6.33. Data types used by CUDA Runtime
Classes
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- union
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
- struct
Defines
- #define CUDA_EGL_MAX_PLANES 3
- #define CUDA_IPC_HANDLE_SIZE 64
- #define cudaArrayColorAttachment 0x20
- #define cudaArrayCubemap 0x04
- #define cudaArrayDefault 0x00
- #define cudaArrayDeferredMapping 0x80
- #define cudaArrayLayered 0x01
- #define cudaArraySparse 0x40
- #define cudaArraySparsePropertiesSingleMipTail 0x1
- #define cudaArraySurfaceLoadStore 0x02
- #define cudaArrayTextureGather 0x08
- #define cudaCooperativeLaunchMultiDeviceNoPostSync 0x02
- #define cudaCooperativeLaunchMultiDeviceNoPreSync 0x01
- #define cudaCpuDeviceId ((int)-1)
- #define cudaDeviceBlockingSync 0x04
- #define cudaDeviceLmemResizeToMax 0x10
- #define cudaDeviceMapHost 0x08
- #define cudaDeviceMask 0xff
- #define cudaDeviceScheduleAuto 0x00
- #define cudaDeviceScheduleBlockingSync 0x04
- #define cudaDeviceScheduleMask 0x07
- #define cudaDeviceScheduleSpin 0x01
- #define cudaDeviceScheduleYield 0x02
- #define cudaDeviceSyncMemops 0x80
- #define cudaEventBlockingSync 0x01
- #define cudaEventDefault 0x00
- #define cudaEventDisableTiming 0x02
- #define cudaEventInterprocess 0x04
- #define cudaEventRecordDefault 0x00
- #define cudaEventRecordExternal 0x01
- #define cudaEventWaitDefault 0x00
- #define cudaEventWaitExternal 0x01
- #define cudaExternalMemoryDedicated 0x1
- #define cudaExternalSemaphoreSignalSkipNvSciBufMemSync 0x01
- #define cudaExternalSemaphoreWaitSkipNvSciBufMemSync 0x02
- #define cudaGraphKernelNodePortDefault 0
- #define cudaGraphKernelNodePortLaunchCompletion 2
- #define cudaGraphKernelNodePortProgrammatic 1
- #define cudaHostAllocDefault 0x00
- #define cudaHostAllocMapped 0x02
- #define cudaHostAllocPortable 0x01
- #define cudaHostAllocWriteCombined 0x04
- #define cudaHostRegisterDefault 0x00
- #define cudaHostRegisterIoMemory 0x04
- #define cudaHostRegisterMapped 0x02
- #define cudaHostRegisterPortable 0x01
- #define cudaHostRegisterReadOnly 0x08
- #define cudaInitDeviceFlagsAreValid 0x01
- #define cudaInvalidDeviceId ((int)-2)
- #define cudaIpcMemLazyEnablePeerAccess 0x01
- #define cudaMemAttachGlobal 0x01
- #define cudaMemAttachHost 0x02
- #define cudaMemAttachSingle 0x04
- #define cudaNvSciSyncAttrSignal 0x1
- #define cudaNvSciSyncAttrWait 0x2
- #define cudaOccupancyDefault 0x00
- #define cudaOccupancyDisableCachingOverride 0x01
- #define cudaPeerAccessDefault 0x00
- #define cudaStreamDefault 0x00
- #define cudaStreamLegacy ((cudaStream_t)0x1)
- #define cudaStreamNonBlocking 0x01
- #define cudaStreamPerThread ((cudaStream_t)0x2)
Typedefs
- typedef cudaArray * cudaArray_const_t
- typedef cudaArray * cudaArray_t
- typedef CUeglStreamConnection_st * cudaEglStreamConnection
- typedef enumcudaError cudaError_t
- typedef CUevent_st * cudaEvent_t
- typedef CUexternalMemory_st * cudaExternalMemory_t
- typedef CUexternalSemaphore_st * cudaExternalSemaphore_t
- typedef CUfunc_st * cudaFunction_t
- typedef unsigned long long cudaGraphConditionalHandle
- typedef CUgraphExec_st * cudaGraphExec_t
- typedef CUgraphNode_st * cudaGraphNode_t
- typedef CUgraph_st * cudaGraph_t
- typedef cudaGraphicsResource * cudaGraphicsResource_t
- typedef void(CUDART_CB* cudaHostFn_t )( void* userData )
- typedef CUkern_st * cudaKernel_t
- typedef CUmemPoolHandle_st * cudaMemPool_t
- typedef cudaMipmappedArray * cudaMipmappedArray_const_t
- typedef cudaMipmappedArray * cudaMipmappedArray_t
- typedef CUstream_st * cudaStream_t
- typedef unsigned long long cudaSurfaceObject_t
- typedef unsigned long long cudaTextureObject_t
- typedef CUuserObject_st * cudaUserObject_t
Enumerations
- enum cudaAccessProperty
- enum cudaCGScope
- enum cudaChannelFormatKind
- enum cudaClusterSchedulingPolicy
- enum cudaComputeMode
- enum cudaDeviceAttr
- enum cudaDeviceP2PAttr
- enum cudaDriverEntryPointQueryResult
- enum cudaEglColorFormat
- enum cudaEglFrameType
- enum cudaEglResourceLocationFlags
- enum cudaError
- enum cudaExternalMemoryHandleType
- enum cudaExternalSemaphoreHandleType
- enum cudaFlushGPUDirectRDMAWritesOptions
- enum cudaFlushGPUDirectRDMAWritesScope
- enum cudaFlushGPUDirectRDMAWritesTarget
- enum cudaFuncAttribute
- enum cudaFuncCache
- enum cudaGPUDirectRDMAWritesOrdering
- enum cudaGetDriverEntryPointFlags
- enum cudaGraphConditionalNodeType
- enum cudaGraphDebugDotFlags
- enum cudaGraphDependencyType
- enum cudaGraphExecUpdateResult
- enum cudaGraphInstantiateFlags
- enum cudaGraphInstantiateResult
- enum cudaGraphMemAttributeType
- enum cudaGraphNodeType
- enum cudaGraphicsCubeFace
- enum cudaGraphicsMapFlags
- enum cudaGraphicsRegisterFlags
- enum cudaLaunchAttributeID
- enum cudaLaunchMemSyncDomain
- enum cudaLimit
- enum cudaMemAccessFlags
- enum cudaMemAllocationHandleType
- enum cudaMemAllocationType
- enum cudaMemLocationType
- enum cudaMemPoolAttr
- enum cudaMemRangeAttribute
- enum cudaMemcpyKind
- enum cudaMemoryAdvise
- enum cudaMemoryType
- enum cudaResourceType
- enum cudaResourceViewFormat
- enum cudaSharedCarveout
- enum cudaSharedMemConfig
- enum cudaStreamCaptureMode
- enum cudaStreamCaptureStatus
- enum cudaStreamUpdateCaptureDependenciesFlags
- enum cudaSurfaceBoundaryMode
- enum cudaSurfaceFormatMode
- enum cudaTextureAddressMode
- enum cudaTextureFilterMode
- enum cudaTextureReadMode
- enum cudaUserObjectFlags
- enum cudaUserObjectRetainFlags
Defines
- #define CUDA_EGL_MAX_PLANES 3
-
Maximum number of planes per frame
- #define CUDA_IPC_HANDLE_SIZE 64
-
CUDA IPC Handle Size
- #define cudaArrayColorAttachment 0x20
-
Must be set in cudaExternalMemoryGetMappedMipmappedArray if the mipmapped array is used as a color target in a graphics API
- #define cudaArrayCubemap 0x04
-
Must be set in cudaMalloc3DArray to create a cubemap CUDA array
- #define cudaArrayDefault 0x00
-
Default CUDA array allocation flag
- #define cudaArrayDeferredMapping 0x80
-
Must be set in cudaMallocArray, cudaMalloc3DArray or cudaMallocMipmappedArray in order to create a deferred mapping CUDA array or CUDA mipmapped array
- #define cudaArrayLayered 0x01
-
Must be set in cudaMalloc3DArray to create a layered CUDA array
- #define cudaArraySparse 0x40
-
Must be set in cudaMallocArray, cudaMalloc3DArray or cudaMallocMipmappedArray in order to create a sparse CUDA array or CUDA mipmapped array
- #define cudaArraySparsePropertiesSingleMipTail 0x1
-
Indicates that the layered sparse CUDA array or CUDA mipmapped array has a single mip tail region for all layers
- #define cudaArraySurfaceLoadStore 0x02
-
Must be set in cudaMallocArray or cudaMalloc3DArray in order to bind surfaces to the CUDA array
- #define cudaArrayTextureGather 0x08
-
Must be set in cudaMallocArray or cudaMalloc3DArray in order to perform texture gather operations on the CUDA array
- #define cudaCooperativeLaunchMultiDeviceNoPostSync 0x02
-
If set, any subsequent work pushed in a stream that participated in a call to cudaLaunchCooperativeKernelMultiDevice will only wait for the kernel launched on the GPU corresponding to that stream to complete before it begins execution.
- #define cudaCooperativeLaunchMultiDeviceNoPreSync 0x01
-
If set, each kernel launched as part of cudaLaunchCooperativeKernelMultiDevice only waits for prior work in the stream corresponding to that GPU to complete before the kernel begins execution.
- #define cudaCpuDeviceId ((int)-1)
-
Device id that represents the CPU
- #define cudaDeviceBlockingSync 0x04
-
Deprecated
This flag was deprecated as of CUDA 4.0 and replaced with cudaDeviceScheduleBlockingSync.
Device flag - Use blocking synchronization
- #define cudaDeviceLmemResizeToMax 0x10
-
Device flag - Keep local memory allocation after launch
- #define cudaDeviceMapHost 0x08
-
Device flag - Support mapped pinned allocations
- #define cudaDeviceMask 0xff
-
Device flags mask
- #define cudaDeviceScheduleAuto 0x00
-
Device flag - Automatic scheduling
- #define cudaDeviceScheduleBlockingSync 0x04
-
Device flag - Use blocking synchronization
- #define cudaDeviceScheduleMask 0x07
-
Device schedule flags mask
- #define cudaDeviceScheduleSpin 0x01
-
Device flag - Spin default scheduling
- #define cudaDeviceScheduleYield 0x02
-
Device flag - Yield default scheduling
- #define cudaDeviceSyncMemops 0x80
-
Device flag - Ensure synchronous memory operations on this context will synchronize
- #define cudaEventBlockingSync 0x01
-
Event uses blocking synchronization
- #define cudaEventDefault 0x00
-
Default event flag
- #define cudaEventDisableTiming 0x02
-
Event will not record timing data
- #define cudaEventInterprocess 0x04
-
Event is suitable for interprocess use. cudaEventDisableTiming must be set
- #define cudaEventRecordDefault 0x00
-
Default event record flag
- #define cudaEventRecordExternal 0x01
-
Event is captured in the graph as an external event node when performing stream capture
- #define cudaEventWaitDefault 0x00
-
Default event wait flag
- #define cudaEventWaitExternal 0x01
-
Event is captured in the graph as an external event node when performing stream capture
- #define cudaExternalMemoryDedicated 0x1
-
Indicates that the external memory object is a dedicated resource
- #define cudaExternalSemaphoreSignalSkipNvSciBufMemSync 0x01
-
When the /p flags parameter of cudaExternalSemaphoreSignalParams contains this flag, it indicates that signaling an external semaphore object should skip performing appropriate memory synchronization operations over all the external memory objects that are imported as cudaExternalMemoryHandleTypeNvSciBuf, which otherwise are performed by default to ensure data coherency with other importers of the same NvSciBuf memory objects.
- #define cudaExternalSemaphoreWaitSkipNvSciBufMemSync 0x02
-
When the /p flags parameter of cudaExternalSemaphoreWaitParams contains this flag, it indicates that waiting an external semaphore object should skip performing appropriate memory synchronization operations over all the external memory objects that are imported as cudaExternalMemoryHandleTypeNvSciBuf, which otherwise are performed by default to ensure data coherency with other importers of the same NvSciBuf memory objects.
- #define cudaGraphKernelNodePortDefault 0
-
This port activates when the kernel has finished executing.
- #define cudaGraphKernelNodePortLaunchCompletion 2
-
This port activates when all blocks of the kernel have begun execution. See also cudaLaunchAttributeLaunchCompletionEvent.
- #define cudaGraphKernelNodePortProgrammatic 1
-
This port activates when all blocks of the kernel have performed cudaTriggerProgrammaticLaunchCompletion() or have terminated. It must be used with edge type cudaGraphDependencyTypeProgrammatic. See also cudaLaunchAttributeProgrammaticEvent.
- #define cudaHostAllocDefault 0x00
-
Default page-locked allocation flag
- #define cudaHostAllocMapped 0x02
-
Map allocation into device space
- #define cudaHostAllocPortable 0x01
-
Pinned memory accessible by all CUDA contexts
- #define cudaHostAllocWriteCombined 0x04
-
Write-combined memory
- #define cudaHostRegisterDefault 0x00
-
Default host memory registration flag
- #define cudaHostRegisterIoMemory 0x04
-
Memory-mapped I/O space
- #define cudaHostRegisterMapped 0x02
-
Map registered memory into device space
- #define cudaHostRegisterPortable 0x01
-
Pinned memory accessible by all CUDA contexts
- #define cudaHostRegisterReadOnly 0x08
-
Memory-mapped read-only
- #define cudaInitDeviceFlagsAreValid 0x01
-
Tell the CUDA runtime that DeviceFlags is being set in cudaInitDevice call
- #define cudaInvalidDeviceId ((int)-2)
-
Device id that represents an invalid device
- #define cudaIpcMemLazyEnablePeerAccess 0x01
-
Automatically enable peer access between remote devices as needed
- #define cudaMemAttachGlobal 0x01
-
Memory can be accessed by any stream on any device
- #define cudaMemAttachHost 0x02
-
Memory cannot be accessed by any stream on any device
- #define cudaMemAttachSingle 0x04
-
Memory can only be accessed by a single stream on the associated device
- #define cudaNvSciSyncAttrSignal 0x1
-
When /p flags of cudaDeviceGetNvSciSyncAttributes is set to this, it indicates that application need signaler specific NvSciSyncAttr to be filled by cudaDeviceGetNvSciSyncAttributes.
- #define cudaNvSciSyncAttrWait 0x2
-
When /p flags of cudaDeviceGetNvSciSyncAttributes is set to this, it indicates that application need waiter specific NvSciSyncAttr to be filled by cudaDeviceGetNvSciSyncAttributes.
- #define cudaOccupancyDefault 0x00
-
Default behavior
- #define cudaOccupancyDisableCachingOverride 0x01
-
Assume global caching is enabled and cannot be automatically turned off
- #define cudaPeerAccessDefault 0x00
-
Default peer addressing enable flag
- #define cudaStreamDefault 0x00
-
Default stream flag
- #define cudaStreamLegacy ((cudaStream_t)0x1)
-
Legacy stream handle
Stream handle that can be passed as a cudaStream_t to use an implicit stream with legacy synchronization behavior.
See details of the synchronization behavior.
- #define cudaStreamNonBlocking 0x01
-
Stream does not synchronize with stream 0 (the NULL stream)
- #define cudaStreamPerThread ((cudaStream_t)0x2)
-
Per-thread stream handle
Stream handle that can be passed as a cudaStream_t to use an implicit stream with per-thread synchronization behavior.
See details of the synchronization behavior.
Typedefs
- typedef cudaArray * cudaArray_const_t
-
CUDA array (as source copy argument)
- typedef cudaArray * cudaArray_t
-
CUDA array
- typedef CUeglStreamConnection_st * cudaEglStreamConnection
-
CUDA EGLSream Connection
- typedef enumcudaError cudaError_t
-
CUDA Error types
- typedef CUevent_st * cudaEvent_t
-
CUDA event types
- typedef CUexternalMemory_st * cudaExternalMemory_t
-
CUDA external memory
- typedef CUexternalSemaphore_st * cudaExternalSemaphore_t
-
CUDA external semaphore
- typedef CUfunc_st * cudaFunction_t
-
CUDA function
- typedef unsigned long long cudaGraphConditionalHandle
-
CUDA handle for conditional graph nodes
- typedef CUgraphExec_st * cudaGraphExec_t
-
CUDA executable (launchable) graph
- typedef CUgraphNode_st * cudaGraphNode_t
-
CUDA graph node.
- typedef CUgraph_st * cudaGraph_t
-
CUDA graph
- typedef cudaGraphicsResource * cudaGraphicsResource_t
-
CUDA graphics resource types
- void(CUDART_CB* cudaHostFn_t )( void* userData )
-
CUDA host function
- userData
- Argument value passed to the function
- typedef CUkern_st * cudaKernel_t
-
CUDA kernel
- typedef CUmemPoolHandle_st * cudaMemPool_t
-
CUDA memory pool
- typedef cudaMipmappedArray * cudaMipmappedArray_const_t
-
CUDA mipmapped array (as source argument)
- typedef cudaMipmappedArray * cudaMipmappedArray_t
-
CUDA mipmapped array
- typedef CUstream_st * cudaStream_t
-
CUDA stream
- typedef unsigned long long cudaSurfaceObject_t
-
An opaque value that represents a CUDA Surface object
- typedef unsigned long long cudaTextureObject_t
-
An opaque value that represents a CUDA texture object
- typedef CUuserObject_st * cudaUserObject_t
-
CUDA user object for graphs
Parameters
Enumerations
- enum cudaAccessProperty
-
Specifies performance hint with cudaAccessPolicyWindow for hitProp and missProp members.
Values
- cudaAccessPropertyNormal = 0
- Normal cache persistence.
- cudaAccessPropertyStreaming = 1
- Streaming access is less likely to persit from cache.
- cudaAccessPropertyPersisting = 2
- Persisting access is more likely to persist in cache.
- enum cudaCGScope
-
CUDA cooperative group scope
Values
- cudaCGScopeInvalid = 0
- Invalid cooperative group scope
- cudaCGScopeGrid = 1
- Scope represented by a grid_group
- cudaCGScopeMultiGrid = 2
- Scope represented by a multi_grid_group
- enum cudaChannelFormatKind
-
Channel format kind
Values
- cudaChannelFormatKindSigned = 0
- Signed channel format
- cudaChannelFormatKindUnsigned = 1
- Unsigned channel format
- cudaChannelFormatKindFloat = 2
- Float channel format
- cudaChannelFormatKindNone = 3
- No channel format
- cudaChannelFormatKindNV12 = 4
- Unsigned 8-bit integers, planar 4:2:0 YUV format
- cudaChannelFormatKindUnsignedNormalized8X1 = 5
- 1 channel unsigned 8-bit normalized integer
- cudaChannelFormatKindUnsignedNormalized8X2 = 6
- 2 channel unsigned 8-bit normalized integer
- cudaChannelFormatKindUnsignedNormalized8X4 = 7
- 4 channel unsigned 8-bit normalized integer
- cudaChannelFormatKindUnsignedNormalized16X1 = 8
- 1 channel unsigned 16-bit normalized integer
- cudaChannelFormatKindUnsignedNormalized16X2 = 9
- 2 channel unsigned 16-bit normalized integer
- cudaChannelFormatKindUnsignedNormalized16X4 = 10
- 4 channel unsigned 16-bit normalized integer
- cudaChannelFormatKindSignedNormalized8X1 = 11
- 1 channel signed 8-bit normalized integer
- cudaChannelFormatKindSignedNormalized8X2 = 12
- 2 channel signed 8-bit normalized integer
- cudaChannelFormatKindSignedNormalized8X4 = 13
- 4 channel signed 8-bit normalized integer
- cudaChannelFormatKindSignedNormalized16X1 = 14
- 1 channel signed 16-bit normalized integer
- cudaChannelFormatKindSignedNormalized16X2 = 15
- 2 channel signed 16-bit normalized integer
- cudaChannelFormatKindSignedNormalized16X4 = 16
- 4 channel signed 16-bit normalized integer
- cudaChannelFormatKindUnsignedBlockCompressed1 = 17
- 4 channel unsigned normalized block-compressed (BC1 compression) format
- cudaChannelFormatKindUnsignedBlockCompressed1SRGB = 18
- 4 channel unsigned normalized block-compressed (BC1 compression) format with sRGB encoding
- cudaChannelFormatKindUnsignedBlockCompressed2 = 19
- 4 channel unsigned normalized block-compressed (BC2 compression) format
- cudaChannelFormatKindUnsignedBlockCompressed2SRGB = 20
- 4 channel unsigned normalized block-compressed (BC2 compression) format with sRGB encoding
- cudaChannelFormatKindUnsignedBlockCompressed3 = 21
- 4 channel unsigned normalized block-compressed (BC3 compression) format
- cudaChannelFormatKindUnsignedBlockCompressed3SRGB = 22
- 4 channel unsigned normalized block-compressed (BC3 compression) format with sRGB encoding
- cudaChannelFormatKindUnsignedBlockCompressed4 = 23
- 1 channel unsigned normalized block-compressed (BC4 compression) format
- cudaChannelFormatKindSignedBlockCompressed4 = 24
- 1 channel signed normalized block-compressed (BC4 compression) format
- cudaChannelFormatKindUnsignedBlockCompressed5 = 25
- 2 channel unsigned normalized block-compressed (BC5 compression) format
- cudaChannelFormatKindSignedBlockCompressed5 = 26
- 2 channel signed normalized block-compressed (BC5 compression) format
- cudaChannelFormatKindUnsignedBlockCompressed6H = 27
- 3 channel unsigned half-float block-compressed (BC6H compression) format
- cudaChannelFormatKindSignedBlockCompressed6H = 28
- 3 channel signed half-float block-compressed (BC6H compression) format
- cudaChannelFormatKindUnsignedBlockCompressed7 = 29
- 4 channel unsigned normalized block-compressed (BC7 compression) format
- cudaChannelFormatKindUnsignedBlockCompressed7SRGB = 30
- 4 channel unsigned normalized block-compressed (BC7 compression) format with sRGB encoding
- enum cudaClusterSchedulingPolicy
-
Cluster scheduling policies. These may be passed to cudaFuncSetAttribute
Values
- cudaClusterSchedulingPolicyDefault = 0
- the default policy
- cudaClusterSchedulingPolicySpread = 1
- spread the blocks within a cluster to the SMs
- cudaClusterSchedulingPolicyLoadBalancing = 2
- allow the hardware to load-balance the blocks in a cluster to the SMs
- enum cudaComputeMode
-
CUDA device compute modes
Values
- cudaComputeModeDefault = 0
- Default compute mode (Multiple threads can use cudaSetDevice() with this device)
- cudaComputeModeExclusive = 1
- Compute-exclusive-thread mode (Only one thread in one process will be able to use cudaSetDevice() with this device)
- cudaComputeModeProhibited = 2
- Compute-prohibited mode (No threads can use cudaSetDevice() with this device)
- cudaComputeModeExclusiveProcess = 3
- Compute-exclusive-process mode (Many threads in one process will be able to use cudaSetDevice() with this device)
- enum cudaDeviceAttr
-
CUDA device attributes
Values
- cudaDevAttrMaxThreadsPerBlock = 1
- Maximum number of threads per block
- cudaDevAttrMaxBlockDimX = 2
- Maximum block dimension X
- cudaDevAttrMaxBlockDimY = 3
- Maximum block dimension Y
- cudaDevAttrMaxBlockDimZ = 4
- Maximum block dimension Z
- cudaDevAttrMaxGridDimX = 5
- Maximum grid dimension X
- cudaDevAttrMaxGridDimY = 6
- Maximum grid dimension Y
- cudaDevAttrMaxGridDimZ = 7
- Maximum grid dimension Z
- cudaDevAttrMaxSharedMemoryPerBlock = 8
- Maximum shared memory available per block in bytes
- cudaDevAttrTotalConstantMemory = 9
- Memory available on device for __constant__ variables in a CUDA C kernel in bytes
- cudaDevAttrWarpSize = 10
- Warp size in threads
- cudaDevAttrMaxPitch = 11
- Maximum pitch in bytes allowed by memory copies
- cudaDevAttrMaxRegistersPerBlock = 12
- Maximum number of 32-bit registers available per block
- cudaDevAttrClockRate = 13
- Peak clock frequency in kilohertz
- cudaDevAttrTextureAlignment = 14
- Alignment requirement for textures
- cudaDevAttrGpuOverlap = 15
- Device can possibly copy memory and execute a kernel concurrently
- cudaDevAttrMultiProcessorCount = 16
- Number of multiprocessors on device
- cudaDevAttrKernelExecTimeout = 17
- Specifies whether there is a run time limit on kernels
- cudaDevAttrIntegrated = 18
- Device is integrated with host memory
- cudaDevAttrCanMapHostMemory = 19
- Device can map host memory into CUDA address space
- cudaDevAttrComputeMode = 20
- Compute mode (See cudaComputeMode for details)
- cudaDevAttrMaxTexture1DWidth = 21
- Maximum 1D texture width
- cudaDevAttrMaxTexture2DWidth = 22
- Maximum 2D texture width
- cudaDevAttrMaxTexture2DHeight = 23
- Maximum 2D texture height
- cudaDevAttrMaxTexture3DWidth = 24
- Maximum 3D texture width
- cudaDevAttrMaxTexture3DHeight = 25
- Maximum 3D texture height
- cudaDevAttrMaxTexture3DDepth = 26
- Maximum 3D texture depth
- cudaDevAttrMaxTexture2DLayeredWidth = 27
- Maximum 2D layered texture width
- cudaDevAttrMaxTexture2DLayeredHeight = 28
- Maximum 2D layered texture height
- cudaDevAttrMaxTexture2DLayeredLayers = 29
- Maximum layers in a 2D layered texture
- cudaDevAttrSurfaceAlignment = 30
- Alignment requirement for surfaces
- cudaDevAttrConcurrentKernels = 31
- Device can possibly execute multiple kernels concurrently
- cudaDevAttrEccEnabled = 32
- Device has ECC support enabled
- cudaDevAttrPciBusId = 33
- PCI bus ID of the device
- cudaDevAttrPciDeviceId = 34
- PCI device ID of the device
- cudaDevAttrTccDriver = 35
- Device is using TCC driver model
- cudaDevAttrMemoryClockRate = 36
- Peak memory clock frequency in kilohertz
- cudaDevAttrGlobalMemoryBusWidth = 37
- Global memory bus width in bits
- cudaDevAttrL2CacheSize = 38
- Size of L2 cache in bytes
- cudaDevAttrMaxThreadsPerMultiProcessor = 39
- Maximum resident threads per multiprocessor
- cudaDevAttrAsyncEngineCount = 40
- Number of asynchronous engines
- cudaDevAttrUnifiedAddressing = 41
- Device shares a unified address space with the host
- cudaDevAttrMaxTexture1DLayeredWidth = 42
- Maximum 1D layered texture width
- cudaDevAttrMaxTexture1DLayeredLayers = 43
- Maximum layers in a 1D layered texture
- cudaDevAttrMaxTexture2DGatherWidth = 45
- Maximum 2D texture width if cudaArrayTextureGather is set
- cudaDevAttrMaxTexture2DGatherHeight = 46
- Maximum 2D texture height if cudaArrayTextureGather is set
- cudaDevAttrMaxTexture3DWidthAlt = 47
- Alternate maximum 3D texture width
- cudaDevAttrMaxTexture3DHeightAlt = 48
- Alternate maximum 3D texture height
- cudaDevAttrMaxTexture3DDepthAlt = 49
- Alternate maximum 3D texture depth
- cudaDevAttrPciDomainId = 50
- PCI domain ID of the device
- cudaDevAttrTexturePitchAlignment = 51
- Pitch alignment requirement for textures
- cudaDevAttrMaxTextureCubemapWidth = 52
- Maximum cubemap texture width/height
- cudaDevAttrMaxTextureCubemapLayeredWidth = 53
- Maximum cubemap layered texture width/height
- cudaDevAttrMaxTextureCubemapLayeredLayers = 54
- Maximum layers in a cubemap layered texture
- cudaDevAttrMaxSurface1DWidth = 55
- Maximum 1D surface width
- cudaDevAttrMaxSurface2DWidth = 56
- Maximum 2D surface width
- cudaDevAttrMaxSurface2DHeight = 57
- Maximum 2D surface height
- cudaDevAttrMaxSurface3DWidth = 58
- Maximum 3D surface width
- cudaDevAttrMaxSurface3DHeight = 59
- Maximum 3D surface height
- cudaDevAttrMaxSurface3DDepth = 60
- Maximum 3D surface depth
- cudaDevAttrMaxSurface1DLayeredWidth = 61
- Maximum 1D layered surface width
- cudaDevAttrMaxSurface1DLayeredLayers = 62
- Maximum layers in a 1D layered surface
- cudaDevAttrMaxSurface2DLayeredWidth = 63
- Maximum 2D layered surface width
- cudaDevAttrMaxSurface2DLayeredHeight = 64
- Maximum 2D layered surface height
- cudaDevAttrMaxSurface2DLayeredLayers = 65
- Maximum layers in a 2D layered surface
- cudaDevAttrMaxSurfaceCubemapWidth = 66
- Maximum cubemap surface width
- cudaDevAttrMaxSurfaceCubemapLayeredWidth = 67
- Maximum cubemap layered surface width
- cudaDevAttrMaxSurfaceCubemapLayeredLayers = 68
- Maximum layers in a cubemap layered surface
- cudaDevAttrMaxTexture1DLinearWidth = 69
- Maximum 1D linear texture width
- cudaDevAttrMaxTexture2DLinearWidth = 70
- Maximum 2D linear texture width
- cudaDevAttrMaxTexture2DLinearHeight = 71
- Maximum 2D linear texture height
- cudaDevAttrMaxTexture2DLinearPitch = 72
- Maximum 2D linear texture pitch in bytes
- cudaDevAttrMaxTexture2DMipmappedWidth = 73
- Maximum mipmapped 2D texture width
- cudaDevAttrMaxTexture2DMipmappedHeight = 74
- Maximum mipmapped 2D texture height
- cudaDevAttrComputeCapabilityMajor = 75
- Major compute capability version number
- cudaDevAttrComputeCapabilityMinor = 76
- Minor compute capability version number
- cudaDevAttrMaxTexture1DMipmappedWidth = 77
- Maximum mipmapped 1D texture width
- cudaDevAttrStreamPrioritiesSupported = 78
- Device supports stream priorities
- cudaDevAttrGlobalL1CacheSupported = 79
- Device supports caching globals in L1
- cudaDevAttrLocalL1CacheSupported = 80
- Device supports caching locals in L1
- cudaDevAttrMaxSharedMemoryPerMultiprocessor = 81
- Maximum shared memory available per multiprocessor in bytes
- cudaDevAttrMaxRegistersPerMultiprocessor = 82
- Maximum number of 32-bit registers available per multiprocessor
- cudaDevAttrManagedMemory = 83
- Device can allocate managed memory on this system
- cudaDevAttrIsMultiGpuBoard = 84
- Device is on a multi-GPU board
- cudaDevAttrMultiGpuBoardGroupID = 85
- Unique identifier for a group of devices on the same multi-GPU board
- cudaDevAttrHostNativeAtomicSupported = 86
- Link between the device and the host supports native atomic operations
- cudaDevAttrSingleToDoublePrecisionPerfRatio = 87
- Ratio of single precision performance (in floating-point operations per second) to double precision performance
- cudaDevAttrPageableMemoryAccess = 88
- Device supports coherently accessing pageable memory without calling cudaHostRegister on it
- cudaDevAttrConcurrentManagedAccess = 89
- Device can coherently access managed memory concurrently with the CPU
- cudaDevAttrComputePreemptionSupported = 90
- Device supports Compute Preemption
- cudaDevAttrCanUseHostPointerForRegisteredMem = 91
- Device can access host registered memory at the same virtual address as the CPU
- cudaDevAttrReserved92 = 92
- cudaDevAttrReserved93 = 93
- cudaDevAttrReserved94 = 94
- cudaDevAttrCooperativeLaunch = 95
- Device supports launching cooperative kernels via cudaLaunchCooperativeKernel
- cudaDevAttrCooperativeMultiDeviceLaunch = 96
- Deprecated, cudaLaunchCooperativeKernelMultiDevice is deprecated.
- cudaDevAttrMaxSharedMemoryPerBlockOptin = 97
- The maximum optin shared memory per block. This value may vary by chip. See cudaFuncSetAttribute
- cudaDevAttrCanFlushRemoteWrites = 98
- Device supports flushing of outstanding remote writes.
- cudaDevAttrHostRegisterSupported = 99
- Device supports host memory registration via cudaHostRegister.
- cudaDevAttrPageableMemoryAccessUsesHostPageTables = 100
- Device accesses pageable memory via the host's page tables.
- cudaDevAttrDirectManagedMemAccessFromHost = 101
- Host can directly access managed memory on the device without migration.
- cudaDevAttrMaxBlocksPerMultiprocessor = 106
- Maximum number of blocks per multiprocessor
- cudaDevAttrMaxPersistingL2CacheSize = 108
- Maximum L2 persisting lines capacity setting in bytes.
- cudaDevAttrMaxAccessPolicyWindowSize = 109
- Maximum value of cudaAccessPolicyWindow::num_bytes.
- cudaDevAttrReservedSharedMemoryPerBlock = 111
- Shared memory reserved by CUDA driver per block in bytes
- cudaDevAttrSparseCudaArraySupported = 112
- Device supports sparse CUDA arrays and sparse CUDA mipmapped arrays
- cudaDevAttrHostRegisterReadOnlySupported = 113
- Device supports using the cudaHostRegister flag cudaHostRegisterReadOnly to register memory that must be mapped as read-only to the GPU
- cudaDevAttrTimelineSemaphoreInteropSupported = 114
- External timeline semaphore interop is supported on the device
- cudaDevAttrMaxTimelineSemaphoreInteropSupported = 114
- Deprecated, External timeline semaphore interop is supported on the device
- cudaDevAttrMemoryPoolsSupported = 115
- Device supports using the cudaMallocAsync and cudaMemPool family of APIs
- cudaDevAttrGPUDirectRDMASupported = 116
- Device supports GPUDirect RDMA APIs, like nvidia_p2p_get_pages (see https://docs.nvidia.com/cuda/gpudirect-rdma for more information)
- cudaDevAttrGPUDirectRDMAFlushWritesOptions = 117
- The returned attribute shall be interpreted as a bitmask, where the individual bits are listed in the cudaFlushGPUDirectRDMAWritesOptions enum
- cudaDevAttrGPUDirectRDMAWritesOrdering = 118
- GPUDirect RDMA writes to the device do not need to be flushed for consumers within the scope indicated by the returned attribute. See cudaGPUDirectRDMAWritesOrdering for the numerical values returned here.
- cudaDevAttrMemoryPoolSupportedHandleTypes = 119
- Handle types supported with mempool based IPC
- cudaDevAttrClusterLaunch = 120
- Indicates device supports cluster launch
- cudaDevAttrDeferredMappingCudaArraySupported = 121
- Device supports deferred mapping CUDA arrays and CUDA mipmapped arrays
- cudaDevAttrReserved122 = 122
- cudaDevAttrReserved123 = 123
- cudaDevAttrReserved124 = 124
- cudaDevAttrIpcEventSupport = 125
- Device supports IPC Events.
- cudaDevAttrMemSyncDomainCount = 126
- Number of memory synchronization domains the device supports.
- cudaDevAttrReserved127 = 127
- cudaDevAttrReserved128 = 128
- cudaDevAttrReserved129 = 129
- cudaDevAttrNumaConfig = 130
- NUMA configuration of a device: value is of type cudaDeviceNumaConfig enum
- cudaDevAttrNumaId = 131
- NUMA node ID of the GPU memory
- cudaDevAttrReserved132 = 132
- cudaDevAttrMpsEnabled = 133
- Contexts created on this device will be shared via MPS
- cudaDevAttrHostNumaId = 134
- NUMA ID of the host node closest to the device. Returns -1 when system does not support NUMA.
- cudaDevAttrMax
- enum cudaDeviceP2PAttr
-
CUDA device P2P attributes
Values
- cudaDevP2PAttrPerformanceRank = 1
- A relative value indicating the performance of the link between two devices
- cudaDevP2PAttrAccessSupported = 2
- Peer access is enabled
- cudaDevP2PAttrNativeAtomicSupported = 3
- Native atomic operation over the link supported
- cudaDevP2PAttrCudaArrayAccessSupported = 4
- Accessing CUDA arrays over the link supported
- enum cudaDriverEntryPointQueryResult
-
Enum for status from obtaining driver entry points, used with cudaApiGetDriverEntryPoint
Values
- cudaDriverEntryPointSuccess = 0
- Search for symbol found a match
- cudaDriverEntryPointSymbolNotFound = 1
- Search for symbol was not found
- cudaDriverEntryPointVersionNotSufficent = 2
- Search for symbol was found but version wasn't great enough
- enum cudaEglColorFormat
-
CUDA EGL Color Format - The different planar and multiplanar formats currently supported for CUDA_EGL interops.
Values
- cudaEglColorFormatYUV420Planar = 0
- Y, U, V in three surfaces, each in a separate surface, U/V width = 1/2 Y width, U/V height = 1/2 Y height.
- cudaEglColorFormatYUV420SemiPlanar = 1
- Y, UV in two surfaces (UV as one surface) with VU byte ordering, width, height ratio same as YUV420Planar.
- cudaEglColorFormatYUV422Planar = 2
- Y, U, V each in a separate surface, U/V width = 1/2 Y width, U/V height = Y height.
- cudaEglColorFormatYUV422SemiPlanar = 3
- Y, UV in two surfaces with VU byte ordering, width, height ratio same as YUV422Planar.
- cudaEglColorFormatARGB = 6
- R/G/B/A four channels in one surface with BGRA byte ordering.
- cudaEglColorFormatRGBA = 7
- R/G/B/A four channels in one surface with ABGR byte ordering.
- cudaEglColorFormatL = 8
- single luminance channel in one surface.
- cudaEglColorFormatR = 9
- single color channel in one surface.
- cudaEglColorFormatYUV444Planar = 10
- Y, U, V in three surfaces, each in a separate surface, U/V width = Y width, U/V height = Y height.
- cudaEglColorFormatYUV444SemiPlanar = 11
- Y, UV in two surfaces (UV as one surface) with VU byte ordering, width, height ratio same as YUV444Planar.
- cudaEglColorFormatYUYV422 = 12
- Y, U, V in one surface, interleaved as UYVY in one channel.
- cudaEglColorFormatUYVY422 = 13
- Y, U, V in one surface, interleaved as YUYV in one channel.
- cudaEglColorFormatABGR = 14
- R/G/B/A four channels in one surface with RGBA byte ordering.
- cudaEglColorFormatBGRA = 15
- R/G/B/A four channels in one surface with ARGB byte ordering.
- cudaEglColorFormatA = 16
- Alpha color format - one channel in one surface.
- cudaEglColorFormatRG = 17
- R/G color format - two channels in one surface with GR byte ordering
- cudaEglColorFormatAYUV = 18
- Y, U, V, A four channels in one surface, interleaved as VUYA.
- cudaEglColorFormatYVU444SemiPlanar = 19
- Y, VU in two surfaces (VU as one surface) with UV byte ordering, U/V width = Y width, U/V height = Y height.
- cudaEglColorFormatYVU422SemiPlanar = 20
- Y, VU in two surfaces (VU as one surface) with UV byte ordering, U/V width = 1/2 Y width, U/V height = Y height.
- cudaEglColorFormatYVU420SemiPlanar = 21
- Y, VU in two surfaces (VU as one surface) with UV byte ordering, U/V width = 1/2 Y width, U/V height = 1/2 Y height.
- cudaEglColorFormatY10V10U10_444SemiPlanar = 22
- Y10, V10U10 in two surfaces (VU as one surface) with UV byte ordering, U/V width = Y width, U/V height = Y height.
- cudaEglColorFormatY10V10U10_420SemiPlanar = 23
- Y10, V10U10 in two surfaces (VU as one surface) with UV byte ordering, U/V width = 1/2 Y width, U/V height = 1/2 Y height.
- cudaEglColorFormatY12V12U12_444SemiPlanar = 24
- Y12, V12U12 in two surfaces (VU as one surface) with UV byte ordering, U/V width = Y width, U/V height = Y height.
- cudaEglColorFormatY12V12U12_420SemiPlanar = 25
- Y12, V12U12 in two surfaces (VU as one surface) with UV byte ordering, U/V width = 1/2 Y width, U/V height = 1/2 Y height.
- cudaEglColorFormatVYUY_ER = 26
- Extended Range Y, U, V in one surface, interleaved as YVYU in one channel.
- cudaEglColorFormatUYVY_ER = 27
- Extended Range Y, U, V in one surface, interleaved as YUYV in one channel.
- cudaEglColorFormatYUYV_ER = 28
- Extended Range Y, U, V in one surface, interleaved as UYVY in one channel.
- cudaEglColorFormatYVYU_ER = 29
- Extended Range Y, U, V in one surface, interleaved as VYUY in one channel.
- cudaEglColorFormatYUVA_ER = 31
- Extended Range Y, U, V, A four channels in one surface, interleaved as AVUY.
- cudaEglColorFormatAYUV_ER = 32
- Extended Range Y, U, V, A four channels in one surface, interleaved as VUYA.
- cudaEglColorFormatYUV444Planar_ER = 33
- Extended Range Y, U, V in three surfaces, U/V width = Y width, U/V height = Y height.
- cudaEglColorFormatYUV422Planar_ER = 34
- Extended Range Y, U, V in three surfaces, U/V width = 1/2 Y width, U/V height = Y height.
- cudaEglColorFormatYUV420Planar_ER = 35
- Extended Range Y, U, V in three surfaces, U/V width = 1/2 Y width, U/V height = 1/2 Y height.
- cudaEglColorFormatYUV444SemiPlanar_ER = 36
- Extended Range Y, UV in two surfaces (UV as one surface) with VU byte ordering, U/V width = Y width, U/V height = Y height.
- cudaEglColorFormatYUV422SemiPlanar_ER = 37
- Extended Range Y, UV in two surfaces (UV as one surface) with VU byte ordering, U/V width = 1/2 Y width, U/V height = Y height.
- cudaEglColorFormatYUV420SemiPlanar_ER = 38
- Extended Range Y, UV in two surfaces (UV as one surface) with VU byte ordering, U/V width = 1/2 Y width, U/V height = 1/2 Y height.
- cudaEglColorFormatYVU444Planar_ER = 39
- Extended Range Y, V, U in three surfaces, U/V width = Y width, U/V height = Y height.
- cudaEglColorFormatYVU422Planar_ER = 40
- Extended Range Y, V, U in three surfaces, U/V width = 1/2 Y width, U/V height = Y height.
- cudaEglColorFormatYVU420Planar_ER = 41
- Extended Range Y, V, U in three surfaces, U/V width = 1/2 Y width, U/V height = 1/2 Y height.
- cudaEglColorFormatYVU444SemiPlanar_ER = 42
- Extended Range Y, VU in two surfaces (VU as one surface) with UV byte ordering, U/V width = Y width, U/V height = Y height.
- cudaEglColorFormatYVU422SemiPlanar_ER = 43
- Extended Range Y, VU in two surfaces (VU as one surface) with UV byte ordering, U/V width = 1/2 Y width, U/V height = Y height.
- cudaEglColorFormatYVU420SemiPlanar_ER = 44
- Extended Range Y, VU in two surfaces (VU as one surface) with UV byte ordering, U/V width = 1/2 Y width, U/V height = 1/2 Y height.
- cudaEglColorFormatBayerRGGB = 45
- Bayer format - one channel in one surface with interleaved RGGB ordering.
- cudaEglColorFormatBayerBGGR = 46
- Bayer format - one channel in one surface with interleaved BGGR ordering.
- cudaEglColorFormatBayerGRBG = 47
- Bayer format - one channel in one surface with interleaved GRBG ordering.
- cudaEglColorFormatBayerGBRG = 48
- Bayer format - one channel in one surface with interleaved GBRG ordering.
- cudaEglColorFormatBayer10RGGB = 49
- Bayer10 format - one channel in one surface with interleaved RGGB ordering. Out of 16 bits, 10 bits used 6 bits No-op.
- cudaEglColorFormatBayer10BGGR = 50
- Bayer10 format - one channel in one surface with interleaved BGGR ordering. Out of 16 bits, 10 bits used 6 bits No-op.
- cudaEglColorFormatBayer10GRBG = 51
- Bayer10 format - one channel in one surface with interleaved GRBG ordering. Out of 16 bits, 10 bits used 6 bits No-op.
- cudaEglColorFormatBayer10GBRG = 52
- Bayer10 format - one channel in one surface with interleaved GBRG ordering. Out of 16 bits, 10 bits used 6 bits No-op.
- cudaEglColorFormatBayer12RGGB = 53
- Bayer12 format - one channel in one surface with interleaved RGGB ordering. Out of 16 bits, 12 bits used 4 bits No-op.
- cudaEglColorFormatBayer12BGGR = 54
- Bayer12 format - one channel in one surface with interleaved BGGR ordering. Out of 16 bits, 12 bits used 4 bits No-op.
- cudaEglColorFormatBayer12GRBG = 55
- Bayer12 format - one channel in one surface with interleaved GRBG ordering. Out of 16 bits, 12 bits used 4 bits No-op.
- cudaEglColorFormatBayer12GBRG = 56
- Bayer12 format - one channel in one surface with interleaved GBRG ordering. Out of 16 bits, 12 bits used 4 bits No-op.
- cudaEglColorFormatBayer14RGGB = 57
- Bayer14 format - one channel in one surface with interleaved RGGB ordering. Out of 16 bits, 14 bits used 2 bits No-op.
- cudaEglColorFormatBayer14BGGR = 58
- Bayer14 format - one channel in one surface with interleaved BGGR ordering. Out of 16 bits, 14 bits used 2 bits No-op.
- cudaEglColorFormatBayer14GRBG = 59
- Bayer14 format - one channel in one surface with interleaved GRBG ordering. Out of 16 bits, 14 bits used 2 bits No-op.
- cudaEglColorFormatBayer14GBRG = 60
- Bayer14 format - one channel in one surface with interleaved GBRG ordering. Out of 16 bits, 14 bits used 2 bits No-op.
- cudaEglColorFormatBayer20RGGB = 61
- Bayer20 format - one channel in one surface with interleaved RGGB ordering. Out of 32 bits, 20 bits used 12 bits No-op.
- cudaEglColorFormatBayer20BGGR = 62
- Bayer20 format - one channel in one surface with interleaved BGGR ordering. Out of 32 bits, 20 bits used 12 bits No-op.
- cudaEglColorFormatBayer20GRBG = 63
- Bayer20 format - one channel in one surface with interleaved GRBG ordering. Out of 32 bits, 20 bits used 12 bits No-op.
- cudaEglColorFormatBayer20GBRG = 64
- Bayer20 format - one channel in one surface with interleaved GBRG ordering. Out of 32 bits, 20 bits used 12 bits No-op.
- cudaEglColorFormatYVU444Planar = 65
- Y, V, U in three surfaces, each in a separate surface, U/V width = Y width, U/V height = Y height.
- cudaEglColorFormatYVU422Planar = 66
- Y, V, U in three surfaces, each in a separate surface, U/V width = 1/2 Y width, U/V height = Y height.
- cudaEglColorFormatYVU420Planar = 67
- Y, V, U in three surfaces, each in a separate surface, U/V width = 1/2 Y width, U/V height = 1/2 Y height.
- cudaEglColorFormatBayerIspRGGB = 68
- Nvidia proprietary Bayer ISP format - one channel in one surface with interleaved RGGB ordering and mapped to opaque integer datatype.
- cudaEglColorFormatBayerIspBGGR = 69
- Nvidia proprietary Bayer ISP format - one channel in one surface with interleaved BGGR ordering and mapped to opaque integer datatype.
- cudaEglColorFormatBayerIspGRBG = 70
- Nvidia proprietary Bayer ISP format - one channel in one surface with interleaved GRBG ordering and mapped to opaque integer datatype.
- cudaEglColorFormatBayerIspGBRG = 71
- Nvidia proprietary Bayer ISP format - one channel in one surface with interleaved GBRG ordering and mapped to opaque integer datatype.
- cudaEglColorFormatBayerBCCR = 72
- Bayer format - one channel in one surface with interleaved BCCR ordering.
- cudaEglColorFormatBayerRCCB = 73
- Bayer format - one channel in one surface with interleaved RCCB ordering.
- cudaEglColorFormatBayerCRBC = 74
- Bayer format - one channel in one surface with interleaved CRBC ordering.
- cudaEglColorFormatBayerCBRC = 75
- Bayer format - one channel in one surface with interleaved CBRC ordering.
- cudaEglColorFormatBayer10CCCC = 76
- Bayer10 format - one channel in one surface with interleaved CCCC ordering. Out of 16 bits, 10 bits used 6 bits No-op.
- cudaEglColorFormatBayer12BCCR = 77
- Bayer12 format - one channel in one surface with interleaved BCCR ordering. Out of 16 bits, 12 bits used 4 bits No-op.
- cudaEglColorFormatBayer12RCCB = 78
- Bayer12 format - one channel in one surface with interleaved RCCB ordering. Out of 16 bits, 12 bits used 4 bits No-op.
- cudaEglColorFormatBayer12CRBC = 79
- Bayer12 format - one channel in one surface with interleaved CRBC ordering. Out of 16 bits, 12 bits used 4 bits No-op.
- cudaEglColorFormatBayer12CBRC = 80
- Bayer12 format - one channel in one surface with interleaved CBRC ordering. Out of 16 bits, 12 bits used 4 bits No-op.
- cudaEglColorFormatBayer12CCCC = 81
- Bayer12 format - one channel in one surface with interleaved CCCC ordering. Out of 16 bits, 12 bits used 4 bits No-op.
- cudaEglColorFormatY = 82
- Color format for single Y plane.
- cudaEglColorFormatYUV420SemiPlanar_2020 = 83
- Y, UV in two surfaces (UV as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height.
- cudaEglColorFormatYVU420SemiPlanar_2020 = 84
- Y, VU in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height.
- cudaEglColorFormatYUV420Planar_2020 = 85
- Y, U, V in three surfaces, each in a separate surface, U/V width = 1/2 Y width, U/V height = 1/2 Y height.
- cudaEglColorFormatYVU420Planar_2020 = 86
- Y, V, U in three surfaces, each in a separate surface, U/V width = 1/2 Y width, U/V height = 1/2 Y height.
- cudaEglColorFormatYUV420SemiPlanar_709 = 87
- Y, UV in two surfaces (UV as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height.
- cudaEglColorFormatYVU420SemiPlanar_709 = 88
- Y, VU in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height.
- cudaEglColorFormatYUV420Planar_709 = 89
- Y, U, V in three surfaces, each in a separate surface, U/V width = 1/2 Y width, U/V height = 1/2 Y height.
- cudaEglColorFormatYVU420Planar_709 = 90
- Y, V, U in three surfaces, each in a separate surface, U/V width = 1/2 Y width, U/V height = 1/2 Y height.
- cudaEglColorFormatY10V10U10_420SemiPlanar_709 = 91
- Y10, V10U10 in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height.
- cudaEglColorFormatY10V10U10_420SemiPlanar_2020 = 92
- Y10, V10U10 in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height.
- cudaEglColorFormatY10V10U10_422SemiPlanar_2020 = 93
- Y10, V10U10 in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height = Y height.
- cudaEglColorFormatY10V10U10_422SemiPlanar = 94
- Y10, V10U10 in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height = Y height.
- cudaEglColorFormatY10V10U10_422SemiPlanar_709 = 95
- Y10, V10U10 in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height = Y height.
- cudaEglColorFormatY_ER = 96
- Extended Range Color format for single Y plane.
- cudaEglColorFormatY_709_ER = 97
- Extended Range Color format for single Y plane.
- cudaEglColorFormatY10_ER = 98
- Extended Range Color format for single Y10 plane.
- cudaEglColorFormatY10_709_ER = 99
- Extended Range Color format for single Y10 plane.
- cudaEglColorFormatY12_ER = 100
- Extended Range Color format for single Y12 plane.
- cudaEglColorFormatY12_709_ER = 101
- Extended Range Color format for single Y12 plane.
- cudaEglColorFormatYUVA = 102
- Y, U, V, A four channels in one surface, interleaved as AVUY.
- cudaEglColorFormatYVYU = 104
- Y, U, V in one surface, interleaved as YVYU in one channel.
- cudaEglColorFormatVYUY = 105
- Y, U, V in one surface, interleaved as VYUY in one channel.
- cudaEglColorFormatY10V10U10_420SemiPlanar_ER = 106
- Extended Range Y10, V10U10 in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height.
- cudaEglColorFormatY10V10U10_420SemiPlanar_709_ER = 107
- Extended Range Y10, V10U10 in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height.
- cudaEglColorFormatY10V10U10_444SemiPlanar_ER = 108
- Extended Range Y10, V10U10 in two surfaces (VU as one surface) U/V width = Y width, U/V height = Y height.
- cudaEglColorFormatY10V10U10_444SemiPlanar_709_ER = 109
- Extended Range Y10, V10U10 in two surfaces (VU as one surface) U/V width = Y width, U/V height = Y height.
- cudaEglColorFormatY12V12U12_420SemiPlanar_ER = 110
- Extended Range Y12, V12U12 in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height.
- cudaEglColorFormatY12V12U12_420SemiPlanar_709_ER = 111
- Extended Range Y12, V12U12 in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height.
- cudaEglColorFormatY12V12U12_444SemiPlanar_ER = 112
- Extended Range Y12, V12U12 in two surfaces (VU as one surface) U/V width = Y width, U/V height = Y height.
- cudaEglColorFormatY12V12U12_444SemiPlanar_709_ER = 113
- Extended Range Y12, V12U12 in two surfaces (VU as one surface) U/V width = Y width, U/V height = Y height.
- enum cudaEglFrameType
-
CUDA EglFrame type - array or pointer
Values
- cudaEglFrameTypeArray = 0
- Frame type CUDA array
- cudaEglFrameTypePitch = 1
- Frame type CUDA pointer
- enum cudaEglResourceLocationFlags
-
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 cudaEglResourceLocationFlags to give a hint about the desired location.
cudaEglResourceLocationSysmem - the frame data is made resident on the system memory to be accessed by CUDA.
cudaEglResourceLocationVidmem - 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.
Values
- cudaEglResourceLocationSysmem = 0x00
- Resource location sysmem
- cudaEglResourceLocationVidmem = 0x01
- Resource location vidmem
- enum cudaError
-
CUDA error types
Values
- cudaSuccess = 0
- The API call returned with no errors. In the case of query calls, this also means that the operation being queried is complete (see cudaEventQuery() and cudaStreamQuery()).
- cudaErrorInvalidValue = 1
- This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
- cudaErrorMemoryAllocation = 2
- The API call failed because it was unable to allocate enough memory or other resources to perform the requested operation.
- cudaErrorInitializationError = 3
- The API call failed because the CUDA driver and runtime could not be initialized.
- cudaErrorCudartUnloading = 4
- This indicates that a CUDA Runtime API call cannot be executed because it is being called during process shut down, at a point in time after CUDA driver has been unloaded.
- cudaErrorProfilerDisabled = 5
- This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
- cudaErrorProfilerNotInitialized = 6
-
Deprecated
This error return is deprecated as of CUDA 5.0. It is no longer an error to attempt to enable/disable the profiling via cudaProfilerStart or cudaProfilerStop without initialization.
- cudaErrorProfilerAlreadyStarted = 7
-
Deprecated
This error return is deprecated as of CUDA 5.0. It is no longer an error to call cudaProfilerStart() when profiling is already enabled.
- cudaErrorProfilerAlreadyStopped = 8
-
Deprecated
This error return is deprecated as of CUDA 5.0. It is no longer an error to call cudaProfilerStop() when profiling is already disabled.
- cudaErrorInvalidConfiguration = 9
- This indicates that a kernel launch is requesting resources that can never be satisfied by the current device. Requesting more shared memory per block than the device supports will trigger this error, as will requesting too many threads or blocks. See cudaDeviceProp for more device limitations.
- cudaErrorInvalidPitchValue = 12
- This indicates that one or more of the pitch-related parameters passed to the API call is not within the acceptable range for pitch.
- cudaErrorInvalidSymbol = 13
- This indicates that the symbol name/identifier passed to the API call is not a valid name or identifier.
- cudaErrorInvalidHostPointer = 16
-
Deprecated
This error return is deprecated as of CUDA 10.1.
This indicates that at least one host pointer passed to the API call is not a valid host pointer.
- cudaErrorInvalidDevicePointer = 17
-
Deprecated
This error return is deprecated as of CUDA 10.1.
This indicates that at least one device pointer passed to the API call is not a valid device pointer.
- cudaErrorInvalidTexture = 18
- This indicates that the texture passed to the API call is not a valid texture.
- cudaErrorInvalidTextureBinding = 19
- This indicates that the texture binding is not valid. This occurs if you call cudaGetTextureAlignmentOffset() with an unbound texture.
- cudaErrorInvalidChannelDescriptor = 20
- This indicates that the channel descriptor passed to the API call is not valid. This occurs if the format is not one of the formats specified by cudaChannelFormatKind, or if one of the dimensions is invalid.
- cudaErrorInvalidMemcpyDirection = 21
- This indicates that the direction of the memcpy passed to the API call is not one of the types specified by cudaMemcpyKind.
- cudaErrorAddressOfConstant = 22
-
Deprecated
This error return is deprecated as of CUDA 3.1. Variables in constant memory may now have their address taken by the runtime via cudaGetSymbolAddress().
This indicated that the user has taken the address of a constant variable, which was forbidden up until the CUDA 3.1 release.
- cudaErrorTextureFetchFailed = 23
-
Deprecated
This error return is deprecated as of CUDA 3.1. Device emulation mode was removed with the CUDA 3.1 release.
This indicated that a texture fetch was not able to be performed. This was previously used for device emulation of texture operations.
- cudaErrorTextureNotBound = 24
-
Deprecated
This error return is deprecated as of CUDA 3.1. Device emulation mode was removed with the CUDA 3.1 release.
This indicated that a texture was not bound for access. This was previously used for device emulation of texture operations.
- cudaErrorSynchronizationError = 25
-
Deprecated
This error return is deprecated as of CUDA 3.1. Device emulation mode was removed with the CUDA 3.1 release.
This indicated that a synchronization operation had failed. This was previously used for some device emulation functions.
- cudaErrorInvalidFilterSetting = 26
- This indicates that a non-float texture was being accessed with linear filtering. This is not supported by CUDA.
- cudaErrorInvalidNormSetting = 27
- This indicates that an attempt was made to read a non-float texture as a normalized float. This is not supported by CUDA.
- cudaErrorMixedDeviceExecution = 28
-
Deprecated
This error return is deprecated as of CUDA 3.1. Device emulation mode was removed with the CUDA 3.1 release.
Mixing of device and device emulation code was not allowed.
- cudaErrorNotYetImplemented = 31
-
Deprecated
This error return is deprecated as of CUDA 4.1.
This indicates that the API call is not yet implemented. Production releases of CUDA will never return this error.
- cudaErrorMemoryValueTooLarge = 32
-
Deprecated
This error return is deprecated as of CUDA 3.1. Device emulation mode was removed with the CUDA 3.1 release.
This indicated that an emulated device pointer exceeded the 32-bit address range.
- cudaErrorStubLibrary = 34
- This indicates that the CUDA driver that the application has loaded is a stub library. Applications that run with the stub rather than a real driver loaded will result in CUDA API returning this error.
- cudaErrorInsufficientDriver = 35
- This indicates that the installed NVIDIA CUDA driver is older than the CUDA runtime library. This is not a supported configuration. Users should install an updated NVIDIA display driver to allow the application to run.
- cudaErrorCallRequiresNewerDriver = 36
- This indicates that the API call requires a newer CUDA driver than the one currently installed. Users should install an updated NVIDIA CUDA driver to allow the API call to succeed.
- cudaErrorInvalidSurface = 37
- This indicates that the surface passed to the API call is not a valid surface.
- cudaErrorDuplicateVariableName = 43
- This indicates that multiple global or constant variables (across separate CUDA source files in the application) share the same string name.
- cudaErrorDuplicateTextureName = 44
- This indicates that multiple textures (across separate CUDA source files in the application) share the same string name.
- cudaErrorDuplicateSurfaceName = 45
- This indicates that multiple surfaces (across separate CUDA source files in the application) share the same string name.
- cudaErrorDevicesUnavailable = 46
- This indicates that all CUDA devices are busy or unavailable at the current time. Devices are often busy/unavailable due to use of cudaComputeModeProhibited, cudaComputeModeExclusiveProcess, or when long running CUDA kernels have filled up the GPU and are blocking new work from starting. They can also be unavailable due to memory constraints on a device that already has active CUDA work being performed.
- cudaErrorIncompatibleDriverContext = 49
- This indicates that the current context is not compatible with this the CUDA Runtime. This can only occur if you are using CUDA Runtime/Driver interoperability and have created an existing Driver context using the driver API. The Driver context may be incompatible either because the Driver context was created using an older version of the API, because the Runtime API call expects a primary driver context and the Driver context is not primary, or because the Driver context has been destroyed. Please see Interactions with the CUDA Driver API" for more information.
- cudaErrorMissingConfiguration = 52
- The device function being invoked (usually via cudaLaunchKernel()) was not previously configured via the cudaConfigureCall() function.
- cudaErrorPriorLaunchFailure = 53
-
Deprecated
This error return is deprecated as of CUDA 3.1. Device emulation mode was removed with the CUDA 3.1 release.
This indicated that a previous kernel launch failed. This was previously used for device emulation of kernel launches.
- cudaErrorLaunchMaxDepthExceeded = 65
- This error indicates that a device runtime grid launch did not occur because the depth of the child grid would exceed the maximum supported number of nested grid launches.
- cudaErrorLaunchFileScopedTex = 66
- This error indicates that a grid launch did not occur because the kernel uses file-scoped textures which are unsupported by the device runtime. Kernels launched via the device runtime only support textures created with the Texture Object API's.
- cudaErrorLaunchFileScopedSurf = 67
- This error indicates that a grid launch did not occur because the kernel uses file-scoped surfaces which are unsupported by the device runtime. Kernels launched via the device runtime only support surfaces created with the Surface Object API's.
- cudaErrorSyncDepthExceeded = 68
- This error indicates that a call to cudaDeviceSynchronize made from the device runtime failed because the call was made at grid depth greater than than either the default (2 levels of grids) or user specified device limit cudaLimitDevRuntimeSyncDepth. To be able to synchronize on launched grids at a greater depth successfully, the maximum nested depth at which cudaDeviceSynchronize will be called must be specified with the cudaLimitDevRuntimeSyncDepth limit to the cudaDeviceSetLimit api before the host-side launch of a kernel using the device runtime. Keep in mind that additional levels of sync depth require the runtime to reserve large amounts of device memory that cannot be used for user allocations. Note that cudaDeviceSynchronize made from device runtime is only supported on devices of compute capability < 9.0.
- cudaErrorLaunchPendingCountExceeded = 69
- This error indicates that a device runtime grid launch failed because the launch would exceed the limit cudaLimitDevRuntimePendingLaunchCount. For this launch to proceed successfully, cudaDeviceSetLimit must be called to set the cudaLimitDevRuntimePendingLaunchCount to be higher than the upper bound of outstanding launches that can be issued to the device runtime. Keep in mind that raising the limit of pending device runtime launches will require the runtime to reserve device memory that cannot be used for user allocations.
- cudaErrorInvalidDeviceFunction = 98
- The requested device function does not exist or is not compiled for the proper device architecture.
- cudaErrorNoDevice = 100
- This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
- cudaErrorInvalidDevice = 101
- This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device or that the action requested is invalid for the specified device.
- cudaErrorDeviceNotLicensed = 102
- This indicates that the device doesn't have a valid Grid License.
- cudaErrorSoftwareValidityNotEstablished = 103
- By default, the CUDA runtime may perform a minimal set of self-tests, as well as CUDA driver tests, to establish the validity of both. Introduced in CUDA 11.2, this error return indicates that at least one of these tests has failed and the validity of either the runtime or the driver could not be established.
- cudaErrorStartupFailure = 127
- This indicates an internal startup failure in the CUDA runtime.
- cudaErrorInvalidKernelImage = 200
- This indicates that the device kernel image is invalid.
- cudaErrorDeviceUninitialized = 201
- This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had cuCtxDestroy() invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls). See cuCtxGetApiVersion() for more details.
- cudaErrorMapBufferObjectFailed = 205
- This indicates that the buffer object could not be mapped.
- cudaErrorUnmapBufferObjectFailed = 206
- This indicates that the buffer object could not be unmapped.
- cudaErrorArrayIsMapped = 207
- This indicates that the specified array is currently mapped and thus cannot be destroyed.
- cudaErrorAlreadyMapped = 208
- This indicates that the resource is already mapped.
- cudaErrorNoKernelImageForDevice = 209
- This indicates that there is no kernel image available that is suitable for the device. This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.
- cudaErrorAlreadyAcquired = 210
- This indicates that a resource has already been acquired.
- cudaErrorNotMapped = 211
- This indicates that a resource is not mapped.
- cudaErrorNotMappedAsArray = 212
- This indicates that a mapped resource is not available for access as an array.
- cudaErrorNotMappedAsPointer = 213
- This indicates that a mapped resource is not available for access as a pointer.
- cudaErrorECCUncorrectable = 214
- This indicates that an uncorrectable ECC error was detected during execution.
- cudaErrorUnsupportedLimit = 215
- This indicates that the cudaLimit passed to the API call is not supported by the active device.
- cudaErrorDeviceAlreadyInUse = 216
- This indicates that a call tried to access an exclusive-thread device that is already in use by a different thread.
- cudaErrorPeerAccessUnsupported = 217
- This error indicates that P2P access is not supported across the given devices.
- cudaErrorInvalidPtx = 218
- A PTX compilation failed. The runtime may fall back to compiling PTX if an application does not contain a suitable binary for the current device.
- cudaErrorInvalidGraphicsContext = 219
- This indicates an error with the OpenGL or DirectX context.
- cudaErrorNvlinkUncorrectable = 220
- This indicates that an uncorrectable NVLink error was detected during the execution.
- cudaErrorJitCompilerNotFound = 221
- This indicates that the PTX JIT compiler library was not found. The JIT Compiler library is used for PTX compilation. The runtime may fall back to compiling PTX if an application does not contain a suitable binary for the current device.
- cudaErrorUnsupportedPtxVersion = 222
- This indicates that the provided PTX was compiled with an unsupported toolchain. The most common reason for this, is the PTX was generated by a compiler newer than what is supported by the CUDA driver and PTX JIT compiler.
- cudaErrorJitCompilationDisabled = 223
- This indicates that the JIT compilation was disabled. The JIT compilation compiles PTX. The runtime may fall back to compiling PTX if an application does not contain a suitable binary for the current device.
- cudaErrorUnsupportedExecAffinity = 224
- This indicates that the provided execution affinity is not supported by the device.
- cudaErrorUnsupportedDevSideSync = 225
- This indicates that the code to be compiled by the PTX JIT contains unsupported call to cudaDeviceSynchronize.
- cudaErrorInvalidSource = 300
- This indicates that the device kernel source is invalid.
- cudaErrorFileNotFound = 301
- This indicates that the file specified was not found.
- cudaErrorSharedObjectSymbolNotFound = 302
- This indicates that a link to a shared object failed to resolve.
- cudaErrorSharedObjectInitFailed = 303
- This indicates that initialization of a shared object failed.
- cudaErrorOperatingSystem = 304
- This error indicates that an OS call failed.
- cudaErrorInvalidResourceHandle = 400
- This indicates that a resource handle passed to the API call was not valid. Resource handles are opaque types like cudaStream_t and cudaEvent_t.
- cudaErrorIllegalState = 401
- This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
- cudaErrorLossyQuery = 402
- This indicates an attempt was made to introspect an object in a way that would discard semantically important information. This is either due to the object using funtionality newer than the API version used to introspect it or omission of optional return arguments.
- cudaErrorSymbolNotFound = 500
- This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, driver function names, texture names, and surface names.
- cudaErrorNotReady = 600
- This indicates that asynchronous operations issued previously have not completed yet. This result is not actually an error, but must be indicated differently than cudaSuccess (which indicates completion). Calls that may return this value include cudaEventQuery() and cudaStreamQuery().
- cudaErrorIllegalAddress = 700
- The device encountered a load or store instruction on an invalid memory address. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.
- cudaErrorLaunchOutOfResources = 701
- This indicates that a launch did not occur because it did not have appropriate resources. Although this error is similar to cudaErrorInvalidConfiguration, this error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count.
- cudaErrorLaunchTimeout = 702
- This indicates that the device kernel took too long to execute. This can only occur if timeouts are enabled - see the device property kernelExecTimeoutEnabled for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.
- cudaErrorLaunchIncompatibleTexturing = 703
- This error indicates a kernel launch that uses an incompatible texturing mode.
- cudaErrorPeerAccessAlreadyEnabled = 704
- This error indicates that a call to cudaDeviceEnablePeerAccess() is trying to re-enable peer addressing on from a context which has already had peer addressing enabled.
- cudaErrorPeerAccessNotEnabled = 705
- This error indicates that cudaDeviceDisablePeerAccess() is trying to disable peer addressing which has not been enabled yet via cudaDeviceEnablePeerAccess().
- cudaErrorSetOnActiveProcess = 708
- This indicates that the user has called cudaSetValidDevices(), cudaSetDeviceFlags(), cudaD3D9SetDirect3DDevice(), cudaD3D10SetDirect3DDevice, cudaD3D11SetDirect3DDevice(), or cudaVDPAUSetVDPAUDevice() after initializing the CUDA runtime by calling non-device management operations (allocating memory and launching kernels are examples of non-device management operations). This error can also be returned if using runtime/driver interoperability and there is an existing CUcontext active on the host thread.
- cudaErrorContextIsDestroyed = 709
- This error indicates that the context current to the calling thread has been destroyed using cuCtxDestroy, or is a primary context which has not yet been initialized.
- cudaErrorAssert = 710
- An assert triggered in device code during kernel execution. The device cannot be used again. All existing allocations are invalid. To continue using CUDA, the process must be terminated and relaunched.
- cudaErrorTooManyPeers = 711
- This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to cudaEnablePeerAccess().
- cudaErrorHostMemoryAlreadyRegistered = 712
- This error indicates that the memory range passed to cudaHostRegister() has already been registered.
- cudaErrorHostMemoryNotRegistered = 713
- This error indicates that the pointer passed to cudaHostUnregister() does not correspond to any currently registered memory region.
- cudaErrorHardwareStackError = 714
- Device encountered an error in the call stack during kernel execution, possibly due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.
- cudaErrorIllegalInstruction = 715
- The device encountered an illegal instruction during kernel execution This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.
- cudaErrorMisalignedAddress = 716
- The device encountered a load or store instruction on a memory address which is not aligned. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.
- cudaErrorInvalidAddressSpace = 717
- While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.
- cudaErrorInvalidPc = 718
- The device encountered an invalid program counter. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.
- cudaErrorLaunchFailure = 719
- An exception occurred on the device while executing a kernel. Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. Less common cases can be system specific - more information about these cases can be found in the system specific user guide. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.
- cudaErrorCooperativeLaunchTooLarge = 720
- This error indicates that the number of blocks launched per grid for a kernel that was launched via either cudaLaunchCooperativeKernel or cudaLaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by cudaOccupancyMaxActiveBlocksPerMultiprocessor or cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute cudaDevAttrMultiProcessorCount.
- cudaErrorNotPermitted = 800
- This error indicates the attempted operation is not permitted.
- cudaErrorNotSupported = 801
- This error indicates the attempted operation is not supported on the current system or device.
- cudaErrorSystemNotReady = 802
- This error indicates that the system is not yet ready to start any CUDA work. To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.
- cudaErrorSystemDriverMismatch = 803
- This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver. Refer to the compatibility documentation for supported versions.
- cudaErrorCompatNotSupportedOnDevice = 804
- This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration. Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.
- cudaErrorMpsConnectionFailed = 805
- This error indicates that the MPS client failed to connect to the MPS control daemon or the MPS server.
- cudaErrorMpsRpcFailure = 806
- This error indicates that the remote procedural call between the MPS server and the MPS client failed.
- cudaErrorMpsServerNotReady = 807
- This error indicates that the MPS server is not ready to accept new MPS client requests. This error can be returned when the MPS server is in the process of recovering from a fatal failure.
- cudaErrorMpsMaxClientsReached = 808
- This error indicates that the hardware resources required to create MPS client have been exhausted.
- cudaErrorMpsMaxConnectionsReached = 809
- This error indicates the the hardware resources required to device connections have been exhausted.
- cudaErrorMpsClientTerminated = 810
- This error indicates that the MPS client has been terminated by the server. To continue using CUDA, the process must be terminated and relaunched.
- cudaErrorCdpNotSupported = 811
- This error indicates, that the program is using CUDA Dynamic Parallelism, but the current configuration, like MPS, does not support it.
- cudaErrorCdpVersionMismatch = 812
- This error indicates, that the program contains an unsupported interaction between different versions of CUDA Dynamic Parallelism.
- cudaErrorStreamCaptureUnsupported = 900
- The operation is not permitted when the stream is capturing.
- cudaErrorStreamCaptureInvalidated = 901
- The current capture sequence on the stream has been invalidated due to a previous error.
- cudaErrorStreamCaptureMerge = 902
- The operation would have resulted in a merge of two independent capture sequences.
- cudaErrorStreamCaptureUnmatched = 903
- The capture was not initiated in this stream.
- cudaErrorStreamCaptureUnjoined = 904
- The capture sequence contains a fork that was not joined to the primary stream.
- cudaErrorStreamCaptureIsolation = 905
- A dependency would have been created which crosses the capture sequence boundary. Only implicit in-stream ordering dependencies are allowed to cross the boundary.
- cudaErrorStreamCaptureImplicit = 906
- The operation would have resulted in a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
- cudaErrorCapturedEvent = 907
- The operation is not permitted on an event which was last recorded in a capturing stream.
- cudaErrorStreamCaptureWrongThread = 908
- A stream capture sequence not initiated with the cudaStreamCaptureModeRelaxed argument to cudaStreamBeginCapture was passed to cudaStreamEndCapture in a different thread.
- cudaErrorTimeout = 909
- This indicates that the wait operation has timed out.
- cudaErrorGraphExecUpdateFailure = 910
- This error indicates that the graph update was not performed because it included changes which violated constraints specific to instantiated graph update.
- cudaErrorExternalDevice = 911
- This indicates that an async error has occurred in a device outside of CUDA. If CUDA was waiting for an external device's signal before consuming shared data, the external device signaled an error indicating that the data is not valid for consumption. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.
- cudaErrorInvalidClusterSize = 912
- This indicates that a kernel launch error has occurred due to cluster misconfiguration.
- cudaErrorUnknown = 999
- This indicates that an unknown internal error has occurred.
- cudaErrorApiFailureBase = 10000
- enum cudaExternalMemoryHandleType
-
External memory handle types
Values
- cudaExternalMemoryHandleTypeOpaqueFd = 1
- Handle is an opaque file descriptor
- cudaExternalMemoryHandleTypeOpaqueWin32 = 2
- Handle is an opaque shared NT handle
- cudaExternalMemoryHandleTypeOpaqueWin32Kmt = 3
- Handle is an opaque, globally shared handle
- cudaExternalMemoryHandleTypeD3D12Heap = 4
- Handle is a D3D12 heap object
- cudaExternalMemoryHandleTypeD3D12Resource = 5
- Handle is a D3D12 committed resource
- cudaExternalMemoryHandleTypeD3D11Resource = 6
- Handle is a shared NT handle to a D3D11 resource
- cudaExternalMemoryHandleTypeD3D11ResourceKmt = 7
- Handle is a globally shared handle to a D3D11 resource
- cudaExternalMemoryHandleTypeNvSciBuf = 8
- Handle is an NvSciBuf object
- enum cudaExternalSemaphoreHandleType
-
External semaphore handle types
Values
- cudaExternalSemaphoreHandleTypeOpaqueFd = 1
- Handle is an opaque file descriptor
- cudaExternalSemaphoreHandleTypeOpaqueWin32 = 2
- Handle is an opaque shared NT handle
- cudaExternalSemaphoreHandleTypeOpaqueWin32Kmt = 3
- Handle is an opaque, globally shared handle
- cudaExternalSemaphoreHandleTypeD3D12Fence = 4
- Handle is a shared NT handle referencing a D3D12 fence object
- cudaExternalSemaphoreHandleTypeD3D11Fence = 5
- Handle is a shared NT handle referencing a D3D11 fence object
- cudaExternalSemaphoreHandleTypeNvSciSync = 6
- Opaque handle to NvSciSync Object
- cudaExternalSemaphoreHandleTypeKeyedMutex = 7
- Handle is a shared NT handle referencing a D3D11 keyed mutex object
- cudaExternalSemaphoreHandleTypeKeyedMutexKmt = 8
- Handle is a shared KMT handle referencing a D3D11 keyed mutex object
- cudaExternalSemaphoreHandleTypeTimelineSemaphoreFd = 9
- Handle is an opaque handle file descriptor referencing a timeline semaphore
- cudaExternalSemaphoreHandleTypeTimelineSemaphoreWin32 = 10
- Handle is an opaque handle file descriptor referencing a timeline semaphore
- enum cudaFlushGPUDirectRDMAWritesOptions
-
CUDA GPUDirect RDMA flush writes APIs supported on the device
Values
- cudaFlushGPUDirectRDMAWritesOptionHost = 1<<0
- cudaDeviceFlushGPUDirectRDMAWrites() and its CUDA Driver API counterpart are supported on the device.
- cudaFlushGPUDirectRDMAWritesOptionMemOps = 1<<1
- The CU_STREAM_WAIT_VALUE_FLUSH flag and the CU_STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the CUDA device.
- enum cudaFlushGPUDirectRDMAWritesScope
-
CUDA GPUDirect RDMA flush writes scopes
Values
- cudaFlushGPUDirectRDMAWritesToOwner = 100
- Blocks until remote writes are visible to the CUDA device context owning the data.
- cudaFlushGPUDirectRDMAWritesToAllDevices = 200
- Blocks until remote writes are visible to all CUDA device contexts.
- enum cudaFlushGPUDirectRDMAWritesTarget
-
CUDA GPUDirect RDMA flush writes targets
Values
- cudaFlushGPUDirectRDMAWritesTargetCurrentDevice
- Sets the target for cudaDeviceFlushGPUDirectRDMAWrites() to the currently active CUDA device context.
- enum cudaFuncAttribute
-
CUDA function attributes that can be set using cudaFuncSetAttribute
Values
- cudaFuncAttributeMaxDynamicSharedMemorySize = 8
- Maximum dynamic shared memory size
- cudaFuncAttributePreferredSharedMemoryCarveout = 9
- Preferred shared memory-L1 cache split
- cudaFuncAttributeClusterDimMustBeSet = 10
- Indicator to enforce valid cluster dimension specification on kernel launch
- cudaFuncAttributeRequiredClusterWidth = 11
- Required cluster width
- cudaFuncAttributeRequiredClusterHeight = 12
- Required cluster height
- cudaFuncAttributeRequiredClusterDepth = 13
- Required cluster depth
- cudaFuncAttributeNonPortableClusterSizeAllowed = 14
- Whether non-portable cluster scheduling policy is supported
- cudaFuncAttributeClusterSchedulingPolicyPreference = 15
- Required cluster scheduling policy preference
- cudaFuncAttributeMax
- enum cudaFuncCache
-
CUDA function cache configurations
Values
- cudaFuncCachePreferNone = 0
- Default function cache configuration, no preference
- cudaFuncCachePreferShared = 1
- Prefer larger shared memory and smaller L1 cache
- cudaFuncCachePreferL1 = 2
- Prefer larger L1 cache and smaller shared memory
- cudaFuncCachePreferEqual = 3
- Prefer equal size L1 cache and shared memory
- enum cudaGPUDirectRDMAWritesOrdering
-
CUDA GPUDirect RDMA flush writes ordering features of the device
Values
- cudaGPUDirectRDMAWritesOrderingNone = 0
- The device does not natively support ordering of GPUDirect RDMA writes. cudaFlushGPUDirectRDMAWrites() can be leveraged if supported.
- cudaGPUDirectRDMAWritesOrderingOwner = 100
- Natively, the device can consistently consume GPUDirect RDMA writes, although other CUDA devices may not.
- cudaGPUDirectRDMAWritesOrderingAllDevices = 200
- Any CUDA device in the system can consistently consume GPUDirect RDMA writes to this device.
- enum cudaGetDriverEntryPointFlags
-
Flags to specify search options to be used with cudaGetDriverEntryPoint For more details see cuGetProcAddress
Values
- cudaEnableDefault = 0x0
- Default search mode for driver symbols.
- cudaEnableLegacyStream = 0x1
- Search for legacy versions of driver symbols.
- cudaEnablePerThreadDefaultStream = 0x2
- Search for per-thread versions of driver symbols.
- enum cudaGraphConditionalNodeType
-
CUDA conditional node types
Values
- cudaGraphCondTypeIf = 0
- Conditional 'if' Node. Body executed once if condition value is non-zero.
- cudaGraphCondTypeWhile = 1
- Conditional 'while' Node. Body executed repeatedly while condition value is non-zero.
- enum cudaGraphDebugDotFlags
-
CUDA Graph debug write options
Values
- cudaGraphDebugDotFlagsVerbose = 1<<0
- Output all debug data as if every debug flag is enabled
- cudaGraphDebugDotFlagsKernelNodeParams = 1<<2
- Adds cudaKernelNodeParams to output
- cudaGraphDebugDotFlagsMemcpyNodeParams = 1<<3
- Adds cudaMemcpy3DParms to output
- cudaGraphDebugDotFlagsMemsetNodeParams = 1<<4
- Adds cudaMemsetParams to output
- cudaGraphDebugDotFlagsHostNodeParams = 1<<5
- Adds cudaHostNodeParams to output
- cudaGraphDebugDotFlagsEventNodeParams = 1<<6
- Adds cudaEvent_t handle from record and wait nodes to output
- cudaGraphDebugDotFlagsExtSemasSignalNodeParams = 1<<7
- Adds cudaExternalSemaphoreSignalNodeParams values to output
- cudaGraphDebugDotFlagsExtSemasWaitNodeParams = 1<<8
- Adds cudaExternalSemaphoreWaitNodeParams to output
- cudaGraphDebugDotFlagsKernelNodeAttributes = 1<<9
- Adds cudaKernelNodeAttrID values to output
- cudaGraphDebugDotFlagsHandles = 1<<10
- Adds node handles and every kernel function handle to output
- cudaGraphDebugDotFlagsConditionalNodeParams = 1<<15
- Adds cudaConditionalNodeParams to output
- enum cudaGraphDependencyType
-
Type annotations that can be applied to graph edges as part of cudaGraphEdgeData.
Values
- cudaGraphDependencyTypeDefault = 0
- This is an ordinary dependency.
- cudaGraphDependencyTypeProgrammatic = 1
- This dependency type allows the downstream node to use cudaGridDependencySynchronize(). It may only be used between kernel nodes, and must be used with either the cudaGraphKernelNodePortProgrammatic or cudaGraphKernelNodePortLaunchCompletion outgoing port.
- enum cudaGraphExecUpdateResult
-
CUDA Graph Update error types
Values
- cudaGraphExecUpdateSuccess = 0x0
- The update succeeded
- cudaGraphExecUpdateError = 0x1
- The update failed for an unexpected reason which is described in the return value of the function
- cudaGraphExecUpdateErrorTopologyChanged = 0x2
- The update failed because the topology changed
- cudaGraphExecUpdateErrorNodeTypeChanged = 0x3
- The update failed because a node type changed
- cudaGraphExecUpdateErrorFunctionChanged = 0x4
- The update failed because the function of a kernel node changed (CUDA driver < 11.2)
- cudaGraphExecUpdateErrorParametersChanged = 0x5
- The update failed because the parameters changed in a way that is not supported
- cudaGraphExecUpdateErrorNotSupported = 0x6
- The update failed because something about the node is not supported
- cudaGraphExecUpdateErrorUnsupportedFunctionChange = 0x7
- The update failed because the function of a kernel node changed in an unsupported way
- cudaGraphExecUpdateErrorAttributesChanged = 0x8
- The update failed because the node attributes changed in a way that is not supported
- enum cudaGraphInstantiateFlags
-
Flags for instantiating a graph
Values
- cudaGraphInstantiateFlagAutoFreeOnLaunch = 1
- Automatically free memory allocated in a graph before relaunching.
- cudaGraphInstantiateFlagUpload = 2
- Automatically upload the graph after instantiation. Only supported by cudaGraphInstantiateWithParams. The upload will be performed using the stream provided in instantiateParams.
- cudaGraphInstantiateFlagDeviceLaunch = 4
- Instantiate the graph to be launchable from the device. This flag can only be used on platforms which support unified addressing. This flag cannot be used in conjunction with cudaGraphInstantiateFlagAutoFreeOnLaunch.
- cudaGraphInstantiateFlagUseNodePriority = 8
- Run the graph using the per-node priority attributes rather than the priority of the stream it is launched into.
- enum cudaGraphInstantiateResult
-
Graph instantiation results
Values
- cudaGraphInstantiateSuccess = 0
- Instantiation succeeded
- cudaGraphInstantiateError = 1
- Instantiation failed for an unexpected reason which is described in the return value of the function
- cudaGraphInstantiateInvalidStructure = 2
- Instantiation failed due to invalid structure, such as cycles
- cudaGraphInstantiateNodeOperationNotSupported = 3
- Instantiation for device launch failed because the graph contained an unsupported operation
- cudaGraphInstantiateMultipleDevicesNotSupported = 4
- Instantiation for device launch failed due to the nodes belonging to different contexts
- enum cudaGraphMemAttributeType
-
Graph memory attributes
Values
- cudaGraphMemAttrUsedMemCurrent = 0x0
- (value type = cuuint64_t) Amount of memory, in bytes, currently associated with graphs.
- cudaGraphMemAttrUsedMemHigh = 0x1
- (value type = cuuint64_t) High watermark of memory, in bytes, associated with graphs since the last time it was reset. High watermark can only be reset to zero.
- cudaGraphMemAttrReservedMemCurrent = 0x2
- (value type = cuuint64_t) Amount of memory, in bytes, currently allocated for use by the CUDA graphs asynchronous allocator.
- cudaGraphMemAttrReservedMemHigh = 0x3
- (value type = cuuint64_t) High watermark of memory, in bytes, currently allocated for use by the CUDA graphs asynchronous allocator.
- enum cudaGraphNodeType
-
CUDA Graph node types
Values
- cudaGraphNodeTypeKernel = 0x00
- GPU kernel node
- cudaGraphNodeTypeMemcpy = 0x01
- Memcpy node
- cudaGraphNodeTypeMemset = 0x02
- Memset node
- cudaGraphNodeTypeHost = 0x03
- Host (executable) node
- cudaGraphNodeTypeGraph = 0x04
- Node which executes an embedded graph
- cudaGraphNodeTypeEmpty = 0x05
- Empty (no-op) node
- cudaGraphNodeTypeWaitEvent = 0x06
- External event wait node
- cudaGraphNodeTypeEventRecord = 0x07
- External event record node
- cudaGraphNodeTypeExtSemaphoreSignal = 0x08
- External semaphore signal node
- cudaGraphNodeTypeExtSemaphoreWait = 0x09
- External semaphore wait node
- cudaGraphNodeTypeMemAlloc = 0x0a
- Memory allocation node
- cudaGraphNodeTypeMemFree = 0x0b
- Memory free node
- cudaGraphNodeTypeConditional = 0x0d
- Conditional nodeMay be used to implement a conditional execution path or loop inside of a graph. The graph(s) contained within the body of the conditional node can be selectively executed or iterated upon based on the value of a conditional variable.Handles must be created in advance of creating the node using cudaGraphConditionalHandleCreate.The following restrictions apply to graphs which contain conditional nodes: 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.To set the control value, supply a default value when creating the handle and/or call cudaGraphSetConditional from device code.
- cudaGraphNodeTypeCount
- enum cudaGraphicsCubeFace
-
CUDA graphics interop array indices for cube maps
Values
- cudaGraphicsCubeFacePositiveX = 0x00
- Positive X face of cubemap
- cudaGraphicsCubeFaceNegativeX = 0x01
- Negative X face of cubemap
- cudaGraphicsCubeFacePositiveY = 0x02
- Positive Y face of cubemap
- cudaGraphicsCubeFaceNegativeY = 0x03
- Negative Y face of cubemap
- cudaGraphicsCubeFacePositiveZ = 0x04
- Positive Z face of cubemap
- cudaGraphicsCubeFaceNegativeZ = 0x05
- Negative Z face of cubemap
- enum cudaGraphicsMapFlags
-
CUDA graphics interop map flags
Values
- cudaGraphicsMapFlagsNone = 0
- Default; Assume resource can be read/written
- cudaGraphicsMapFlagsReadOnly = 1
- CUDA will not write to this resource
- cudaGraphicsMapFlagsWriteDiscard = 2
- CUDA will only write to and will not read from this resource
- enum cudaGraphicsRegisterFlags
-
CUDA graphics interop register flags
Values
- cudaGraphicsRegisterFlagsNone = 0
- Default
- cudaGraphicsRegisterFlagsReadOnly = 1
- CUDA will not write to this resource
- cudaGraphicsRegisterFlagsWriteDiscard = 2
- CUDA will only write to and will not read from this resource
- cudaGraphicsRegisterFlagsSurfaceLoadStore = 4
- CUDA will bind this resource to a surface reference
- cudaGraphicsRegisterFlagsTextureGather = 8
- CUDA will perform texture gather operations on this resource
- enum cudaLaunchAttributeID
-
Launch attributes enum; used as id field of cudaLaunchAttribute
Values
- cudaLaunchAttributeIgnore = 0
- Ignored entry, for convenient composition
- cudaLaunchAttributeAccessPolicyWindow = 1
- Valid for streams, graph nodes, launches. See cudaLaunchAttributeValue::accessPolicyWindow.
- cudaLaunchAttributeCooperative = 2
- Valid for graph nodes, launches. See cudaLaunchAttributeValue::cooperative.
- cudaLaunchAttributeSynchronizationPolicy = 3
- Valid for streams. See cudaLaunchAttributeValue::syncPolicy.
- cudaLaunchAttributeClusterDimension = 4
- Valid for graph nodes, launches. See cudaLaunchAttributeValue::clusterDim.
- cudaLaunchAttributeClusterSchedulingPolicyPreference = 5
- Valid for graph nodes, launches. See cudaLaunchAttributeValue::clusterSchedulingPolicyPreference.
- cudaLaunchAttributeProgrammaticStreamSerialization = 6
- Valid for launches. Setting cudaLaunchAttributeValue::programmaticStreamSerializationAllowed to non-0 signals that the kernel will use programmatic means to resolve its stream dependency, so that the CUDA runtime should opportunistically allow the grid's execution to overlap with the previous kernel in the stream, if that kernel requests the overlap. The dependent launches can choose to wait on the dependency using the programmatic sync (cudaGridDependencySynchronize() or equivalent PTX instructions).
- cudaLaunchAttributeProgrammaticEvent = 7
- Valid for launches. Set cudaLaunchAttributeValue::programmaticEvent to record the event. Event recorded through this launch attribute is guaranteed to only trigger after all block in the associated kernel trigger the event. A block can trigger the event programmatically in a future CUDA release. A trigger can also be inserted at the beginning of each block's execution if triggerAtBlockStart is set to non-0. The dependent launches can choose to wait on the dependency using the programmatic sync (cudaGridDependencySynchronize() or equivalent PTX instructions). Note that dependents (including the CPU thread calling cudaEventSynchronize()) are not guaranteed to observe the release precisely when it is released. For example, cudaEventSynchronize() may only observe the event trigger long after the associated kernel has completed. This recording type is primarily meant for establishing programmatic dependency between device tasks. Note also this type of dependency allows, but does not guarantee, concurrent execution of tasks. The event supplied must not be an interprocess or interop event. The event must disable timing (i.e. must be created with the cudaEventDisableTiming flag set).
- cudaLaunchAttributePriority = 8
- Valid for streams, graph nodes, launches. See cudaLaunchAttributeValue::priority.
- cudaLaunchAttributeMemSyncDomainMap = 9
- Valid for streams, graph nodes, launches. See cudaLaunchAttributeValue::memSyncDomainMap.
- cudaLaunchAttributeMemSyncDomain = 10
- Valid for streams, graph nodes, launches. See cudaLaunchAttributeValue::memSyncDomain.
- cudaLaunchAttributeLaunchCompletionEvent = 12
- Valid for launches. Set cudaLaunchAttributeValue::launchCompletionEvent to record the event. Nominally, the event is triggered once all blocks of the kernel have begun execution. Currently this is a best effort. If a kernel B has a launch completion dependency on a kernel A, B may wait until A is complete. Alternatively, blocks of B may begin before all blocks of A have begun, for example if B can claim execution resources unavailable to A (e.g. they run on different GPUs) or if B is a higher priority than A. Exercise caution if such an ordering inversion could lead to deadlock. A launch completion event is nominally similar to a programmatic event with triggerAtBlockStart set except that it is not visible to cudaGridDependencySynchronize() and can be used with compute capability less than 9.0. The event supplied must not be an interprocess or interop event. The event must disable timing (i.e. must be created with the cudaEventDisableTiming flag set).
- enum cudaLaunchMemSyncDomain
-
Memory Synchronization Domain
A kernel can be launched in a specified memory synchronization domain that affects all memory operations issued by that kernel. A memory barrier issued in one domain will only order memory operations in that domain, thus eliminating latency increase from memory barriers ordering unrelated traffic.
By default, kernels are launched in domain 0. Kernel launched with cudaLaunchMemSyncDomainRemote will have a different domain ID. User may also alter the domain ID with cudaLaunchMemSyncDomainMap for a specific stream / graph node / kernel launch. See cudaLaunchAttributeMemSyncDomain, cudaStreamSetAttribute, cudaLaunchKernelEx, cudaGraphKernelNodeSetAttribute.
Memory operations done in kernels launched in different domains are considered system-scope distanced. In other words, a GPU scoped memory synchronization is not sufficient for memory order to be observed by kernels in another memory synchronization domain even if they are on the same GPU.
Values
- cudaLaunchMemSyncDomainDefault = 0
- Launch kernels in the default domain
- cudaLaunchMemSyncDomainRemote = 1
- Launch kernels in the remote domain
- enum cudaLimit
-
CUDA Limits
Values
- cudaLimitStackSize = 0x00
- GPU thread stack size
- cudaLimitPrintfFifoSize = 0x01
- GPU printf FIFO size
- cudaLimitMallocHeapSize = 0x02
- GPU malloc heap size
- cudaLimitDevRuntimeSyncDepth = 0x03
- GPU device runtime synchronize depth
- cudaLimitDevRuntimePendingLaunchCount = 0x04
- GPU device runtime pending launch count
- cudaLimitMaxL2FetchGranularity = 0x05
- A value between 0 and 128 that indicates the maximum fetch granularity of L2 (in Bytes). This is a hint
- cudaLimitPersistingL2CacheSize = 0x06
- A size in bytes for L2 persisting lines cache size
- enum cudaMemAccessFlags
-
Specifies the memory protection flags for mapping.
Values
- cudaMemAccessFlagsProtNone = 0
- Default, make the address range not accessible
- cudaMemAccessFlagsProtRead = 1
- Make the address range read accessible
- cudaMemAccessFlagsProtReadWrite = 3
- Make the address range read-write accessible
- enum cudaMemAllocationHandleType
-
Flags for specifying particular handle types
Values
- cudaMemHandleTypeNone = 0x0
- Does not allow any export mechanism. >
- cudaMemHandleTypePosixFileDescriptor = 0x1
- Allows a file descriptor to be used for exporting. Permitted only on POSIX systems. (int)
- cudaMemHandleTypeWin32 = 0x2
- Allows a Win32 NT handle to be used for exporting. (HANDLE)
- cudaMemHandleTypeWin32Kmt = 0x4
- Allows a Win32 KMT handle to be used for exporting. (D3DKMT_HANDLE)
- enum cudaMemAllocationType
-
Defines the allocation types available
Values
- cudaMemAllocationTypeInvalid = 0x0
- cudaMemAllocationTypePinned = 0x1
- This allocation type is 'pinned', i.e. cannot migrate from its current location while the application is actively using it
- cudaMemAllocationTypeMax = 0x7FFFFFFF
- enum cudaMemLocationType
-
Specifies the type of location
Values
- cudaMemLocationTypeInvalid = 0
- cudaMemLocationTypeDevice = 1
- Location is a device location, thus id is a device ordinal
- cudaMemLocationTypeHost = 2
- Location is host, id is ignored
- cudaMemLocationTypeHostNuma = 3
- Location is a host NUMA node, thus id is a host NUMA node id
- cudaMemLocationTypeHostNumaCurrent = 4
- Location is the host NUMA node closest to the current thread's CPU, id is ignored
- enum cudaMemPoolAttr
-
CUDA memory pool attributes
Values
- cudaMemPoolReuseFollowEventDependencies = 0x1
- (value type = int) Allow cuMemAllocAsync to use memory asynchronously freed in another streams 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)
- cudaMemPoolReuseAllowOpportunistic = 0x2
- (value type = int) Allow reuse of already completed frees when there is no dependency between the free and allocation. (default enabled)
- cudaMemPoolReuseAllowInternalDependencies = 0x3
- (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 cuFreeAsync (default enabled).
- cudaMemPoolAttrReleaseThreshold = 0x4
- (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)
- cudaMemPoolAttrReservedMemCurrent = 0x5
- (value type = cuuint64_t) Amount of backing memory currently allocated for the mempool.
- cudaMemPoolAttrReservedMemHigh = 0x6
- (value type = cuuint64_t) High watermark of backing memory allocated for the mempool since the last time it was reset. High watermark can only be reset to zero.
- cudaMemPoolAttrUsedMemCurrent = 0x7
- (value type = cuuint64_t) Amount of memory from the pool that is currently in use by the application.
- cudaMemPoolAttrUsedMemHigh = 0x8
- (value type = cuuint64_t) High watermark of the amount of memory from the pool that was in use by the application since the last time it was reset. High watermark can only be reset to zero.
- enum cudaMemRangeAttribute
-
CUDA range attributes
Values
- cudaMemRangeAttributeReadMostly = 1
- Whether the range will mostly be read and only occassionally be written to
- cudaMemRangeAttributePreferredLocation = 2
- The preferred location of the range
- cudaMemRangeAttributeAccessedBy = 3
- Memory range has cudaMemAdviseSetAccessedBy set for specified device
- cudaMemRangeAttributeLastPrefetchLocation = 4
- The last location to which the range was prefetched
- cudaMemRangeAttributePreferredLocationType = 5
- The preferred location type of the range
- cudaMemRangeAttributePreferredLocationId = 6
- The preferred location id of the range
- cudaMemRangeAttributeLastPrefetchLocationType = 7
- The last location type to which the range was prefetched
- cudaMemRangeAttributeLastPrefetchLocationId = 8
- The last location id to which the range was prefetched
- enum cudaMemcpyKind
-
CUDA memory copy types
Values
- cudaMemcpyHostToHost = 0
- Host -> Host
- cudaMemcpyHostToDevice = 1
- Host -> Device
- cudaMemcpyDeviceToHost = 2
- Device -> Host
- cudaMemcpyDeviceToDevice = 3
- Device -> Device
- cudaMemcpyDefault = 4
- Direction of the transfer is inferred from the pointer values. Requires unified virtual addressing
- enum cudaMemoryAdvise
-
CUDA Memory Advise values
Values
- cudaMemAdviseSetReadMostly = 1
- Data will mostly be read and only occassionally be written to
- cudaMemAdviseUnsetReadMostly = 2
- Undo the effect of cudaMemAdviseSetReadMostly
- cudaMemAdviseSetPreferredLocation = 3
- Set the preferred location for the data as the specified device
- cudaMemAdviseUnsetPreferredLocation = 4
- Clear the preferred location for the data
- cudaMemAdviseSetAccessedBy = 5
- Data will be accessed by the specified device, so prevent page faults as much as possible
- cudaMemAdviseUnsetAccessedBy = 6
- Let the Unified Memory subsystem decide on the page faulting policy for the specified device
- enum cudaMemoryType
-
CUDA memory types
Values
- cudaMemoryTypeUnregistered = 0
- Unregistered memory
- cudaMemoryTypeHost = 1
- Host memory
- cudaMemoryTypeDevice = 2
- Device memory
- cudaMemoryTypeManaged = 3
- Managed memory
- enum cudaResourceType
-
CUDA resource types
Values
- cudaResourceTypeArray = 0x00
- Array resource
- cudaResourceTypeMipmappedArray = 0x01
- Mipmapped array resource
- cudaResourceTypeLinear = 0x02
- Linear resource
- cudaResourceTypePitch2D = 0x03
- Pitch 2D resource
- enum cudaResourceViewFormat
-
CUDA texture resource view formats
Values
- cudaResViewFormatNone = 0x00
- No resource view format (use underlying resource format)
- cudaResViewFormatUnsignedChar1 = 0x01
- 1 channel unsigned 8-bit integers
- cudaResViewFormatUnsignedChar2 = 0x02
- 2 channel unsigned 8-bit integers
- cudaResViewFormatUnsignedChar4 = 0x03
- 4 channel unsigned 8-bit integers
- cudaResViewFormatSignedChar1 = 0x04
- 1 channel signed 8-bit integers
- cudaResViewFormatSignedChar2 = 0x05
- 2 channel signed 8-bit integers
- cudaResViewFormatSignedChar4 = 0x06
- 4 channel signed 8-bit integers
- cudaResViewFormatUnsignedShort1 = 0x07
- 1 channel unsigned 16-bit integers
- cudaResViewFormatUnsignedShort2 = 0x08
- 2 channel unsigned 16-bit integers
- cudaResViewFormatUnsignedShort4 = 0x09
- 4 channel unsigned 16-bit integers
- cudaResViewFormatSignedShort1 = 0x0a
- 1 channel signed 16-bit integers
- cudaResViewFormatSignedShort2 = 0x0b
- 2 channel signed 16-bit integers
- cudaResViewFormatSignedShort4 = 0x0c
- 4 channel signed 16-bit integers
- cudaResViewFormatUnsignedInt1 = 0x0d
- 1 channel unsigned 32-bit integers
- cudaResViewFormatUnsignedInt2 = 0x0e
- 2 channel unsigned 32-bit integers
- cudaResViewFormatUnsignedInt4 = 0x0f
- 4 channel unsigned 32-bit integers
- cudaResViewFormatSignedInt1 = 0x10
- 1 channel signed 32-bit integers
- cudaResViewFormatSignedInt2 = 0x11
- 2 channel signed 32-bit integers
- cudaResViewFormatSignedInt4 = 0x12
- 4 channel signed 32-bit integers
- cudaResViewFormatHalf1 = 0x13
- 1 channel 16-bit floating point
- cudaResViewFormatHalf2 = 0x14
- 2 channel 16-bit floating point
- cudaResViewFormatHalf4 = 0x15
- 4 channel 16-bit floating point
- cudaResViewFormatFloat1 = 0x16
- 1 channel 32-bit floating point
- cudaResViewFormatFloat2 = 0x17
- 2 channel 32-bit floating point
- cudaResViewFormatFloat4 = 0x18
- 4 channel 32-bit floating point
- cudaResViewFormatUnsignedBlockCompressed1 = 0x19
- Block compressed 1
- cudaResViewFormatUnsignedBlockCompressed2 = 0x1a
- Block compressed 2
- cudaResViewFormatUnsignedBlockCompressed3 = 0x1b
- Block compressed 3
- cudaResViewFormatUnsignedBlockCompressed4 = 0x1c
- Block compressed 4 unsigned
- cudaResViewFormatSignedBlockCompressed4 = 0x1d
- Block compressed 4 signed
- cudaResViewFormatUnsignedBlockCompressed5 = 0x1e
- Block compressed 5 unsigned
- cudaResViewFormatSignedBlockCompressed5 = 0x1f
- Block compressed 5 signed
- cudaResViewFormatUnsignedBlockCompressed6H = 0x20
- Block compressed 6 unsigned half-float
- cudaResViewFormatSignedBlockCompressed6H = 0x21
- Block compressed 6 signed half-float
- cudaResViewFormatUnsignedBlockCompressed7 = 0x22
- Block compressed 7
- enum cudaSharedCarveout
-
Shared memory carveout configurations. These may be passed to cudaFuncSetAttribute
Values
- cudaSharedmemCarveoutDefault = -1
- No preference for shared memory or L1 (default)
- cudaSharedmemCarveoutMaxShared = 100
- Prefer maximum available shared memory, minimum L1 cache
- cudaSharedmemCarveoutMaxL1 = 0
- Prefer maximum available L1 cache, minimum shared memory
- enum cudaSharedMemConfig
-
CUDA shared memory configuration
Values
- cudaSharedMemBankSizeDefault = 0
- cudaSharedMemBankSizeFourByte = 1
- cudaSharedMemBankSizeEightByte = 2
- enum cudaStreamCaptureMode
-
Possible modes for stream capture thread interactions. For more details see cudaStreamBeginCapture and cudaThreadExchangeStreamCaptureMode
Values
- cudaStreamCaptureModeGlobal = 0
- cudaStreamCaptureModeThreadLocal = 1
- cudaStreamCaptureModeRelaxed = 2
- enum cudaStreamCaptureStatus
-
Possible stream capture statuses returned by cudaStreamIsCapturing
Values
- cudaStreamCaptureStatusNone = 0
- Stream is not capturing
- cudaStreamCaptureStatusActive = 1
- Stream is actively capturing
- cudaStreamCaptureStatusInvalidated = 2
- Stream is part of a capture sequence that has been invalidated, but not terminated
- enum cudaStreamUpdateCaptureDependenciesFlags
-
Flags for cudaStreamUpdateCaptureDependencies
Values
- cudaStreamAddCaptureDependencies = 0x0
- Add new nodes to the dependency set
- cudaStreamSetCaptureDependencies = 0x1
- Replace the dependency set with the new nodes
- enum cudaSurfaceBoundaryMode
-
CUDA Surface boundary modes
Values
- cudaBoundaryModeZero = 0
- Zero boundary mode
- cudaBoundaryModeClamp = 1
- Clamp boundary mode
- cudaBoundaryModeTrap = 2
- Trap boundary mode
- enum cudaSurfaceFormatMode
-
CUDA Surface format modes
Values
- cudaFormatModeForced = 0
- Forced format mode
- cudaFormatModeAuto = 1
- Auto format mode
- enum cudaTextureAddressMode
-
CUDA texture address modes
Values
- cudaAddressModeWrap = 0
- Wrapping address mode
- cudaAddressModeClamp = 1
- Clamp to edge address mode
- cudaAddressModeMirror = 2
- Mirror address mode
- cudaAddressModeBorder = 3
- Border address mode
- enum cudaTextureFilterMode
-
CUDA texture filter modes
Values
- cudaFilterModePoint = 0
- Point filter mode
- cudaFilterModeLinear = 1
- Linear filter mode
- enum cudaTextureReadMode
-
CUDA texture read modes
Values
- cudaReadModeElementType = 0
- Read texture as specified element type
- cudaReadModeNormalizedFloat = 1
- Read texture as normalized float
- enum cudaUserObjectFlags
-
Flags for user objects for graphs
Values
- cudaUserObjectNoDestructorSync = 0x1
- Indicates the destructor execution is not synchronized by any CUDA handle.
- enum cudaUserObjectRetainFlags
-
Flags for retaining user object references for graphs
Values
- cudaGraphUserObjectMove = 0x1
- Transfer references from the caller rather than creating new references.