CUDA Runtime API (PDF) - v8.0 (older) - Last updated September 4, 2016 - Send Feedback -

## 4.31. Data types used by CUDA Runtime

### Classes

struct
cudaChannelFormatDesc
struct
cudaDeviceProp
struct
cudaEglFrame
struct
cudaEglPlaneDesc
struct
cudaExtent
struct
cudaFuncAttributes
struct
cudaIpcEventHandle_t
struct
cudaIpcMemHandle_t
struct
cudaMemcpy3DParms
struct
cudaMemcpy3DPeerParms
struct
cudaPitchedPtr
struct
cudaPointerAttributes
struct
cudaPos
struct
cudaResourceDesc
struct
cudaResourceViewDesc
struct
cudaTextureDesc
struct
surfaceReference
struct
textureReference

### Defines

#define CUDA_EGL_MAX_PLANES 3
#define CUDA_IPC_HANDLE_SIZE 64
#define cudaArrayCubemap 0x04
#define cudaArrayDefault 0x00
#define cudaArrayLayered 0x01
#define cudaArraySurfaceLoadStore 0x02
#define cudaArrayTextureGather 0x08
#define cudaCpuDeviceId ((int)-1)
#define cudaDeviceBlockingSync 0x04
#define cudaDeviceLmemResizeToMax 0x10
#define cudaDeviceMapHost 0x08
#define cudaDeviceMask 0x1f
#define cudaDevicePropDontCare
#define cudaDeviceScheduleAuto 0x00
#define cudaDeviceScheduleBlockingSync 0x04
#define cudaDeviceScheduleMask 0x07
#define cudaDeviceScheduleSpin 0x01
#define cudaDeviceScheduleYield 0x02
#define cudaEventBlockingSync 0x01
#define cudaEventDefault 0x00
#define cudaEventDisableTiming 0x02
#define cudaEventInterprocess 0x04
#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 cudaInvalidDeviceId ((int)-2)
#define cudaIpcMemLazyEnablePeerAccess 0x01
#define cudaMemAttachGlobal 0x01
#define cudaMemAttachHost 0x02
#define cudaMemAttachSingle 0x04
#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 cudaGraphicsResource * cudaGraphicsResource_t
typedef cudaMipmappedArray * cudaMipmappedArray_const_t
typedef cudaMipmappedArray * cudaMipmappedArray_t
typedef enumcudaOutputMode cudaOutputMode_t
typedef CUstream_st * cudaStream_t
typedef unsigned long long cudaSurfaceObject_t
typedef unsigned long long cudaTextureObject_t
typedef CUuuid_st cudaUUID_t

### Enumerations

enum cudaChannelFormatKind
enum cudaComputeMode
enum cudaDeviceAttr
enum cudaDeviceP2PAttr
enum cudaEglColorFormat
enum cudaEglFrameType
enum cudaEglResourceLocationFlags
enum cudaError
enum cudaFuncCache
enum cudaGraphicsCubeFace
enum cudaGraphicsMapFlags
enum cudaGraphicsRegisterFlags
enum cudaLimit
enum cudaMemRangeAttribute
enum cudaMemcpyKind
enum cudaMemoryAdvise
enum cudaMemoryType
enum cudaOutputMode
enum cudaResourceType
enum cudaResourceViewFormat
enum cudaSharedMemConfig
enum cudaSurfaceBoundaryMode
enum cudaSurfaceFormatMode
enum cudaTextureAddressMode
enum cudaTextureFilterMode
enum cudaTextureReadMode

### Defines

#define CUDA_EGL_MAX_PLANES 3

  Maximum number of planes per frame

#define CUDA_IPC_HANDLE_SIZE 64

  CUDA IPC Handle Size

#define cudaArrayCubemap 0x04

  Must be set in cudaMalloc3DArray to create a cubemap CUDA array

#define cudaArrayDefault 0x00

  Default CUDA array allocation flag

#define cudaArrayLayered 0x01

  Must be set in cudaMalloc3DArray to create a layered CUDA array

#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 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 0x1f

  Device flags mask

#define cudaDevicePropDontCare

  Empty device properties

#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 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 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 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 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 cudaGraphicsResource * cudaGraphicsResource_t

  CUDA graphics resource types

typedef cudaMipmappedArray * cudaMipmappedArray_const_t

  CUDA mipmapped array (as source argument)

typedef cudaMipmappedArray * cudaMipmappedArray_t

  CUDA mipmapped array

typedef enumcudaOutputMode cudaOutputMode_t

  CUDA output file modes

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 CUuuid_st cudaUUID_t

  CUDA UUID types

### Enumerations

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
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
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
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), 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, width, height ratio same as YUV422Planar.
  cudaEglColorFormatRGB = 4
    R/G/B three channels in one surface with RGB byte ordering.
  cudaEglColorFormatBGR = 5
    R/G/B three channels in one surface with BGR byte ordering.
  cudaEglColorFormatARGB = 6
    R/G/B/A four channels in one surface with ARGB byte ordering.
  cudaEglColorFormatRGBA = 7
    R/G/B/A four channels in one surface with RGBA byte ordering.
  cudaEglColorFormatL = 8
    single luminance channel in one surface.
  cudaEglColorFormatR = 9
    single color channel in one surface.
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 can also mean that the operation being queried is complete (see cudaEventQuery() and cudaStreamQuery()).
  cudaErrorMissingConfiguration = 1
    The device function being invoked (usually via cudaLaunchKernel()) was not previously configured via the cudaConfigureCall() function.
  cudaErrorMemoryAllocation = 2
    The API call failed because it was unable to allocate enough memory to perform the requested operation.
  cudaErrorInitializationError = 3
    The API call failed because the CUDA driver and runtime could not be initialized.
  cudaErrorLaunchFailure = 4
    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. The device cannot be used until cudaThreadExit() is called. All existing device memory allocations are invalid and must be reconstructed if the program is to continue using CUDA.
  cudaErrorPriorLaunchFailure = 5

    ###### 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.

  cudaErrorLaunchTimeout = 6
    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. The device cannot be used until cudaThreadExit() is called. All existing device memory allocations are invalid and must be reconstructed if the program is to continue using CUDA.
  cudaErrorLaunchOutOfResources = 7
    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.
  cudaErrorInvalidDeviceFunction = 8
    The requested device function does not exist or is not compiled for the proper device architecture.
  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.
  cudaErrorInvalidDevice = 10
    This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
  cudaErrorInvalidValue = 11
    This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
  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.
  cudaErrorMapBufferObjectFailed = 14
    This indicates that the buffer object could not be mapped.
  cudaErrorUnmapBufferObjectFailed = 15
    This indicates that the buffer object could not be unmapped.
  cudaErrorInvalidHostPointer = 16
    This indicates that at least one host pointer passed to the API call is not a valid host pointer.
  cudaErrorInvalidDevicePointer = 17
    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.

  cudaErrorCudartUnloading = 29
    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.
  cudaErrorUnknown = 30
    This indicates that an unknown internal error has occurred.
  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.

  cudaErrorInvalidResourceHandle = 33
    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.
  cudaErrorNotReady = 34
    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().
  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.
  cudaErrorSetOnActiveProcess = 36
    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.
  cudaErrorInvalidSurface = 37
    This indicates that the surface passed to the API call is not a valid surface.
  cudaErrorNoDevice = 38
    This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
  cudaErrorECCUncorrectable = 39
    This indicates that an uncorrectable ECC error was detected during execution.
  cudaErrorSharedObjectSymbolNotFound = 40
    This indicates that a link to a shared object failed to resolve.
  cudaErrorSharedObjectInitFailed = 41
    This indicates that initialization of a shared object failed.
  cudaErrorUnsupportedLimit = 42
    This indicates that the cudaLimit passed to the API call is not supported by the active device.
  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 cudaComputeModeExclusive, cudaComputeModeProhibited 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.
  cudaErrorInvalidKernelImage = 47
    This indicates that the device kernel image is invalid.
  cudaErrorNoKernelImageForDevice = 48
    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.
  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.
  cudaErrorPeerAccessAlreadyEnabled = 50
    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 = 51
    This error indicates that cudaDeviceDisablePeerAccess() is trying to disable peer addressing which has not been enabled yet via cudaDeviceEnablePeerAccess().
  cudaErrorDeviceAlreadyInUse = 54
    This indicates that a call tried to access an exclusive-thread device that is already in use by a different thread.
  cudaErrorProfilerDisabled = 55
    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 = 56

    ###### 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 = 57

    ###### 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 = 58

    ###### 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.

  cudaErrorAssert = 59
    An assert triggered in device code during kernel execution. The device cannot be used again until cudaThreadExit() is called. All existing allocations are invalid and must be reconstructed if the program is to continue using CUDA.
  cudaErrorTooManyPeers = 60
    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 = 61
    This error indicates that the memory range passed to cudaHostRegister() has already been registered.
  cudaErrorHostMemoryNotRegistered = 62
    This error indicates that the pointer passed to cudaHostUnregister() does not correspond to any currently registered memory region.
  cudaErrorOperatingSystem = 63
    This error indicates that an OS call failed.
  cudaErrorPeerAccessUnsupported = 64
    This error indicates that P2P access is not supported across the given devices.
  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.
  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.
  cudaErrorNotPermitted = 70
    This error indicates the attempted operation is not permitted.
  cudaErrorNotSupported = 71
    This error indicates the attempted operation is not supported on the current system or device.
  cudaErrorHardwareStackError = 72
    Device encountered an error in the call stack during kernel execution, possibly due to stack corruption or exceeding the stack size limit. The context cannot be used, so it must be destroyed (and a new one should be created). All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.
  cudaErrorIllegalInstruction = 73
    The device encountered an illegal instruction during kernel execution The context cannot be used, so it must be destroyed (and a new one should be created). All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.
  cudaErrorMisalignedAddress = 74
    The device encountered a load or store instruction on a memory address which is not aligned. The context cannot be used, so it must be destroyed (and a new one should be created). All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.
  cudaErrorInvalidAddressSpace = 75
    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. The context cannot be used, so it must be destroyed (and a new one should be created). All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.
  cudaErrorInvalidPc = 76
    The device encountered an invalid program counter. The context cannot be used, so it must be destroyed (and a new one should be created). All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.
  cudaErrorIllegalAddress = 77
    The device encountered a load or store instruction on an invalid memory address. The context cannot be used, so it must be destroyed (and a new one should be created). All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.
  cudaErrorInvalidPtx = 78
    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 = 79
    This indicates an error with the OpenGL or DirectX context.
  cudaErrorNvlinkUncorrectable = 80
    This indicates that an uncorrectable NVLink error was detected during the execution.
  cudaErrorStartupFailure = 0x7f
    This indicates an internal startup failure in the CUDA runtime.
  cudaErrorApiFailureBase = 10000

    ###### Deprecated

    This error return is deprecated as of CUDA 4.1.

    Any unhandled CUDA driver error is added to this value and returned via the runtime. Production releases of CUDA should not return such errors.

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 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 cudaLimit

  CUDA Limits

  ###### Values

  cudaLimitStackSize = 0x00
    GPU thread stack size
  cudaLimitPrintfFifoSize = 0x01
    GPU printf/fprintf FIFO size
  cudaLimitMallocHeapSize = 0x02
    GPU malloc heap size
  cudaLimitDevRuntimeSyncDepth = 0x03
    GPU device runtime synchronize depth
  cudaLimitDevRuntimePendingLaunchCount = 0x04
    GPU device runtime pending launch count
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
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

  cudaMemoryTypeHost = 1
    Host memory
  cudaMemoryTypeDevice = 2
    Device memory
enum cudaOutputMode

  CUDA Profiler Output modes

  ###### Values

  cudaKeyValuePair = 0x00
    Output mode Key-Value pair format.
  cudaCSV = 0x01
    Output mode Comma separated values format.
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 cudaSharedMemConfig

  CUDA shared memory configuration

  ###### Values

  cudaSharedMemBankSizeDefault = 0
  cudaSharedMemBankSizeFourByte = 1
  cudaSharedMemBankSizeEightByte = 2
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