified node in the given graphExec.
Sets hNode to be either enabled or disabled. Disabled nodes are functionally equivalent to empty nodes until they are reenabled. Existing node parameters are not affected by disabling/enabling the node.
The node is identified by the corresponding node hNode in the non- executable graph, from which the executable graph was instantiated.
hNode must not have been removed from the original graph.
The modifications only affect future launches of hGraphExec. Already enqueued or running launches of hGraphExec are not affected by this call. hNode is also not modified by this call.
- Parameters
- hGraphExecCUgraphExec or cudaGraphExec_t
The executable graph in which to set the specified node
- hNodeCUgraphNode or cudaGraphNode_t
Node from the graph from which graphExec was instantiated
- isEnabledunsigned int
Node is enabled if != 0, otherwise the node is disabled
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- None
None
Notes
Currently only kernel nodes are supported.
- cuda.cuda.cuGraphNodeGetEnabled(hGraphExec, hNode)¶
Query whether a node in the given graphExec is enabled.
Sets isEnabled to 1 if hNode is enabled, or 0 if hNode is disabled.
The node is identified by the corresponding node hNode in the non- executable graph, from which the executable graph was instantiated.
hNode must not have been removed from the original graph.
- Parameters
- hGraphExecCUgraphExec or cudaGraphExec_t
The executable graph in which to set the specified node
- hNodeCUgraphNode or cudaGraphNode_t
Node from the graph from which graphExec was instantiated
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- isEnabledunsigned int
Location to return the enabled status of the node
Notes
Currently only kernel nodes are supported.
- cuda.cuda.cuGraphUpload(hGraphExec, hStream)¶
Uploads an executable graph in a stream.
Uploads hGraphExec to the device in hStream without executing it. Uploads of the same hGraphExec will be serialized. Each upload is ordered behind both any previous work in hStream and any previous launches of hGraphExec. Uses memory cached by stream to back the allocations owned by hGraphExec.
- Parameters
- hGraphExecCUgraphExec or cudaGraphExec_t
Executable graph to upload
- hStreamCUstream or cudaStream_t
Stream in which to upload the graph
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- None
None
- cuda.cuda.cuGraphLaunch(hGraphExec, hStream)¶
Launches an executable graph in a stream.
Executes hGraphExec in hStream. Only one instance of hGraphExec may be executing at a time. Each launch is ordered behind both any previous work in hStream and any previous launches of hGraphExec. To execute a graph concurrently, it must be instantiated multiple times into multiple executable graphs.
If any allocations created by hGraphExec remain unfreed (from a previous launch) and hGraphExec was not instantiated with CUDA_GRAPH_INSTANTIATE_FLAG_AUTO_FREE_ON_LAUNCH, the launch will fail with CUDA_ERROR_INVALID_VALUE.
- Parameters
- hGraphExecCUgraphExec or cudaGraphExec_t
Executable graph to launch
- hStreamCUstream or cudaStream_t
Stream in which to launch the graph
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- None
None
- cuda.cuda.cuGraphExecDestroy(hGraphExec)¶
Destroys an executable graph.
Destroys the executable graph specified by hGraphExec, as well as all of its executable nodes. If the executable graph is in-flight, it will not be terminated, but rather freed asynchronously on completion.
- Parameters
- hGraphExecCUgraphExec or cudaGraphExec_t
Executable graph to destroy
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- None
None
See also
- cuda.cuda.cuGraphDestroy(hGraph)¶
Destroys a graph.
Destroys the graph specified by hGraph, as well as all of its nodes.
- Parameters
- hGraphAny
Graph to destroy
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_VALUE
- None
None
See also
- cuda.cuda.cuGraphExecUpdate(hGraphExec, hGraph)¶
Check whether an executable graph can be updated with a graph and perform the update if possible.
Updates the node parameters in the instantiated graph specified by hGraphExec with the node parameters in a topologically identical graph specified by hGraph.
Limitations:
Kernel nodes: - The owning context of the function cannot change.
A node whose function originally did not use CUDA dynamic parallelism
cannot be updated to a function which uses CDP. - A cooperative node cannot be updated to a non-cooperative node, and vice-versa. - Memset and memcpy nodes: - The CUDA device(s) to which the operand(s) was allocated/mapped cannot change. - The source/destination memory must be allocated from the same contexts as the original source/destination memory. - Only 1D memsets can be changed. - Additional memcpy node restrictions: - Changing either the source or destination memory type(i.e. CU_MEMORYTYPE_DEVICE, CU_MEMORYTYPE_ARRAY, etc.) is not supported. - External semaphore wait nodes and record nodes: - Changing the number of semaphores is not supported.
Note: The API may add further restrictions in future releases. The return code should always be checked.
cuGraphExecUpdate sets updateResult_out to CU_GRAPH_EXEC_UPDATE_ERROR_TOPOLOGY_CHANGED under the following conditions:
The count of nodes directly in hGraphExec and hGraph differ, in
which case hErrorNode_out is NULL. - A node is deleted in hGraph but not not its pair from hGraphExec, in which case hErrorNode_out is NULL. - A node is deleted in hGraphExec but not its pair from hGraph, in which case hErrorNode_out is the pairless node from hGraph. - The dependent nodes of a pair differ, in which case hErrorNode_out is the node from hGraph.
cuGraphExecUpdate sets updateResult_out to: - CU_GRAPH_EXEC_UPDATE_ERROR if passed an invalid value. - CU_GRAPH_EXEC_UPDATE_ERROR_TOPOLOGY_CHANGED if the graph topology changed - CU_GRAPH_EXEC_UPDATE_ERROR_NODE_TYPE_CHANGED if the type of a node changed, in which case hErrorNode_out is set to the node from hGraph. - CU_GRAPH_EXEC_UPDATE_ERROR_UNSUPPORTED_FUNCTION_CHANGE if the function changed in an unsupported way(see note above), in which case hErrorNode_out is set to the node from hGraph - CU_GRAPH_EXEC_UPDATE_ERROR_PARAMETERS_CHANGED if any parameters to a node changed in a way that is not supported, in which case hErrorNode_out is set to the node from hGraph. - CU_GRAPH_EXEC_UPDATE_ERROR_ATTRIBUTES_CHANGED if any attributes of a node changed in a way that is not supported, in which case hErrorNode_out is set to the node from hGraph. - CU_GRAPH_EXEC_UPDATE_ERROR_NOT_SUPPORTED if something about a node is unsupported, like the node’s type or configuration, in which case hErrorNode_out is set to the node from hGraph
If updateResult_out isn’t set in one of the situations described above, the update check passes and cuGraphExecUpdate updates hGraphExec to match the contents of hGraph. If an error happens during the update, updateResult_out will be set to CU_GRAPH_EXEC_UPDATE_ERROR; otherwise, updateResult_out is set to CU_GRAPH_EXEC_UPDATE_SUCCESS.
cuGraphExecUpdate returns CUDA_SUCCESS when the updated was performed successfully. It returns CUDA_ERROR_GRAPH_EXEC_UPDATE_FAILURE if the graph update was not performed because it included changes which violated constraints specific to instantiated graph update.
- Parameters
- hGraphExecCUgraphExec or cudaGraphExec_t
The instantiated graph to be updated
- hGraphAny
The graph containing the updated parameters
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_GRAPH_EXEC_UPDATE_FAILURE
- hErrorNode_outCUgraphNode
The node which caused the permissibility check to forbid the update, if any
- updateResult_outCUgraphExecUpdateResult
Whether the graph update was permitted. If was forbidden, the reason why
See also
- cuda.cuda.cuGraphKernelNodeCopyAttributes(dst, src)¶
Copies attributes from source node to destination node.
Copies attributes from source node src to destination node dst. Both node must have the same context.
- Parameters
- dstAny
Destination node
- srcAny
Source node For list of attributes see CUkernelNodeAttrID
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- None
None
- cuda.cuda.cuGraphKernelNodeGetAttribute(hNode, attr: CUkernelNodeAttrID)¶
Queries node attribute.
Queries attribute attr from node hNode and stores it in corresponding member of value_out.
- Parameters
- hNodeCUgraphNode or cudaGraphNode_t
- attrCUkernelNodeAttrID
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE
- value_outCUkernelNodeAttrValue
- cuda.cuda.cuGraphKernelNodeSetAttribute(hNode, attr: CUkernelNodeAttrID, CUkernelNodeAttrValue value: CUkernelNodeAttrValue)¶
Sets node attribute.
Sets attribute attr on node hNode from corresponding attribute of value.
- Parameters
- hNodeCUgraphNode or cudaGraphNode_t
- attrCUkernelNodeAttrID
- valueCUkernelNodeAttrValue
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE
- None
None
- cuda.cuda.cuGraphDebugDotPrint(hGraph, char *path, unsigned int flags)¶
Write a DOT file describing graph structure.
Using the provided hGraph, write to path a DOT formatted description of the graph. By default this includes the graph topology, node types, node id, kernel names and memcpy direction. flags can be specified to write more detailed information about each node type such as parameter values, kernel attributes, node and function handles.
- Parameters
- hGraphAny
The graph to create a DOT file from
- pathbytes
The path to write the DOT file to
- flagsunsigned int
Flags from CUgraphDebugDot_flags for specifying which additional node information to write
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_OPERATING_SYSTEM
- None
None
- cuda.cuda.cuUserObjectCreate(ptr, destroy, unsigned int initialRefcount, unsigned int flags)¶
Create a user object.
Create a user object with the specified destructor callback and initial reference count. The initial references are owned by the caller.
Destructor callbacks cannot make CUDA API calls and should avoid blocking behavior, as they are executed by a shared internal thread. Another thread may be signaled to perform such actions, if it does not block forward progress of tasks scheduled through CUDA.
See CUDA User Objects in the CUDA C++ Programming Guide for more information on user objects.
- Parameters
- ptrAny
The pointer to pass to the destroy function
- destroyAny
Callback to free the user object when it is no longer in use
- initialRefcountunsigned int
The initial refcount to create the object with, typically 1. The initial references are owned by the calling thread.
- flagsunsigned int
Currently it is required to pass CU_USER_OBJECT_NO_DESTRUCTOR_SYNC, which is the only defined flag. This indicates that the destroy callback cannot be waited on by any CUDA API. Users requiring synchronization of the callback should signal its completion manually.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- object_outCUuserObject
Location to return the user object handle
- cuda.cuda.cuUserObjectRetain(object, unsigned int count)¶
Retain a reference to a user object.
Retains new references to a user object. The new references are owned by the caller.
See CUDA User Objects in the CUDA C++ Programming Guide for more information on user objects.
- Parameters
- objectAny
The object to retain
- countunsigned int
The number of references to retain, typically 1. Must be nonzero and not larger than INT_MAX.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- None
None
- cuda.cuda.cuUserObjectRelease(object, unsigned int count)¶
Release a reference to a user object.
Releases user object references owned by the caller. The object’s destructor is invoked if the reference count reaches zero.
It is undefined behavior to release references not owned by the caller, or to use a user object handle after all references are released.
See CUDA User Objects in the CUDA C++ Programming Guide for more information on user objects.
- Parameters
- objectAny
The object to release
- countunsigned int
The number of references to release, typically 1. Must be nonzero and not larger than INT_MAX.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- None
None
- cuda.cuda.cuGraphRetainUserObject(graph, object, unsigned int count, unsigned int flags)¶
Retain a reference to a user object from a graph.
Creates or moves user object references that will be owned by a CUDA graph.
See CUDA User Objects in the CUDA C++ Programming Guide for more information on user objects.
- Parameters
- graphCUgraph or cudaGraph_t
The graph to associate the reference with
- objectAny
The user object to retain a reference for
- countunsigned int
The number of references to add to the graph, typically 1. Must be nonzero and not larger than INT_MAX.
- flagsunsigned int
The optional flag CU_GRAPH_USER_OBJECT_MOVE transfers references from the calling thread, rather than create new references. Pass 0 to create new references.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- None
None
- cuda.cuda.cuGraphReleaseUserObject(graph, object, unsigned int count)¶
Release a user object reference from a graph.
Releases user object references owned by a graph.
See CUDA User Objects in the CUDA C++ Programming Guide for more information on user objects.
- Parameters
- graphCUgraph or cudaGraph_t
The graph that will release the reference
- objectAny
The user object to release a reference for
- countunsigned int
The number of references to release, typically 1. Must be nonzero and not larger than INT_MAX.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE
- None
None
Occupancy¶
This section describes the occupancy calculation functions of the low-level CUDA driver application programming interface.
- cuda.cuda.cuOccupancyMaxActiveBlocksPerMultiprocessor(func, int blockSize, size_t dynamicSMemSize)¶
Returns occupancy of a function.
Returns in *numBlocks the number of the maximum active blocks per streaming multiprocessor.
- Parameters
- funcAny
Kernel for which occupancy is calculated
- blockSizeint
Block size the kernel is intended to be launched with
- dynamicSMemSizesize_t
Per-block dynamic shared memory usage intended, in bytes
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_UNKNOWN
- numBlocksint
Returned occupancy
- cuda.cuda.cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags(func, int blockSize, size_t dynamicSMemSize, unsigned int flags)¶
Returns occupancy of a function.
Returns in *numBlocks the number of the maximum active blocks per streaming multiprocessor.
The Flags parameter controls how special cases are handled. The valid flags are:
CU_OCCUPANCY_DEFAULT, which maintains the default behavior as
cuOccupancyMaxActiveBlocksPerMultiprocessor; - CU_OCCUPANCY_DISABLE_CACHING_OVERRIDE, which suppresses the default behavior on platform where global caching affects occupancy. On such platforms, if caching is enabled, but per-block SM resource usage would result in zero occupancy, the occupancy calculator will calculate the occupancy as if caching is disabled. Setting CU_OCCUPANCY_DISABLE_CACHING_OVERRIDE makes the occupancy calculator to return 0 in such cases. More information can be found about this feature in the “Unified L1/Texture Cache” section of the Maxwell tuning guide.
- Parameters
- funcAny
Kernel for which occupancy is calculated
- blockSizeint
Block size the kernel is intended to be launched with
- dynamicSMemSizesize_t
Per-block dynamic shared memory usage intended, in bytes
- flagsunsigned int
Requested behavior for the occupancy calculator
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_UNKNOWN
- numBlocksint
Returned occupancy
- cuda.cuda.cuOccupancyMaxPotentialBlockSize(func, blockSizeToDynamicSMemSize, size_t dynamicSMemSize, int blockSizeLimit)¶
Suggest a launch configuration with reasonable occupancy.
Returns in *blockSize a reasonable block size that can achieve the maximum occupancy (or, the maximum number of active warps with the fewest blocks per multiprocessor), and in *minGridSize the minimum grid size to achieve the maximum occupancy.
If blockSizeLimit is 0, the configurator will use the maximum block size permitted by the device / function instead.
If per-block dynamic shared memory allocation is not needed, the user should leave both blockSizeToDynamicSMemSize and dynamicSMemSize as 0.
If per-block dynamic shared memory allocation is needed, then if the dynamic shared memory size is constant regardless of block size, the size should be passed through dynamicSMemSize, and blockSizeToDynamicSMemSize should be NULL.
Otherwise, if the per-block dynamic shared memory size varies with different block sizes, the user needs to provide a unary function through blockSizeToDynamicSMemSize that computes the dynamic shared memory needed by func for any given block size. dynamicSMemSize is ignored. An example signature is:
//Takeblocksize,returnsdynamicsharedmemoryneeded size_tblockToSmem(intblockSize);
- Parameters
- funcAny
Kernel for which launch configuration is calculated
- blockSizeToDynamicSMemSizeAny
A function that calculates how much per-block dynamic shared memory func uses based on the block size
- dynamicSMemSizesize_t
Dynamic shared memory usage intended, in bytes
- blockSizeLimitint
The maximum block size func is designed to handle
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_UNKNOWN
- minGridSizeint
Returned minimum grid size needed to achieve the maximum occupancy
- blockSizeint
Returned maximum block size that can achieve the maximum occupancy
- cuda.cuda.cuOccupancyMaxPotentialBlockSizeWithFlags(func, blockSizeToDynamicSMemSize, size_t dynamicSMemSize, int blockSizeLimit, unsigned int flags)¶
Suggest a launch configuration with reasonable occupancy.
An extended version of cuOccupancyMaxPotentialBlockSize. In addition to arguments passed to cuOccupancyMaxPotentialBlockSize, cuOccupancyMaxPotentialBlockSizeWithFlags also takes a Flags parameter.
The Flags parameter controls how special cases are handled. The valid flags are:
CU_OCCUPANCY_DEFAULT, which maintains the default behavior as
cuOccupancyMaxPotentialBlockSize; - CU_OCCUPANCY_DISABLE_CACHING_OVERRIDE, which suppresses the default behavior on platform where global caching affects occupancy. On such platforms, the launch configurations that produces maximal occupancy might not support global caching. Setting CU_OCCUPANCY_DISABLE_CACHING_OVERRIDE guarantees that the the produced launch configuration is global caching compatible at a potential cost of occupancy. More information can be found about this feature in the “Unified L1/Texture Cache” section of the Maxwell tuning guide.
- Parameters
- funcAny
Kernel for which launch configuration is calculated
- blockSizeToDynamicSMemSizeAny
A function that calculates how much per-block dynamic shared memory func uses based on the block size
- dynamicSMemSizesize_t
Dynamic shared memory usage intended, in bytes
- blockSizeLimitint
The maximum block size func is designed to handle
- flagsunsigned int
Options
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_UNKNOWN
- minGridSizeint
Returned minimum grid size needed to achieve the maximum occupancy
- blockSizeint
Returned maximum block size that can achieve the maximum occupancy
- cuda.cuda.cuOccupancyAvailableDynamicSMemPerBlock(func, int numBlocks, int blockSize)¶
Returns dynamic shared memory available per block when launching numBlocks blocks on SM.
Returns in *dynamicSmemSize the maximum size of dynamic shared memory to allow numBlocks blocks per SM.
- Parameters
- funcAny
Kernel function for which occupancy is calculated
- numBlocksint
Number of blocks to fit on SM
- blockSizeint
Size of the blocks
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_UNKNOWN
- dynamicSmemSizeint
Returned maximum dynamic shared memory
Texture Object Management¶
This section describes the texture object management functions of the low-level CUDA driver application programming interface. The texture object API is only supported on devices of compute capability 3.0 or higher.
- cuda.cuda.cuTexObjectCreate(CUDA_RESOURCE_DESC pResDesc: CUDA_RESOURCE_DESC, CUDA_TEXTURE_DESC pTexDesc: CUDA_TEXTURE_DESC, CUDA_RESOURCE_VIEW_DESC pResViewDesc: CUDA_RESOURCE_VIEW_DESC)¶
Creates a texture object.
Creates a texture object and returns it in pTexObject. pResDesc describes the data to texture from. pTexDesc describes how the data should be sampled. pResViewDesc is an optional argument that specifies an alternate format for the data described by pResDesc, and also describes the subresource region to restrict access to when texturing. pResViewDesc can only be specified if the type of resource is a CUDA array or a CUDA mipmapped array.
Texture objects are only supported on devices of compute capability 3.0 or higher. Additionally, a texture object is an opaque value, and, as such, should only be accessed through CUDA API calls.
The CUDA_RESOURCE_DESC structure is defined as: typedefstructCUDA_RESOURCE_DESC_st { CUresourcetyperesType; union{ struct{ CUarrayhArray; }array; struct{ CUmipmappedArrayhMipmappedArray; }mipmap; struct{ CUdeviceptrdevPtr; CUarray_formatformat; unsignedintnumChannels; size_tsizeInBytes; }linear; struct{ CUdeviceptrdevPtr; CUarray_formatformat; unsignedintnumChannels; size_twidth; size_theight; size_tpitchInBytes; }pitch2D; }res; unsignedintflags; }CUDA_RESOURCE_DESC; where: - CUDA_RESOURCE_DESC::resType specifies the type of resource to texture from. CUresourceType is defined as: typedefenumCUresourcetype_enum{ CU_RESOURCE_TYPE_ARRAY=0x00, CU_RESOURCE_TYPE_MIPMAPPED_ARRAY=0x01, CU_RESOURCE_TYPE_LINEAR=0x02, CU_RESOURCE_TYPE_PITCH2D=0x03 }CUresourcetype;
If CUDA_RESOURCE_DESC::resType is set to CU_RESOURCE_TYPE_ARRAY, CUDA_RESOURCE_DESC::res::array::hArray must be set to a valid CUDA array handle.
If CUDA_RESOURCE_DESC::resType is set to CU_RESOURCE_TYPE_MIPMAPPED_ARRAY, CUDA_RESOURCE_DESC::res::mipmap::hMipmappedArray must be set to a valid CUDA mipmapped array handle.
If CUDA_RESOURCE_DESC::resType is set to CU_RESOURCE_TYPE_LINEAR, CUDA_RESOURCE_DESC::res::linear::devPtr must be set to a valid device pointer, that is aligned to CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT. CUDA_RESOURCE_DESC::res::linear::format and CUDA_RESOURCE_DESC::res::linear::numChannels describe the format of each component and the number of components per array element. CUDA_RESOURCE_DESC::res::linear::sizeInBytes specifies the size of the array in bytes. The total number of elements in the linear address range cannot exceed CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH. The number of elements is computed as (sizeInBytes / (sizeof(format) * numChannels)).
If CUDA_RESOURCE_DESC::resType is set to CU_RESOURCE_TYPE_PITCH2D, CUDA_RESOURCE_DESC::res::pitch2D::devPtr must be set to a valid device pointer, that is aligned to CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT. CUDA_RESOURCE_DESC::res::pitch2D::format and CUDA_RESOURCE_DESC::res::pitch2D::numChannels describe the format of each component and the number of components per array element. CUDA_RESOURCE_DESC::res::pitch2D::width and CUDA_RESOURCE_DESC::res::pitch2D::height specify the width and height of the array in elements, and cannot exceed CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH and CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT respectively. CUDA_RESOURCE_DESC::res::pitch2D::pitchInBytes specifies the pitch between two rows in bytes and has to be aligned to CU_DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT. Pitch cannot exceed CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH.
The CUDA_TEXTURE_DESC struct is defined as typedefstructCUDA_TEXTURE_DESC_st{ CUaddress_modeaddressMode[3]; CUfilter_modefilterMode; unsignedintflags; unsignedintmaxAnisotropy; CUfilter_modemipmapFilterMode; floatmipmapLevelBias; floatminMipmapLevelClamp; floatmaxMipmapLevelClamp; }CUDA_TEXTURE_DESC; where - CUDA_TEXTURE_DESC::addressMode specifies the addressing mode for each dimension of the texture data. CUaddress_mode is defined as: typedefenumCUaddress_mode_enum{ CU_TR_ADDRESS_MODE_WRAP=0, CU_TR_ADDRESS_MODE_CLAMP=1, CU_TR_ADDRESS_MODE_MIRROR=2, CU_TR_ADDRESS_MODE_BORDER=3 }CUaddress_mode; This is ignored if CUDA_RESOURCE_DESC::resType is CU_RESOURCE_TYPE_LINEAR. Also, if the flag, CU_TRSF_NORMALIZED_COORDINATES is not set, the only supported address mode is CU_TR_ADDRESS_MODE_CLAMP. - CUDA_TEXTURE_DESC::filterMode specifies the filtering mode to be used when fetching from the texture. CUfilter_mode is defined as: typedefenumCUfilter_mode_enum{ CU_TR_FILTER_MODE_POINT=0, CU_TR_FILTER_MODE_LINEAR=1 }CUfilter_mode; This is ignored if CUDA_RESOURCE_DESC::resType is CU_RESOURCE_TYPE_LINEAR. - CUDA_TEXTURE_DESC::flags can be any combination of the following: - CU_TRSF_READ_AS_INTEGER, which suppresses the default behavior of having the texture promote integer data to floating point data in the range [0, 1]. Note that texture with 32-bit integer format would not be promoted, regardless of whether or not this flag is specified. - CU_TRSF_NORMALIZED_COORDINATES, which suppresses the default behavior of having the texture coordinates range from [0, Dim) where Dim is the width or height of the CUDA array. Instead, the texture coordinates [0, 1.0) reference the entire breadth of the array dimension; Note that for CUDA mipmapped arrays, this flag has to be set. - CU_TRSF_DISABLE_TRILINEAR_OPTIMIZATION, which disables any trilinear filtering optimizations. Trilinear optimizations improve texture filtering performance by allowing bilinear filtering on textures in scenarios where it can closely approximate the expected results. - CU_TRSF_SEAMLESS_CUBEMAP, which enables seamless cube map filtering. This flag can only be specified if the underlying resource is a CUDA array or a CUDA mipmapped array that was created with the flag CUDA_ARRAY3D_CUBEMAP. When seamless cube map filtering is enabled, texture address modes specified by CUDA_TEXTURE_DESC::addressMode are ignored. Instead, if the CUDA_TEXTURE_DESC::filterMode is set to CU_TR_FILTER_MODE_POINT the address mode CU_TR_ADDRESS_MODE_CLAMP will be applied for all dimensions. If the CUDA_TEXTURE_DESC::filterMode is set to CU_TR_FILTER_MODE_LINEAR seamless cube map filtering will be performed when sampling along the cube face borders. - CUDA_TEXTURE_DESC::maxAnisotropy specifies the maximum anisotropy ratio to be used when doing anisotropic filtering. This value will be clamped to the range [1,16]. - CUDA_TEXTURE_DESC::mipmapFilterMode specifies the filter mode when the calculated mipmap level lies between two defined mipmap levels. - CUDA_TEXTURE_DESC::mipmapLevelBias specifies the offset to be applied to the calculated mipmap level. - CUDA_TEXTURE_DESC::minMipmapLevelClamp specifies the lower end of the mipmap level range to clamp access to. - CUDA_TEXTURE_DESC::maxMipmapLevelClamp specifies the upper end of the mipmap level range to clamp access to.
The CUDA_RESOURCE_VIEW_DESC struct is defined as typedefstructCUDA_RESOURCE_VIEW_DESC_st { CUresourceViewFormatformat; size_twidth; size_theight; size_tdepth; unsignedintfirstMipmapLevel; unsignedintlastMipmapLevel; unsignedintfirstLayer; unsignedintlastLayer; }CUDA_RESOURCE_VIEW_DESC; where: - CUDA_RESOURCE_VIEW_DESC::format specifies how the data contained in the CUDA array or CUDA mipmapped array should be interpreted. Note that this can incur a change in size of the texture data. If the resource view format is a block compressed format, then the underlying CUDA array or CUDA mipmapped array has to have a base of format CU_AD_FORMAT_UNSIGNED_INT32. with 2 or 4 channels, depending on the block compressed format. For ex., BC1 and BC4 require the underlying CUDA array to have a format of CU_AD_FORMAT_UNSIGNED_INT32 with 2 channels. The other BC formats require the underlying resource to have the same base format but with 4 channels. - CUDA_RESOURCE_VIEW_DESC::width specifies the new width of the texture data. If the resource view format is a block compressed format, this value has to be 4 times the original width of the resource. For non block compressed formats, this value has to be equal to that of the original resource. - CUDA_RESOURCE_VIEW_DESC::height specifies the new height of the texture data. If the resource view format is a block compressed format, this value has to be 4 times the original height of the resource. For non block compressed formats, this value has to be equal to that of the original resource. - CUDA_RESOURCE_VIEW_DESC::depth specifies the new depth of the texture data. This value has to be equal to that of the original resource. - CUDA_RESOURCE_VIEW_DESC::firstMipmapLevel specifies the most detailed mipmap level. This will be the new mipmap level zero. For non-mipmapped resources, this value has to be zero.CUDA_TEXTURE_DESC::minMipmapLevelClamp and CUDA_TEXTURE_DESC::maxMipmapLevelClamp will be relative to this value. For ex., if the firstMipmapLevel is set to 2, and a minMipmapLevelClamp of 1.2 is specified, then the actual minimum mipmap level clamp will be 3.2. - CUDA_RESOURCE_VIEW_DESC::lastMipmapLevel specifies the least detailed mipmap level. For non-mipmapped resources, this value has to be zero. - CUDA_RESOURCE_VIEW_DESC::firstLayer specifies the first layer index for layered textures. This will be the new layer zero. For non-layered resources, this value has to be zero. - CUDA_RESOURCE_VIEW_DESC::lastLayer specifies the last layer index for layered textures. For non-layered resources, this value has to be zero.
- Parameters
- pResDescCUDA_RESOURCE_DESC
Resource descriptor
- pTexDescCUDA_TEXTURE_DESC
Texture descriptor
- pResViewDescCUDA_RESOURCE_VIEW_DESC
Resource view descriptor
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- pTexObjectCUtexObject
Texture object to create
See also
cuTexObjectDestroy
cudaCreateTextureObject
- cuda.cuda.cuTexObjectDestroy(texObject)¶
Destroys a texture object.
Destroys the texture object specified by texObject.
- Parameters
- texObjectAny
Texture object to destroy
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuTexObjectCreate
cudaDestroyTextureObject
- cuda.cuda.cuTexObjectGetResourceDesc(texObject)¶
Returns a texture object’s resource descriptor.
Returns the resource descriptor for the texture object specified by texObject.
- Parameters
- texObjectAny
Texture object
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- pResDescCUDA_RESOURCE_DESC
Resource descriptor
See also
cuTexObjectCreate
cudaGetTextureObjectResourceDesc
- cuda.cuda.cuTexObjectGetTextureDesc(texObject)¶
Returns a texture object’s texture descriptor.
Returns the texture descriptor for the texture object specified by texObject.
- Parameters
- texObjectAny
Texture object
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- pTexDescCUDA_TEXTURE_DESC
Texture descriptor
See also
cuTexObjectCreate
cudaGetTextureObjectTextureDesc
- cuda.cuda.cuTexObjectGetResourceViewDesc(texObject)¶
Returns a texture object’s resource view descriptor.
Returns the resource view descriptor for the texture object specified by texObject. If no resource view was set for texObject, the CUDA_ERROR_INVALID_VALUE is returned.
- Parameters
- texObjectAny
Texture object
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- pResViewDescCUDA_RESOURCE_VIEW_DESC
Resource view descriptor
See also
cuTexObjectCreate
cudaGetTextureObjectResourceViewDesc
Surface Object Management¶
This section describes the surface object management functions of the low-level CUDA driver application programming interface. The surface object API is only supported on devices of compute capability 3.0 or higher.
- cuda.cuda.cuSurfObjectCreate(CUDA_RESOURCE_DESC pResDesc: CUDA_RESOURCE_DESC)¶
Creates a surface object.
Creates a surface object and returns it in pSurfObject. pResDesc describes the data to perform surface load/stores on. CUDA_RESOURCE_DESC::resType must be CU_RESOURCE_TYPE_ARRAY and CUDA_RESOURCE_DESC::res::array::hArray must be set to a valid CUDA array handle. CUDA_RESOURCE_DESC::flags must be set to zero.
Surface objects are only supported on devices of compute capability 3.0 or higher. Additionally, a surface object is an opaque value, and, as such, should only be accessed through CUDA API calls.
- Parameters
- pResDescCUDA_RESOURCE_DESC
Resource descriptor
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- pSurfObjectCUsurfObject
Surface object to create
See also
cuSurfObjectDestroy
cudaCreateSurfaceObject
- cuda.cuda.cuSurfObjectDestroy(surfObject)¶
Destroys a surface object.
Destroys the surface object specified by surfObject.
- Parameters
- surfObjectAny
Surface object to destroy
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuSurfObjectCreate
cudaDestroySurfaceObject
- cuda.cuda.cuSurfObjectGetResourceDesc(surfObject)¶
Returns a surface object’s resource descriptor.
Returns the resource descriptor for the surface object specified by surfObject.
- Parameters
- surfObjectAny
Surface object
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- pResDescCUDA_RESOURCE_DESC
Resource descriptor
See also
cuSurfObjectCreate
cudaGetSurfaceObjectResourceDesc
Peer Context Memory Access¶
This section describes the direct peer context memory access functions of the low-level CUDA driver application programming interface.
- cuda.cuda.cuDeviceCanAccessPeer(dev, peerDev)¶
Queries if a device may directly access a peer device’s memory.
Returns in *canAccessPeer a value of 1 if contexts on dev are capable of directly accessing memory from contexts on peerDev and 0 otherwise. If direct access of peerDev from dev is possible, then access may be enabled on two specific contexts by calling cuCtxEnablePeerAccess().
- Parameters
- devAny
Device from which allocations on peerDev are to be directly accessed.
- peerDevAny
Device on which the allocations to be directly accessed by dev reside.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_DEVICE
- canAccessPeerint
Returned access capability
See also
cuCtxEnablePeerAccess
cuCtxDisablePeerAccess
cudaDeviceCanAccessPeer
- cuda.cuda.cuCtxEnablePeerAccess(peerContext, unsigned int Flags)¶
Enables direct access to memory allocations in a peer context.
If both the current context and peerContext are on devices which support unified addressing (as may be queried using CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING) and same major compute capability, then on success all allocations from peerContext will immediately be accessible by the current context. See Unified Addressing for additional details.
Note that access granted by this call is unidirectional and that in order to access memory from the current context in peerContext, a separate symmetric call to cuCtxEnablePeerAccess() is required.
Note that there are both device-wide and system-wide limitations per system configuration, as noted in the CUDA Programming Guide under the section “Peer-to-Peer Memory Access”.
Returns CUDA_ERROR_PEER_ACCESS_UNSUPPORTED if cuDeviceCanAccessPeer() indicates that the CUdevice of the current context cannot directly access memory from the CUdevice of peerContext.
Returns CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED if direct access of peerContext from the current context has already been enabled.
Returns CUDA_ERROR_TOO_MANY_PEERS if direct peer access is not possible because hardware resources required for peer access have been exhausted.
Returns CUDA_ERROR_INVALID_CONTEXT if there is no current context, peerContext is not a valid context, or if the current context is peerContext.
Returns CUDA_ERROR_INVALID_VALUE if Flags is not 0.
- Parameters
- peerContextAny
Peer context to enable direct access to from the current context
- Flagsunsigned int
Reserved for future use and must be set to 0
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED CUDA_ERROR_TOO_MANY_PEERS CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_PEER_ACCESS_UNSUPPORTED CUDA_ERROR_INVALID_VALUE
- None
None
See also
cuDeviceCanAccessPeer
cuCtxDisablePeerAccess
cudaDeviceEnablePeerAccess
- cuda.cuda.cuCtxDisablePeerAccess(peerContext)¶
Disables direct access to memory allocations in a peer context and unregisters any registered allocations.
Returns CUDA_ERROR_PEER_ACCESS_NOT_ENABLED if direct peer access has not yet been enabled from peerContext to the current context.
Returns CUDA_ERROR_INVALID_CONTEXT if there is no current context, or if peerContext is not a valid context.
- Parameters
- peerContextAny
Peer context to disable direct access to
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_PEER_ACCESS_NOT_ENABLED CUDA_ERROR_INVALID_CONTEXT
- None
None
See also
cuDeviceCanAccessPeer
cuCtxEnablePeerAccess
cudaDeviceDisablePeerAccess
- cuda.cuda.cuDeviceGetP2PAttribute(attrib: CUdevice_P2PAttribute, srcDevice, dstDevice)¶
Queries attributes of the link between two devices.
Returns in *value the value of the requested attribute attrib of the link between srcDevice and dstDevice. The supported attributes are: - CU_DEVICE_P2P_ATTRIBUTE_PERFORMANCE_RANK: A relative value indicating the performance of the link between two devices. - CU_DEVICE_P2P_ATTRIBUTE_ACCESS_SUPPORTED P2P: 1 if P2P Access is enable. - CU_DEVICE_P2P_ATTRIBUTE_NATIVE_ATOMIC_SUPPORTED: 1 if Atomic operations over the link are supported. - CU_DEVICE_P2P_ATTRIBUTE_CUDA_ARRAY_ACCESS_SUPPORTED: 1 if cudaArray can be accessed over the link.
Returns CUDA_ERROR_INVALID_DEVICE if srcDevice or dstDevice are not valid or if they represent the same device.
Returns CUDA_ERROR_INVALID_VALUE if attrib is not valid or if value is a null pointer.
- Parameters
- attribCUdevice_P2PAttribute
The requested attribute of the link between srcDevice and dstDevice.
- srcDeviceAny
The source device of the target link.
- dstDeviceAny
The destination device of the target link.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_DEVICE CUDA_ERROR_INVALID_VALUE
- valueint
Returned value of the requested attribute
See also
cuCtxEnablePeerAccess
cuCtxDisablePeerAccess
cuDeviceCanAccessPeer
cudaDeviceGetP2PAttribute
Graphics Interoperability¶
This section describes the graphics interoperability functions of the low-level CUDA driver application programming interface.
- cuda.cuda.cuGraphicsUnregisterResource(resource)¶
Unregisters a graphics resource for access by CUDA.
Unregisters the graphics resource resource so it is not accessible by CUDA unless registered again.
If resource is invalid then CUDA_ERROR_INVALID_HANDLE is returned.
- Parameters
- resourceAny
Resource to unregister
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_UNKNOWN
- None
None
See also
cuGraphicsGLRegisterBuffer
cuGraphicsGLRegisterImage
cudaGraphicsUnregisterResource
- cuda.cuda.cuGraphicsSubResourceGetMappedArray(resource, unsigned int arrayIndex, unsigned int mipLevel)¶
Get an array through which to access a subresource of a mapped graphics resource.
Returns in *pArray an array through which the subresource of the mapped graphics resource resource which corresponds to array index arrayIndex and mipmap level mipLevel may be accessed. The value set in *pArray may change every time that resource is mapped.
If resource is not a texture then it cannot be accessed via an array and CUDA_ERROR_NOT_MAPPED_AS_ARRAY is returned. If arrayIndex is not a valid array index for resource then CUDA_ERROR_INVALID_VALUE is returned. If mipLevel is not a valid mipmap level for resource then CUDA_ERROR_INVALID_VALUE is returned. If resource is not mapped then CUDA_ERROR_NOT_MAPPED is returned.
- Parameters
- resourceAny
Mapped resource to access
- arrayIndexunsigned int
Array index for array textures or cubemap face index as defined by CUarray_cubemap_face for cubemap textures for the subresource to access
- mipLevelunsigned int
Mipmap level for the subresource to access
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_NOT_MAPPED CUDA_ERROR_NOT_MAPPED_AS_ARRAY
- pArrayCUarray
Returned array through which a subresource of resource may be accessed
See also
cuGraphicsResourceGetMappedPointer
cudaGraphicsSubResourceGetMappedArray
- cuda.cuda.cuGraphicsResourceGetMappedMipmappedArray(resource)¶
Get a mipmapped array through which to access a mapped graphics resource.
Returns in *pMipmappedArray a mipmapped array through which the mapped graphics resource resource. The value set in *pMipmappedArray may change every time that resource is mapped.
If resource is not a texture then it cannot be accessed via a mipmapped array and CUDA_ERROR_NOT_MAPPED_AS_ARRAY is returned. If resource is not mapped then CUDA_ERROR_NOT_MAPPED is returned.
- Parameters
- resourceAny
Mapped resource to access
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_NOT_MAPPED CUDA_ERROR_NOT_MAPPED_AS_ARRAY
- pMipmappedArrayCUmipmappedArray
Returned mipmapped array through which resource may be accessed
See also
cuGraphicsResourceGetMappedPointer
cudaGraphicsResourceGetMappedMipmappedArray
- cuda.cuda.cuGraphicsResourceGetMappedPointer(resource)¶
Get a device pointer through which to access a mapped graphics resource.
Returns in *pDevPtr a pointer through which the mapped graphics resource resource may be accessed. Returns in pSize the size of the memory in bytes which may be accessed from that pointer. The value set in pPointer may change every time that resource is mapped.
If resource is not a buffer then it cannot be accessed via a pointer and CUDA_ERROR_NOT_MAPPED_AS_POINTER is returned. If resource is not mapped then CUDA_ERROR_NOT_MAPPED is returned. - pDevPtr - Returned pointer through which resource may be accessed pSize - Returned size of the buffer accessible starting at *pPointer resource - Mapped resource to access CUDA_SUCCESS, CUDA_ERROR_DEINITIALIZED, CUDA_ERROR_NOT_INITIALIZED, CUDA_ERROR_INVALID_CONTEXT, CUDA_ERROR_INVALID_VALUE, CUDA_ERROR_INVALID_HANDLE, CUDA_ERROR_NOT_MAPPED, CUDA_ERROR_NOT_MAPPED_AS_POINTER
- otefnerr cuGraphicsMapResources,
cuGraphicsSubResourceGetMappedArray, cudaGraphicsResourceGetMappedPointer
- Returns
- CUresult
- None
None
- cuda.cuda.cuGraphicsResourceSetMapFlags(resource, unsigned int flags)¶
Set usage flags for mapping a graphics resource.
Set flags for mapping the graphics resource resource.
Changes to flags will take effect the next time resource is mapped. The flags argument may be any of the following:
CU_GRAPHICS_MAP_RESOURCE_FLAGS_NONE: Specifies no hints about how
this resource will be used. It is therefore assumed that this resource will be read from and written to by CUDA kernels. This is the default value. - CU_GRAPHICS_MAP_RESOURCE_FLAGS_READONLY: Specifies that CUDA kernels which access this resource will not write to this resource. - CU_GRAPHICS_MAP_RESOURCE_FLAGS_WRITEDISCARD: Specifies that CUDA kernels which access this resource will not read from this resource and will write over the entire contents of the resource, so none of the data previously stored in the resource will be preserved.
If resource is presently mapped for access by CUDA then CUDA_ERROR_ALREADY_MAPPED is returned. If flags is not one of the above values then CUDA_ERROR_INVALID_VALUE is returned.
- Parameters
- resourceAny
Registered resource to set flags for
- flagsunsigned int
Parameters for resource mapping
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_ALREADY_MAPPED
- None
None
See also
cuGraphicsMapResources
cudaGraphicsResourceSetMapFlags
- cuda.cuda.cuGraphicsMapResources(unsigned int count, resources, hStream)¶
Map graphics resources for access by CUDA.
Maps the count graphics resources in resources for access by CUDA.
The resources in resources may be accessed by CUDA until they are unmapped. The graphics API from which resources were registered should not access any resources while they are mapped by CUDA. If an application does so, the results are undefined.
This function provides the synchronization guarantee that any graphics calls issued before cuGraphicsMapResources() will complete before any subsequent CUDA work issued in stream begins.
If resources includes any duplicate entries then CUDA_ERROR_INVALID_HANDLE is returned. If any of resources are presently mapped for access by CUDA then CUDA_ERROR_ALREADY_MAPPED is returned.
- Parameters
- countunsigned int
Number of resources to map
- resourcesAny
Resources to map for CUDA usage
- hStreamCUstream or cudaStream_t
Stream with which to synchronize
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_ALREADY_MAPPED CUDA_ERROR_UNKNOWN
- None
None
See also
cuGraphicsResourceGetMappedPointer
cuGraphicsSubResourceGetMappedArray
cuGraphicsUnmapResources
cudaGraphicsMapResources
- cuda.cuda.cuGraphicsUnmapResources(unsigned int count, resources, hStream)¶
Unmap graphics resources.
Unmaps the count graphics resources in resources.
Once unmapped, the resources in resources may not be accessed by CUDA until they are mapped again.
This function provides the synchronization guarantee that any CUDA work issued in stream before cuGraphicsUnmapResources() will complete before any subsequently issued graphics work begins.
If resources includes any duplicate entries then CUDA_ERROR_INVALID_HANDLE is returned. If any of resources are not presently mapped for access by CUDA then CUDA_ERROR_NOT_MAPPED is returned.
- Parameters
- countunsigned int
Number of resources to unmap
- resourcesAny
Resources to unmap
- hStreamCUstream or cudaStream_t
Stream with which to synchronize
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_NOT_MAPPED CUDA_ERROR_UNKNOWN
- None
None
See also
cuGraphicsMapResources
cudaGraphicsUnmapResources
Driver Entry Point Access¶
This section describes the driver entry point access functions of the low-level CUDA driver application programming interface.
- cuda.cuda.cuGetProcAddress(char *symbol, int cudaVersion, flags)¶
Returns the requested driver API function pointer.
Returns in **pfn the address of the CUDA driver function for the requested CUDA version and flags.
The CUDA version is specified as (1000 * major + 10 * minor), so CUDA 11.2 should be specified as 11020. For a requested driver symbol, if the specified CUDA version is greater than or equal to the CUDA version in which the driver symbol was introduced, this API will return the function pointer to the corresponding versioned function.
The pointer returned by the API should be cast to a function pointer matching the requested driver function’s definition in the API header file. The function pointer typedef can be picked up from the corresponding typedefs header file. For example, cudaTypedefs.h consists of function pointer typedefs for driver APIs defined in cuda.h.
The API will return CUDA_ERROR_NOT_FOUND if the requested driver function is not supported on the platform, no ABI compatible driver function exists for the specified cudaVersion or if the driver symbol is invalid.
The requested flags can be: - CU_GET_PROC_ADDRESS_DEFAULT: This is the default mode. This is equivalent to CU_GET_PROC_ADDRESS_PER_THREAD_DEFAULT_STREAM if the code is compiled with –default-stream per-thread compilation flag or the macro CUDA_API_PER_THREAD_DEFAULT_STREAM is defined; CU_GET_PROC_ADDRESS_LEGACY_STREAM otherwise. - CU_GET_PROC_ADDRESS_LEGACY_STREAM: This will enable the search for all driver symbols that match the requested driver symbol name except the corresponding per-thread versions. - CU_GET_PROC_ADDRESS_PER_THREAD_DEFAULT_STREAM: This will enable the search for all driver symbols that match the requested driver symbol name including the per-thread versions. If a per-thread version is not found, the API will return the legacy version of the driver function.
- Parameters
- symbolbytes
The base name of the driver API function to look for. As an example, for the driver API cuMemAlloc_v2, symbol would be cuMemAlloc and cudaVersion would be the ABI compatible CUDA version for the _v2 variant.
- cudaVersionint
The CUDA version to look for the requested driver symbol
- flagsAny
Flags to specify search options.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_VALUE CUDA_ERROR_NOT_SUPPORTED CUDA_ERROR_NOT_FOUND
- pfnint
Location to return the function pointer to the requested driver function
EGL Interoperability¶
This section describes the EGL interoperability functions of the low-level CUDA driver application programming interface.
- cuda.cuda.cuGraphicsEGLRegisterImage(image, unsigned int flags)¶
Registers an EGL image.
Registers the EGLImageKHR specified by image for access by CUDA. A handle to the registered object is returned as pCudaResource. Additional Mapping/Unmapping is not required for the registered resource and cuGraphicsResourceGetMappedEglFrame can be directly called on the pCudaResource.
The application will be responsible for synchronizing access to shared objects. The application must ensure that any pending operation which access the objects have completed before passing control to CUDA. This may be accomplished by issuing and waiting for glFinish command on all GLcontexts (for OpenGL and likewise for other APIs). The application will be also responsible for ensuring that any pending operation on the registered CUDA resource has completed prior to executing subsequent commands in other APIs accesing the same memory objects. This can be accomplished by calling cuCtxSynchronize or cuEventSynchronize (preferably).
The surface’s intended usage is specified using flags, as follows:
CU_GRAPHICS_MAP_RESOURCE_FLAGS_NONE: Specifies no hints about how
this resource will be used. It is therefore assumed that this resource will be read from and written to by CUDA. This is the default value. - CU_GRAPHICS_MAP_RESOURCE_FLAGS_READ_ONLY: Specifies that CUDA will not write to this resource. - CU_GRAPHICS_MAP_RESOURCE_FLAGS_WRITE_DISCARD: Specifies that CUDA will not read from this resource and will write over the entire contents of the resource, so none of the data previously stored in the resource will be preserved.
The EGLImageKHR is an object which can be used to create EGLImage target resource. It is defined as a void pointer. typedef void* EGLImageKHR
- Parameters
- imageAny
An EGLImageKHR image which can be used to create target resource.
- flagsunsigned int
Map flags
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_ALREADY_MAPPED CUDA_ERROR_INVALID_CONTEXT
- pCudaResourceCUgraphicsResource
Pointer to the returned object handle
See also
cuGraphicsEGLRegisterImage
cuGraphicsUnregisterResource
cuGraphicsResourceSetMapFlags
cuGraphicsMapResources
cuGraphicsUnmapResources
cudaGraphicsEGLRegisterImage
- cuda.cuda.cuEGLStreamConsumerConnect(stream)¶
Connect CUDA to EGLStream as a consumer.
Connect CUDA as a consumer to EGLStreamKHR specified by stream.
The EGLStreamKHR is an EGL object that transfers a sequence of image frames from one API to another.
- Parameters
- streamCUstream or cudaStream_t
EGLStreamKHR handle
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_INVALID_CONTEXT
- connCUeglStreamConnection
Pointer to the returned connection handle
See also
cuEGLStreamConsumerConnect
cuEGLStreamConsumerDisconnect
cuEGLStreamConsumerAcquireFrame
cuEGLStreamConsumerReleaseFrame
cudaEGLStreamConsumerConnect
- cuda.cuda.cuEGLStreamConsumerConnectWithFlags(stream, unsigned int flags)¶
Connect CUDA to EGLStream as a consumer with given flags.
Connect CUDA as a consumer to EGLStreamKHR specified by stream with specified flags defined by CUeglResourceLocationFlags.
The flags specify whether the consumer wants to access frames from system memory or video memory. Default is CU_EGL_RESOURCE_LOCATION_VIDMEM.
- Parameters
- streamCUstream or cudaStream_t
EGLStreamKHR handle
- flagsunsigned int
Flags denote intended location - system or video.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_INVALID_CONTEXT
- connCUeglStreamConnection
Pointer to the returned connection handle
See also
cuEGLStreamConsumerConnect
cuEGLStreamConsumerDisconnect
cuEGLStreamConsumerAcquireFrame
cuEGLStreamConsumerReleaseFrame
cudaEGLStreamConsumerConnectWithFlags
- cuda.cuda.cuEGLStreamConsumerDisconnect(conn)¶
Disconnect CUDA as a consumer to EGLStream .
Disconnect CUDA as a consumer to EGLStreamKHR.
- Parameters
- connAny
Conection to disconnect.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_INVALID_CONTEXT
- None
None
See also
cuEGLStreamConsumerConnect
cuEGLStreamConsumerDisconnect
cuEGLStreamConsumerAcquireFrame
cuEGLStreamConsumerReleaseFrame
cudaEGLStreamConsumerDisconnect
- cuda.cuda.cuEGLStreamConsumerAcquireFrame(conn, pCudaResource, pStream, unsigned int timeout)¶
Acquire an image frame from the EGLStream with CUDA as a consumer.
Acquire an image frame from EGLStreamKHR. This API can also acquire an old frame presented by the producer unless explicitly disabled by setting EGL_SUPPORT_REUSE_NV flag to EGL_FALSE during stream initialization. By default, EGLStream is created with this flag set to EGL_TRUE. cuGraphicsResourceGetMappedEglFrame can be called on pCudaResource to get CUeglFrame.
- Parameters
- connAny
Connection on which to acquire
- pCudaResourceAny
CUDA resource on which the stream frame will be mapped for use.
- pStreamAny
CUDA stream for synchronization and any data migrations implied by CUeglResourceLocationFlags.
- timeoutunsigned int
Desired timeout in usec for a new frame to be acquired. If set as CUDA_EGL_INFINITE_TIMEOUT, acquire waits infinitely. After timeout occurs CUDA consumer tries to acquire an old frame if available and EGL_SUPPORT_REUSE_NV flag is set.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_LAUNCH_TIMEOUT
- None
None
See also
cuEGLStreamConsumerConnect
cuEGLStreamConsumerDisconnect
cuEGLStreamConsumerAcquireFrame
cuEGLStreamConsumerReleaseFrame
cudaEGLStreamConsumerAcquireFrame
- cuda.cuda.cuEGLStreamConsumerReleaseFrame(conn, pCudaResource, pStream)¶
Releases the last frame acquired from the EGLStream.
Release the acquired image frame specified by pCudaResource to EGLStreamKHR. If EGL_SUPPORT_REUSE_NV flag is set to EGL_TRUE, at the time of EGL creation this API doesn’t release the last frame acquired on the EGLStream. By default, EGLStream is created with this flag set to EGL_TRUE.
- Parameters
- connAny
Connection on which to release
- pCudaResourceAny
CUDA resource whose corresponding frame is to be released
- pStreamAny
CUDA stream on which release will be done.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_HANDLE
- None
None
See also
cuEGLStreamConsumerConnect
cuEGLStreamConsumerDisconnect
cuEGLStreamConsumerAcquireFrame
cuEGLStreamConsumerReleaseFrame
cudaEGLStreamConsumerReleaseFrame
- cuda.cuda.cuEGLStreamProducerConnect(stream, width, height)¶
Connect CUDA to EGLStream as a producer.
Connect CUDA as a producer to EGLStreamKHR specified by stream.
The EGLStreamKHR is an EGL object that transfers a sequence of image frames from one API to another.
- Parameters
- streamCUstream or cudaStream_t
EGLStreamKHR handle
- widthAny
width of the image to be submitted to the stream
- heightAny
height of the image to be submitted to the stream
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_INVALID_CONTEXT
- connCUeglStreamConnection
Pointer to the returned connection handle
See also
cuEGLStreamProducerConnect
cuEGLStreamProducerDisconnect
cuEGLStreamProducerPresentFrame
cudaEGLStreamProducerConnect
- cuda.cuda.cuEGLStreamProducerDisconnect(conn)¶
Disconnect CUDA as a producer to EGLStream .
Disconnect CUDA as a producer to EGLStreamKHR.
- Parameters
- connAny
Conection to disconnect.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_INVALID_CONTEXT
- None
None
See also
cuEGLStreamProducerConnect
cuEGLStreamProducerDisconnect
cuEGLStreamProducerPresentFrame
cudaEGLStreamProducerDisconnect
- cuda.cuda.cuEGLStreamProducerPresentFrame(conn, CUeglFrame eglframe: CUeglFrame, pStream)¶
Present a CUDA eglFrame to the EGLStream with CUDA as a producer.
When a frame is presented by the producer, it gets associated with the EGLStream and thus it is illegal to free the frame before the producer is disconnected. If a frame is freed and reused it may lead to undefined behavior.
If producer and consumer are on different GPUs (iGPU and dGPU) then frametype CU_EGL_FRAME_TYPE_ARRAY is not supported. CU_EGL_FRAME_TYPE_PITCH can be used for such cross-device applications.
The CUeglFrame is defined as: typedefstructCUeglFrame_st{ union{ CUarraypArray[MAX_PLANES]; void*pPitch[MAX_PLANES]; }frame; unsignedintwidth; unsignedintheight; unsignedintdepth; unsignedintpitch; unsignedintplaneCount; unsignedintnumChannels; CUeglFrameTypeframeType; CUeglColorFormateglColorFormat; CUarray_formatcuFormat; }CUeglFrame;
For CUeglFrame of type CU_EGL_FRAME_TYPE_PITCH, the application may present sub-region of a memory allocation. In that case, the pitched pointer will specify the start address of the sub-region in the allocation and corresponding CUeglFrame fields will specify the dimensions of the sub-region.
- Parameters
- connAny
Connection on which to present the CUDA array
- eglframeCUeglFrame
CUDA Eglstream Proucer Frame handle to be sent to the consumer over EglStream.
- pStreamAny
CUDA stream on which to present the frame.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_HANDLE
- None
None
See also
cuEGLStreamProducerConnect
cuEGLStreamProducerDisconnect
cuEGLStreamProducerReturnFrame
cudaEGLStreamProducerPresentFrame
- cuda.cuda.cuEGLStreamProducerReturnFrame(conn, CUeglFrame eglframe: CUeglFrame, pStream)¶
Return the CUDA eglFrame to the EGLStream released by the consumer.
This API can potentially return CUDA_ERROR_LAUNCH_TIMEOUT if the consumer has not returned a frame to EGL stream. If timeout is returned the application can retry.
- Parameters
- connAny
Connection on which to return
- eglframeCUeglFrame
CUDA Eglstream Proucer Frame handle returned from the consumer over EglStream.
- pStreamAny
CUDA stream on which to return the frame.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_LAUNCH_TIMEOUT
- None
None
See also
cuEGLStreamProducerConnect
cuEGLStreamProducerDisconnect
cuEGLStreamProducerPresentFrame
cudaEGLStreamProducerReturnFrame
- cuda.cuda.cuGraphicsResourceGetMappedEglFrame(resource, unsigned int index, unsigned int mipLevel)¶
Get an eglFrame through which to access a registered EGL graphics resource.
Returns in *eglFrame an eglFrame pointer through which the registered graphics resource resource may be accessed. This API can only be called for registered EGL graphics resources.
The CUeglFrame is defined as: typedefstructCUeglFrame_st{ union{ CUarraypArray[MAX_PLANES]; void*pPitch[MAX_PLANES]; }frame; unsignedintwidth; unsignedintheight; unsignedintdepth; unsignedintpitch; unsignedintplaneCount; unsignedintnumChannels; CUeglFrameTypeframeType; CUeglColorFormateglColorFormat; CUarray_formatcuFormat; }CUeglFrame;
If resource is not registered then CUDA_ERROR_NOT_MAPPED is returned. - eglFrame - Returned eglFrame. resource - Registered resource to access. index - Index for cubemap surfaces. mipLevel - Mipmap level for the subresource to access. CUDA_SUCCESS, CUDA_ERROR_DEINITIALIZED, CUDA_ERROR_NOT_INITIALIZED, CUDA_ERROR_INVALID_CONTEXT, CUDA_ERROR_INVALID_VALUE, CUDA_ERROR_INVALID_HANDLE, CUDA_ERROR_NOT_MAPPED cuGraphicsMapResources, cuGraphicsSubResourceGetMappedArray, cuGraphicsResourceGetMappedPointer, cudaGraphicsResourceGetMappedEglFrame
- Returns
- CUresult
- None
None
- cuda.cuda.cuEventCreateFromEGLSync(eglSync, unsigned int flags)¶
Creates an event from EGLSync object.
Creates an event *phEvent from an EGLSyncKHR eglSync with the flags specified via flags. Valid flags include: - CU_EVENT_DEFAULT: Default event creation flag. - CU_EVENT_BLOCKING_SYNC: Specifies that the created event should use blocking synchronization. A CPU thread that uses cuEventSynchronize() to wait on an event created with this flag will block until the event has actually been completed.
Once the eglSync gets destroyed, cuEventDestroy is the only API that can be invoked on the event.
cuEventRecord and TimingData are not supported for events created from EGLSync.
The EGLSyncKHR is an opaque handle to an EGL sync object. typedef void* EGLSyncKHR
- Parameters
- eglSyncAny
Opaque handle to EGLSync object
- flagsunsigned int
Event creation flags
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_OUT_OF_MEMORY
- phEventCUevent
Returns newly created event
See also
OpenGL Interoperability¶
This section describes the OpenGL interoperability functions of the low-level CUDA driver application programming interface. Note that mapping of OpenGL resources is performed with the graphics API agnostic, resource mapping interface described in Graphics Interoperability.
- enum cuda.cuda.CUGLDeviceList(value)¶
CUDA devices corresponding to an OpenGL device
- Member Type
int
Valid values are as follows:
- CU_GL_DEVICE_LIST_ALL¶
- CU_GL_DEVICE_LIST_CURRENT_FRAME¶
- CU_GL_DEVICE_LIST_NEXT_FRAME¶
- cuda.cuda.cuGraphicsGLRegisterBuffer(buffer, unsigned int Flags)¶
Registers an OpenGL buffer object.
Registers the buffer object specified by buffer for access by CUDA. A handle to the registered object is returned as pCudaResource. The register flags Flags specify the intended usage, as follows:
CU_GRAPHICS_REGISTER_FLAGS_NONE: Specifies no hints about how this
resource will be used. It is therefore assumed that this resource will be read from and written to by CUDA. This is the default value. - CU_GRAPHICS_REGISTER_FLAGS_READ_ONLY: Specifies that CUDA will not write to this resource. - CU_GRAPHICS_REGISTER_FLAGS_WRITE_DISCARD: Specifies that CUDA will not read from this resource and will write over the entire contents of the resource, so none of the data previously stored in the resource will be preserved.
- Parameters
- bufferAny
name of buffer object to be registered
- Flagsunsigned int
Register flags
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_ALREADY_MAPPED CUDA_ERROR_INVALID_CONTEXT
- pCudaResourceCUgraphicsResource
Pointer to the returned object handle
See also
cuGraphicsUnregisterResource
cuGraphicsMapResources
cuGraphicsResourceGetMappedPointer
cudaGraphicsGLRegisterBuffer
- cuda.cuda.cuGraphicsGLRegisterImage(image, target, unsigned int Flags)¶
Register an OpenGL texture or renderbuffer object.
Registers the texture or renderbuffer object specified by image for access by CUDA. A handle to the registered object is returned as pCudaResource.
target must match the type of the object, and must be one of GL_TEXTURE_2D, GL_TEXTURE_RECTANGLE, GL_TEXTURE_CUBE_MAP, GL_TEXTURE_3D, GL_TEXTURE_2D_ARRAY, or GL_RENDERBUFFER.
The register flags Flags specify the intended usage, as follows:
CU_GRAPHICS_REGISTER_FLAGS_NONE: Specifies no hints about how this
resource will be used. It is therefore assumed that this resource will be read from and written to by CUDA. This is the default value. - CU_GRAPHICS_REGISTER_FLAGS_READ_ONLY: Specifies that CUDA will not write to this resource. - CU_GRAPHICS_REGISTER_FLAGS_WRITE_DISCARD: Specifies that CUDA will not read from this resource and will write over the entire contents of the resource, so none of the data previously stored in the resource will be preserved. - CU_GRAPHICS_REGISTER_FLAGS_SURFACE_LDST: Specifies that CUDA will bind this resource to a surface reference. - CU_GRAPHICS_REGISTER_FLAGS_TEXTURE_GATHER: Specifies that CUDA will perform texture gather operations on this resource.
The following image formats are supported. For brevity’s sake, the list is abbreviated. For ex., {GL_R, GL_RG} X {8, 16} would expand to the following 4 formats {GL_R8, GL_R16, GL_RG8, GL_RG16} : - GL_RED, GL_RG, GL_RGBA, GL_LUMINANCE, GL_ALPHA, GL_LUMINANCE_ALPHA, GL_INTENSITY - {GL_R, GL_RG, GL_RGBA} X {8, 16, 16F, 32F, 8UI, 16UI, 32UI, 8I, 16I, 32I} - {GL_LUMINANCE, GL_ALPHA, GL_LUMINANCE_ALPHA, GL_INTENSITY} X {8, 16, 16F_ARB, 32F_ARB, 8UI_EXT, 16UI_EXT, 32UI_EXT, 8I_EXT, 16I_EXT, 32I_EXT}
The following image classes are currently disallowed: - Textures with borders - Multisampled renderbuffers
- Parameters
- imageAny
name of texture or renderbuffer object to be registered
- targetAny
Identifies the type of object specified by image
- Flagsunsigned int
Register flags
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_ALREADY_MAPPED CUDA_ERROR_INVALID_CONTEXT
- pCudaResourceCUgraphicsResource
Pointer to the returned object handle
See also
cuGraphicsUnregisterResource
cuGraphicsMapResources
cuGraphicsSubResourceGetMappedArray
cudaGraphicsGLRegisterImage
- cuda.cuda.cuGLGetDevices(unsigned int cudaDeviceCount, deviceList: CUGLDeviceList)¶
Gets the CUDA devices associated with the current OpenGL context.
Returns in *pCudaDeviceCount the number of CUDA-compatible devices corresponding to the current OpenGL context. Also returns in *pCudaDevices at most cudaDeviceCount of the CUDA-compatible devices corresponding to the current OpenGL context. If any of the GPUs being used by the current OpenGL context are not CUDA capable then the call will return CUDA_ERROR_NO_DEVICE.
The deviceList argument may be any of the following: CU_GL_DEVICE_LIST_ALL: Query all devices used by the current OpenGL context. CU_GL_DEVICE_LIST_CURRENT_FRAME: Query the devices used by the current OpenGL context to render the current frame (in SLI). CU_GL_DEVICE_LIST_NEXT_FRAME: Query the devices used by the current OpenGL context to render the next frame (in SLI). Note that this is a prediction, it can’t be guaranteed that this is correct in all cases.
- Parameters
- cudaDeviceCountunsigned int
The size of the output device array pCudaDevices.
- deviceListCUGLDeviceList
The set of devices to return.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_NO_DEVICE CUDA_ERROR_INVALID_VALUE CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_GRAPHICS_CONTEXT
- pCudaDeviceCountunsigned int
Returned number of CUDA devices.
- pCudaDevicesList[CUdevice]
Returned CUDA devices.
See also
cudaGLGetDevices
Notes
This function is not supported on Mac OS X.
Profiler Control¶
This section describes the profiler control functions of the low-level CUDA driver application programming interface.
- cuda.cuda.cuProfilerStart()¶
Enable profiling.
Enables profile collection by the active profiling tool for the current context. If profiling is already enabled, then cuProfilerStart() has no effect.
cuProfilerStart and cuProfilerStop APIs are used to programmatically control the profiling granularity by allowing profiling to be done only on selective pieces of code.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_CONTEXT
- None
None
See also
cuProfilerInitialize
cuProfilerStop
cudaProfilerStart
- cuda.cuda.cuProfilerStop()¶
Disable profiling.
Disables profile collection by the active profiling tool for the current context. If profiling is already disabled, then cuProfilerStop() has no effect.
cuProfilerStart and cuProfilerStop APIs are used to programmatically control the profiling granularity by allowing profiling to be done only on selective pieces of code.
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_CONTEXT
- None
None
See also
cuProfilerInitialize
cuProfilerStart
cudaProfilerStop
VDPAU Interoperability¶
This section describes the VDPAU interoperability functions of the low-level CUDA driver application programming interface.
- cuda.cuda.cuVDPAUGetDevice(vdpDevice, vdpGetProcAddress)¶
Gets the CUDA device associated with a VDPAU device.
Returns in *pDevice the CUDA device associated with a vdpDevice, if applicable.
- Parameters
- vdpDeviceAny
A VdpDevice handle
- vdpGetProcAddressAny
VDPAU’s VdpGetProcAddress function pointer
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE
- pDeviceCUdevice
Device associated with vdpDevice
- cuda.cuda.cuVDPAUCtxCreate(unsigned int flags, device, vdpDevice, vdpGetProcAddress)¶
Create a CUDA context for interoperability with VDPAU.
Creates a new CUDA context, initializes VDPAU interoperability, and associates the CUDA context with the calling thread. It must be called before performing any other VDPAU interoperability operations. It may fail if the needed VDPAU driver facilities are not available. For usage of the flags parameter, see cuCtxCreate().
- Parameters
- flagsunsigned int
Options for CUDA context creation
- deviceAny
Device on which to create the context
- vdpDeviceAny
The VdpDevice to interop with
- vdpGetProcAddressAny
VDPAU’s VdpGetProcAddress function pointer
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_DEINITIALIZED CUDA_ERROR_NOT_INITIALIZED CUDA_ERROR_INVALID_CONTEXT CUDA_ERROR_INVALID_VALUE CUDA_ERROR_OUT_OF_MEMORY
- pCtxCUcontext
Returned CUDA context
- cuda.cuda.cuGraphicsVDPAURegisterVideoSurface(vdpSurface, unsigned int flags)¶
Registers a VDPAU VdpVideoSurface object.
Registers the VdpVideoSurface specified by vdpSurface for access by CUDA. A handle to the registered object is returned as pCudaResource. The surface’s intended usage is specified using flags, as follows:
CU_GRAPHICS_MAP_RESOURCE_FLAGS_NONE: Specifies no hints about how
this resource will be used. It is therefore assumed that this resource will be read from and written to by CUDA. This is the default value. - CU_GRAPHICS_MAP_RESOURCE_FLAGS_READ_ONLY: Specifies that CUDA will not write to this resource. - CU_GRAPHICS_MAP_RESOURCE_FLAGS_WRITE_DISCARD: Specifies that CUDA will not read from this resource and will write over the entire contents of the resource, so none of the data previously stored in the resource will be preserved.
The VdpVideoSurface is presented as an array of subresources that may be accessed using pointers returned by cuGraphicsSubResourceGetMappedArray. The exact number of valid arrayIndex values depends on the VDPAU surface format. The mapping is shown in the table below. mipLevel must be 0.
<table> <tr><th>VdpChromaType
</th><th>arrayIndex</th><th>Size </th><th>Format</th><th>Content </th></tr> <tr><td rowspan=”4” valign=”top”>VDP_CHROMA_TYPE_420</td><td>0 </td><td>w x h/2</td><td>R8 </td><td>Top-field luma </td></tr> <tr> <td>1 </td><td>w x h/2</td><td>R8 </td><td>Bottom-field luma </td></tr> <tr> <td>2 </td><td>w/2 x h/4</td><td>R8G8 </td><td>Top-field chroma </td></tr> <tr> <td>3 </td><td>w/2 x h/4</td><td>R8G8 </td><td>Bottom-field chroma</td></tr> <tr><td rowspan=”4” valign=”top”>VDP_CHROMA_TYPE_422</td><td>0 </td><td>w x h/2</td><td>R8 </td><td>Top-field luma </td></tr> <tr> <td>1 </td><td>w x h/2</td><td>R8 </td><td>Bottom-field luma </td></tr> <tr> <td>2 </td><td>w/2 x h/2</td><td>R8G8 </td><td>Top-field chroma </td></tr> <tr> <td>3 </td><td>w/2 x h/2</td><td>R8G8 </td><td>Bottom-field chroma</td></tr> </table>
egin{tabular}{|l|l|l|l|l|} hline VdpChromaType &
arrayIndex & Size & Format & Content hline VDP_CHROMA_TYPE_420 & 0 & w x h/2 & R8 & Top-field luma & 1 & w x h/2 & R8 & Bottom-field luma & 2 & w/2 x h/4 & R8G8 & Top-field chroma & 3 & w/2 x h/4 & R8G8 & Bottom-field chroma hline VDP_CHROMA_TYPE_422 & 0 & w x h/2 & R8 & Top-field luma & 1 & w x h/2 & R8 & Bottom-field luma & 2 & w/2 x h/2 & R8G8 & Top-field chroma & 3 & w/2 x h/2 & R8G8 & Bottom-field chroma hline end{tabular}
- Parameters
- vdpSurfaceAny
The VdpVideoSurface to be registered
- flagsunsigned int
Map flags
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_ALREADY_MAPPED CUDA_ERROR_INVALID_CONTEXT
- pCudaResourceCUgraphicsResource
Pointer to the returned object handle
See also
- cuda.cuda.cuGraphicsVDPAURegisterOutputSurface(vdpSurface, unsigned int flags)¶
Registers a VDPAU VdpOutputSurface object.
Registers the VdpOutputSurface specified by vdpSurface for access by CUDA. A handle to the registered object is returned as pCudaResource. The surface’s intended usage is specified using flags, as follows:
CU_GRAPHICS_MAP_RESOURCE_FLAGS_NONE: Specifies no hints about how
this resource will be used. It is therefore assumed that this resource will be read from and written to by CUDA. This is the default value. - CU_GRAPHICS_MAP_RESOURCE_FLAGS_READ_ONLY: Specifies that CUDA will not write to this resource. - CU_GRAPHICS_MAP_RESOURCE_FLAGS_WRITE_DISCARD: Specifies that CUDA will not read from this resource and will write over the entire contents of the resource, so none of the data previously stored in the resource will be preserved.
The VdpOutputSurface is presented as an array of subresources that may be accessed using pointers returned by cuGraphicsSubResourceGetMappedArray. The exact number of valid arrayIndex values depends on the VDPAU surface format. The mapping is shown in the table below. mipLevel must be 0.
<table> <tr><th>VdpRGBAFormat
</th><th>arrayIndex</th><th>Size </th><th>Format </th><th>Content </th></tr> <tr><td>VDP_RGBA_FORMAT_B8G8R8A8 </td><td>0 </td><td>w x h</td><td>ARGB8 </td><td>Entire surface</td></tr> <tr><td>VDP_RGBA_FORMAT_R10G10B10A2</td><td>0 </td><td>w x h</td><td>A2BGR10</td><td>Entire surface</td></tr> </table>
egin{tabular}{|l|l|l|l|l|} hline VdpRGBAFormat &
arrayIndex & Size & Format & Content hline VDP_RGBA_FORMAT_B8G8R8A8 & 0 & w x h & ARGB8 & Entire surface VDP_RGBA_FORMAT_R10G10B10A2 & 0 & w x h & A2BGR10 & Entire surface hline end{tabular}
- Parameters
- vdpSurfaceAny
The VdpOutputSurface to be registered
- flagsunsigned int
Map flags
- Returns
- CUresult
CUDA_SUCCESS CUDA_ERROR_INVALID_HANDLE CUDA_ERROR_ALREADY_MAPPED CUDA_ERROR_INVALID_CONTEXT
- pCudaResourceCUgraphicsResource
Pointer to the returned object handle
See also