4.1. Data types used by CUDA driver

Classes

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Defines

#define CUDA_ARRAY3D_2DARRAY 0x01
#define CUDA_ARRAY3D_CUBEMAP 0x04
#define CUDA_ARRAY3D_DEPTH_TEXTURE 0x10
#define CUDA_ARRAY3D_LAYERED 0x01
#define CUDA_ARRAY3D_SURFACE_LDST 0x02
#define CUDA_ARRAY3D_TEXTURE_GATHER 0x08
#define CUDA_VERSION 8000
#define CU_DEVICE_CPU ((CUdevice)-1)
#define CU_DEVICE_INVALID ((CUdevice)-2)
#define CU_IPC_HANDLE_SIZE 64
#define CU_LAUNCH_PARAM_BUFFER_POINTER ((void*)0x01)
#define CU_LAUNCH_PARAM_BUFFER_SIZE ((void*)0x02)
#define CU_LAUNCH_PARAM_END ((void*)0x00)
#define CU_MEMHOSTALLOC_DEVICEMAP 0x02
#define CU_MEMHOSTALLOC_PORTABLE 0x01
#define CU_MEMHOSTALLOC_WRITECOMBINED 0x04
#define CU_MEMHOSTREGISTER_DEVICEMAP 0x02
#define CU_MEMHOSTREGISTER_IOMEMORY 0x04
#define CU_MEMHOSTREGISTER_PORTABLE 0x01
#define CU_PARAM_TR_DEFAULT -1
#define CU_STREAM_LEGACY ((CUstream)0x1)
#define CU_STREAM_PER_THREAD ((CUstream)0x2)
#define CU_TRSA_OVERRIDE_FORMAT 0x01
#define CU_TRSF_NORMALIZED_COORDINATES 0x02
#define CU_TRSF_READ_AS_INTEGER 0x01
#define CU_TRSF_SRGB 0x10
#define MAX_PLANES 3

Typedefs

typedef CUarray_st *  CUarray
typedef CUctx_st *  CUcontext
typedef int  CUdevice
typedef unsigned int  CUdeviceptr
typedef CUeglStreamConnection_st *  CUeglStreamConnection
typedef CUevent_st *  CUevent
typedef CUfunc_st *  CUfunction
typedef CUgraphicsResource_st *  CUgraphicsResource
typedef CUmipmappedArray_st *  CUmipmappedArray
typedef CUmod_st *  CUmodule
typedef size_t(CUDA_CB*  CUoccupancyB2DSize )( int  blockSize )
typedef CUstream_st *  CUstream
typedef void(CUDA_CB*  CUstreamCallback )( CUstream hStream,  CUresult status, void*  userData )
typedef unsigned long long  CUsurfObject
typedef CUsurfref_st *  CUsurfref
typedef unsigned long long  CUtexObject
typedef CUtexref_st *  CUtexref

Enumerations

enum CUaddress_mode
enum CUarray_cubemap_face
enum CUarray_format
enum CUcomputemode
enum CUctx_flags
enum CUdevice_P2PAttribute
enum CUdevice_attribute
enum CUeglColorFormat
enum CUeglFrameType
enum CUeglResourceLocationFlags
enum CUevent_flags
enum CUfilter_mode
enum CUfunc_cache
enum CUfunction_attribute
enum CUgraphicsMapResourceFlags
enum CUgraphicsRegisterFlags
enum CUipcMem_flags
enum CUjitInputType
enum CUjit_cacheMode
enum CUjit_fallback
enum CUjit_option
enum CUjit_target
enum CUlimit
enum CUmemAttach_flags
enum CUmem_advise
enum CUmemorytype
enum CUoccupancy_flags
enum CUpointer_attribute
enum CUresourceViewFormat
enum CUresourcetype
enum CUresult
enum CUsharedconfig
enum CUstreamBatchMemOpType
enum CUstreamWaitValue_flags
enum CUstreamWriteValue_flags
enum CUstream_flags

Defines

#define CUDA_ARRAY3D_2DARRAY 0x01

Deprecated, use CUDA_ARRAY3D_LAYERED

#define CUDA_ARRAY3D_CUBEMAP 0x04

If set, the CUDA array is a collection of six 2D arrays, representing faces of a cube. The width of such a CUDA array must be equal to its height, and Depth must be six. If CUDA_ARRAY3D_LAYERED flag is also set, then the CUDA array is a collection of cubemaps and Depth must be a multiple of six.

#define CUDA_ARRAY3D_DEPTH_TEXTURE 0x10

This flag if set indicates that the CUDA array is a DEPTH_TEXTURE.

#define CUDA_ARRAY3D_LAYERED 0x01

If set, the CUDA array is a collection of layers, where each layer is either a 1D or a 2D array and the Depth member of CUDA_ARRAY3D_DESCRIPTOR specifies the number of layers, not the depth of a 3D array.

#define CUDA_ARRAY3D_SURFACE_LDST 0x02

This flag must be set in order to bind a surface reference to the CUDA array

#define CUDA_ARRAY3D_TEXTURE_GATHER 0x08

This flag must be set in order to perform texture gather operations on a CUDA array.

#define CUDA_VERSION 8000

CUDA API version number

#define CU_DEVICE_CPU ((CUdevice)-1)

Device that represents the CPU

#define CU_DEVICE_INVALID ((CUdevice)-2)

Device that represents an invalid device

#define CU_IPC_HANDLE_SIZE 64

CUDA IPC handle size

#define CU_LAUNCH_PARAM_BUFFER_POINTER ((void*)0x01)

Indicator that the next value in the extra parameter to cuLaunchKernel will be a pointer to a buffer containing all kernel parameters used for launching kernel f. This buffer needs to honor all alignment/padding requirements of the individual parameters. If CU_LAUNCH_PARAM_BUFFER_SIZE is not also specified in the extra array, then CU_LAUNCH_PARAM_BUFFER_POINTER will have no effect.

#define CU_LAUNCH_PARAM_BUFFER_SIZE ((void*)0x02)

Indicator that the next value in the extra parameter to cuLaunchKernel will be a pointer to a size_t which contains the size of the buffer specified with CU_LAUNCH_PARAM_BUFFER_POINTER. It is required that CU_LAUNCH_PARAM_BUFFER_POINTER also be specified in the extra array if the value associated with CU_LAUNCH_PARAM_BUFFER_SIZE is not zero.

#define CU_LAUNCH_PARAM_END ((void*)0x00)

End of array terminator for the extra parameter to cuLaunchKernel

#define CU_MEMHOSTALLOC_DEVICEMAP 0x02

If set, host memory is mapped into CUDA address space and cuMemHostGetDevicePointer() may be called on the host pointer. Flag for cuMemHostAlloc()

#define CU_MEMHOSTALLOC_PORTABLE 0x01

If set, host memory is portable between CUDA contexts. Flag for cuMemHostAlloc()

#define CU_MEMHOSTALLOC_WRITECOMBINED 0x04

If set, host memory is allocated as write-combined - fast to write, faster to DMA, slow to read except via SSE4 streaming load instruction (MOVNTDQA). Flag for cuMemHostAlloc()

#define CU_MEMHOSTREGISTER_DEVICEMAP 0x02

If set, host memory is mapped into CUDA address space and cuMemHostGetDevicePointer() may be called on the host pointer. Flag for cuMemHostRegister()

#define CU_MEMHOSTREGISTER_IOMEMORY 0x04

If set, the passed memory pointer is treated as pointing to some memory-mapped I/O space, e.g. belonging to a third-party PCIe device. On Windows the flag is a no-op. On Linux that memory is marked as non cache-coherent for the GPU and is expected to be physically contiguous. It may return CUDA_ERROR_NOT_PERMITTED if run as an unprivileged user, CUDA_ERROR_NOT_SUPPORTED on older Linux kernel versions. On all other platforms, it is not supported and CUDA_ERROR_NOT_SUPPORTED is returned. Flag for cuMemHostRegister()

#define CU_MEMHOSTREGISTER_PORTABLE 0x01

If set, host memory is portable between CUDA contexts. Flag for cuMemHostRegister()

#define CU_PARAM_TR_DEFAULT -1

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

#define CU_STREAM_LEGACY ((CUstream)0x1)

Legacy stream handle

Stream handle that can be passed as a CUstream to use an implicit stream with legacy synchronization behavior.

See details of the synchronization behavior.

#define CU_STREAM_PER_THREAD ((CUstream)0x2)

Per-thread stream handle

Stream handle that can be passed as a CUstream to use an implicit stream with per-thread synchronization behavior.

See details of the synchronization behavior.

#define CU_TRSA_OVERRIDE_FORMAT 0x01

Override the texref format with a format inferred from the array. Flag for cuTexRefSetArray()

#define CU_TRSF_NORMALIZED_COORDINATES 0x02

Use normalized texture coordinates in the range [0,1) instead of [0,dim). Flag for cuTexRefSetFlags()

#define CU_TRSF_READ_AS_INTEGER 0x01

Read the texture as integers rather than promoting the values to floats in the range [0,1]. Flag for cuTexRefSetFlags()

#define CU_TRSF_SRGB 0x10

Perform sRGB->linear conversion during texture read. Flag for cuTexRefSetFlags()

#define MAX_PLANES 3

Maximum number of planes per frame

Typedefs

typedef CUarray_st * CUarray

CUDA array

typedef CUctx_st * CUcontext

CUDA context

typedef int CUdevice

CUDA device

typedef unsigned int CUdeviceptr

CUDA device pointer CUdeviceptr is defined as an unsigned integer type whose size matches the size of a pointer on the target platform.

typedef CUeglStreamConnection_st * CUeglStreamConnection

CUDA EGLSream Connection

typedef CUevent_st * CUevent

CUDA event

typedef CUfunc_st * CUfunction

CUDA function

typedef CUgraphicsResource_st * CUgraphicsResource

CUDA graphics interop resource

typedef CUmipmappedArray_st * CUmipmappedArray

CUDA mipmapped array

typedef CUmod_st * CUmodule

CUDA module

size_t(CUDA_CB* CUoccupancyB2DSize )( int  blockSize )

Block size to per-block dynamic shared memory mapping for a certain kernel

Parameters
blockSize
Block size of the kernel.
Returns

The dynamic shared memory needed by a block.

typedef CUstream_st * CUstream

CUDA stream

void(CUDA_CB* CUstreamCallback )( CUstream hStream,  CUresult status, void*  userData )

CUDA stream callback

Parameters
hStream
The stream the callback was added to, as passed to cuStreamAddCallback. May be NULL.
CUresult status
userData
User parameter provided at registration.
typedef unsigned long long CUsurfObject

An opaque value that represents a CUDA surface object

typedef CUsurfref_st * CUsurfref

CUDA surface reference

typedef unsigned long long CUtexObject

An opaque value that represents a CUDA texture object

typedef CUtexref_st * CUtexref

CUDA texture reference

Enumerations

enum CUaddress_mode

Texture reference addressing modes

Values
CU_TR_ADDRESS_MODE_WRAP = 0
Wrapping address mode
CU_TR_ADDRESS_MODE_CLAMP = 1
Clamp to edge address mode
CU_TR_ADDRESS_MODE_MIRROR = 2
Mirror address mode
CU_TR_ADDRESS_MODE_BORDER = 3
Border address mode
enum CUarray_cubemap_face

Array indices for cube faces

Values
CU_CUBEMAP_FACE_POSITIVE_X = 0x00
Positive X face of cubemap
CU_CUBEMAP_FACE_NEGATIVE_X = 0x01
Negative X face of cubemap
CU_CUBEMAP_FACE_POSITIVE_Y = 0x02
Positive Y face of cubemap
CU_CUBEMAP_FACE_NEGATIVE_Y = 0x03
Negative Y face of cubemap
CU_CUBEMAP_FACE_POSITIVE_Z = 0x04
Positive Z face of cubemap
CU_CUBEMAP_FACE_NEGATIVE_Z = 0x05
Negative Z face of cubemap
enum CUarray_format

Array formats

Values
CU_AD_FORMAT_UNSIGNED_INT8 = 0x01
Unsigned 8-bit integers
CU_AD_FORMAT_UNSIGNED_INT16 = 0x02
Unsigned 16-bit integers
CU_AD_FORMAT_UNSIGNED_INT32 = 0x03
Unsigned 32-bit integers
CU_AD_FORMAT_SIGNED_INT8 = 0x08
Signed 8-bit integers
CU_AD_FORMAT_SIGNED_INT16 = 0x09
Signed 16-bit integers
CU_AD_FORMAT_SIGNED_INT32 = 0x0a
Signed 32-bit integers
CU_AD_FORMAT_HALF = 0x10
16-bit floating point
CU_AD_FORMAT_FLOAT = 0x20
32-bit floating point
enum CUcomputemode

Compute Modes

Values
CU_COMPUTEMODE_DEFAULT = 0
Default compute mode (Multiple contexts allowed per device)
CU_COMPUTEMODE_PROHIBITED = 2
Compute-prohibited mode (No contexts can be created on this device at this time)
CU_COMPUTEMODE_EXCLUSIVE_PROCESS = 3
Compute-exclusive-process mode (Only one context used by a single process can be present on this device at a time)
enum CUctx_flags

Context creation flags

Values
CU_CTX_SCHED_AUTO = 0x00
Automatic scheduling
CU_CTX_SCHED_SPIN = 0x01
Set spin as default scheduling
CU_CTX_SCHED_YIELD = 0x02
Set yield as default scheduling
CU_CTX_SCHED_BLOCKING_SYNC = 0x04
Set blocking synchronization as default scheduling
CU_CTX_BLOCKING_SYNC = 0x04
Deprecated

This flag was deprecated as of CUDA 4.0 and was replaced with CU_CTX_SCHED_BLOCKING_SYNC.

Set blocking synchronization as default scheduling

CU_CTX_SCHED_MASK = 0x07
CU_CTX_MAP_HOST = 0x08
Support mapped pinned allocations
CU_CTX_LMEM_RESIZE_TO_MAX = 0x10
Keep local memory allocation after launch
CU_CTX_FLAGS_MASK = 0x1f
enum CUdevice_P2PAttribute

P2P Attributes

Values
CU_DEVICE_P2P_ATTRIBUTE_PERFORMANCE_RANK = 0x01
A relative value indicating the performance of the link between two devices
CU_DEVICE_P2P_ATTRIBUTE_ACCESS_SUPPORTED = 0x02
P2P Access is enable
CU_DEVICE_P2P_ATTRIBUTE_NATIVE_ATOMIC_SUPPORTED = 0x03
Atomic operation over the link supported
enum CUdevice_attribute

Device properties

Values
CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK = 1
Maximum number of threads per block
CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X = 2
Maximum block dimension X
CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y = 3
Maximum block dimension Y
CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z = 4
Maximum block dimension Z
CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_X = 5
Maximum grid dimension X
CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y = 6
Maximum grid dimension Y
CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z = 7
Maximum grid dimension Z
CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK = 8
Maximum shared memory available per block in bytes
CU_DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK = 8
Deprecated, use CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
CU_DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY = 9
Memory available on device for __constant__ variables in a CUDA C kernel in bytes
CU_DEVICE_ATTRIBUTE_WARP_SIZE = 10
Warp size in threads
CU_DEVICE_ATTRIBUTE_MAX_PITCH = 11
Maximum pitch in bytes allowed by memory copies
CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK = 12
Maximum number of 32-bit registers available per block
CU_DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK = 12
Deprecated, use CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK
CU_DEVICE_ATTRIBUTE_CLOCK_RATE = 13
Typical clock frequency in kilohertz
CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT = 14
Alignment requirement for textures
CU_DEVICE_ATTRIBUTE_GPU_OVERLAP = 15
Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead CU_DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT.
CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT = 16
Number of multiprocessors on device
CU_DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT = 17
Specifies whether there is a run time limit on kernels
CU_DEVICE_ATTRIBUTE_INTEGRATED = 18
Device is integrated with host memory
CU_DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY = 19
Device can map host memory into CUDA address space
CU_DEVICE_ATTRIBUTE_COMPUTE_MODE = 20
Compute mode (See CUcomputemode for details)
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH = 21
Maximum 1D texture width
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH = 22
Maximum 2D texture width
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT = 23
Maximum 2D texture height
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH = 24
Maximum 3D texture width
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT = 25
Maximum 3D texture height
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH = 26
Maximum 3D texture depth
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH = 27
Maximum 2D layered texture width
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT = 28
Maximum 2D layered texture height
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS = 29
Maximum layers in a 2D layered texture
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH = 27
Deprecated, use CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT = 28
Deprecated, use CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES = 29
Deprecated, use CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
CU_DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT = 30
Alignment requirement for surfaces
CU_DEVICE_ATTRIBUTE_CONCURRENT_KERNELS = 31
Device can possibly execute multiple kernels concurrently
CU_DEVICE_ATTRIBUTE_ECC_ENABLED = 32
Device has ECC support enabled
CU_DEVICE_ATTRIBUTE_PCI_BUS_ID = 33
PCI bus ID of the device
CU_DEVICE_ATTRIBUTE_PCI_DEVICE_ID = 34
PCI device ID of the device
CU_DEVICE_ATTRIBUTE_TCC_DRIVER = 35
Device is using TCC driver model
CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE = 36
Peak memory clock frequency in kilohertz
CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH = 37
Global memory bus width in bits
CU_DEVICE_ATTRIBUTE_L2_CACHE_SIZE = 38
Size of L2 cache in bytes
CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR = 39
Maximum resident threads per multiprocessor
CU_DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT = 40
Number of asynchronous engines
CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING = 41
Device shares a unified address space with the host
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH = 42
Maximum 1D layered texture width
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS = 43
Maximum layers in a 1D layered texture
CU_DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER = 44
Deprecated, do not use.
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH = 45
Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT = 46
Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE = 47
Alternate maximum 3D texture width
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE = 48
Alternate maximum 3D texture height
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE = 49
Alternate maximum 3D texture depth
CU_DEVICE_ATTRIBUTE_PCI_DOMAIN_ID = 50
PCI domain ID of the device
CU_DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT = 51
Pitch alignment requirement for textures
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH = 52
Maximum cubemap texture width/height
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH = 53
Maximum cubemap layered texture width/height
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS = 54
Maximum layers in a cubemap layered texture
CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH = 55
Maximum 1D surface width
CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH = 56
Maximum 2D surface width
CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT = 57
Maximum 2D surface height
CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH = 58
Maximum 3D surface width
CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT = 59
Maximum 3D surface height
CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH = 60
Maximum 3D surface depth
CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH = 61
Maximum 1D layered surface width
CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS = 62
Maximum layers in a 1D layered surface
CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH = 63
Maximum 2D layered surface width
CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT = 64
Maximum 2D layered surface height
CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS = 65
Maximum layers in a 2D layered surface
CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH = 66
Maximum cubemap surface width
CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH = 67
Maximum cubemap layered surface width
CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS = 68
Maximum layers in a cubemap layered surface
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH = 69
Maximum 1D linear texture width
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH = 70
Maximum 2D linear texture width
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT = 71
Maximum 2D linear texture height
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH = 72
Maximum 2D linear texture pitch in bytes
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH = 73
Maximum mipmapped 2D texture width
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT = 74
Maximum mipmapped 2D texture height
CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR = 75
Major compute capability version number
CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR = 76
Minor compute capability version number
CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH = 77
Maximum mipmapped 1D texture width
CU_DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED = 78
Device supports stream priorities
CU_DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED = 79
Device supports caching globals in L1
CU_DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED = 80
Device supports caching locals in L1
CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR = 81
Maximum shared memory available per multiprocessor in bytes
CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR = 82
Maximum number of 32-bit registers available per multiprocessor
CU_DEVICE_ATTRIBUTE_MANAGED_MEMORY = 83
Device can allocate managed memory on this system
CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD = 84
Device is on a multi-GPU board
CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID = 85
Unique id for a group of devices on the same multi-GPU board
CU_DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED = 86
Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
CU_DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO = 87
Ratio of single precision performance (in floating-point operations per second) to double precision performance
CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS = 88
Device supports coherently accessing pageable memory without calling cudaHostRegister on it
CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS = 89
Device can coherently access managed memory concurrently with the CPU
CU_DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED = 90
Device supports compute preemption.
CU_DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM = 91
Device can access host registered memory at the same virtual address as the CPU
CU_DEVICE_ATTRIBUTE_MAX
enum CUeglColorFormat

CUDA EGL Color Format - The different planar and multiplanar formats currently supported for CUDA_EGL interops.

Values
CU_EGL_COLOR_FORMAT_YUV420_PLANAR = 0x00
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.
CU_EGL_COLOR_FORMAT_YUV420_SEMIPLANAR = 0x01
Y, UV in two surfaces (UV as one surface), width, height ratio same as YUV420Planar.
CU_EGL_COLOR_FORMAT_YUV422_PLANAR = 0x02
Y, U, V each in a separate surface, U/V width = 1/2 Y width, U/V height = Y height.
CU_EGL_COLOR_FORMAT_YUV422_SEMIPLANAR = 0x03
Y, UV in two surfaces, width, height ratio same as YUV422Planar.
CU_EGL_COLOR_FORMAT_RGB = 0x04
R/G/B three channels in one surface with RGB byte ordering.
CU_EGL_COLOR_FORMAT_BGR = 0x05
R/G/B three channels in one surface with BGR byte ordering.
CU_EGL_COLOR_FORMAT_ARGB = 0x06
R/G/B/A four channels in one surface with ARGB byte ordering.
CU_EGL_COLOR_FORMAT_RGBA = 0x07
R/G/B/A four channels in one surface with RGBA byte ordering.
CU_EGL_COLOR_FORMAT_L = 0x08
single luminance channel in one surface.
CU_EGL_COLOR_FORMAT_R = 0x09
single color channel in one surface.
enum CUeglFrameType

CUDA EglFrame type - array or pointer

Values
CU_EGL_FRAME_TYPE_ARRAY = 0
Frame type CUDA array
CU_EGL_FRAME_TYPE_PITCH = 1
Frame type pointer
enum CUeglResourceLocationFlags

Resource location flags- sysmem or vidmem If the producer is on sysmem and CU_EGL_RESOURCE_LOCATION_VIDMEM is set, it will involve additional copy of the resource from sysmem to vidmem.

Values
CU_EGL_RESOURCE_LOCATION_SYSMEM = 0x00
Resource location sysmem
CU_EGL_RESOURCE_LOCATION_VIDMEM = 0x01
Resource location vidmem
enum CUevent_flags

Event creation flags

Values
CU_EVENT_DEFAULT = 0x0
Default event flag
CU_EVENT_BLOCKING_SYNC = 0x1
Event uses blocking synchronization
CU_EVENT_DISABLE_TIMING = 0x2
Event will not record timing data
CU_EVENT_INTERPROCESS = 0x4
Event is suitable for interprocess use. CU_EVENT_DISABLE_TIMING must be set
enum CUfilter_mode

Texture reference filtering modes

Values
CU_TR_FILTER_MODE_POINT = 0
Point filter mode
CU_TR_FILTER_MODE_LINEAR = 1
Linear filter mode
enum CUfunc_cache

Function cache configurations

Values
CU_FUNC_CACHE_PREFER_NONE = 0x00
no preference for shared memory or L1 (default)
CU_FUNC_CACHE_PREFER_SHARED = 0x01
prefer larger shared memory and smaller L1 cache
CU_FUNC_CACHE_PREFER_L1 = 0x02
prefer larger L1 cache and smaller shared memory
CU_FUNC_CACHE_PREFER_EQUAL = 0x03
prefer equal sized L1 cache and shared memory
enum CUfunction_attribute

Function properties

Values
CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK = 0
The maximum number of threads per block, beyond which a launch of the function would fail. This number depends on both the function and the device on which the function is currently loaded.
CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES = 1
The size in bytes of statically-allocated shared memory required by this function. This does not include dynamically-allocated shared memory requested by the user at runtime.
CU_FUNC_ATTRIBUTE_CONST_SIZE_BYTES = 2
The size in bytes of user-allocated constant memory required by this function.
CU_FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES = 3
The size in bytes of local memory used by each thread of this function.
CU_FUNC_ATTRIBUTE_NUM_REGS = 4
The number of registers used by each thread of this function.
CU_FUNC_ATTRIBUTE_PTX_VERSION = 5
The PTX virtual architecture version for which the function was compiled. This value is the major PTX version * 10 + the minor PTX version, so a PTX version 1.3 function would return the value 13. Note that this may return the undefined value of 0 for cubins compiled prior to CUDA 3.0.
CU_FUNC_ATTRIBUTE_BINARY_VERSION = 6
The binary architecture version for which the function was compiled. This value is the major binary version * 10 + the minor binary version, so a binary version 1.3 function would return the value 13. Note that this will return a value of 10 for legacy cubins that do not have a properly-encoded binary architecture version.
CU_FUNC_ATTRIBUTE_CACHE_MODE_CA = 7
The attribute to indicate whether the function has been compiled with user specified option "-Xptxas --dlcm=ca" set .
CU_FUNC_ATTRIBUTE_MAX
enum CUgraphicsMapResourceFlags

Flags for mapping and unmapping interop resources

Values
CU_GRAPHICS_MAP_RESOURCE_FLAGS_NONE = 0x00
CU_GRAPHICS_MAP_RESOURCE_FLAGS_READ_ONLY = 0x01
CU_GRAPHICS_MAP_RESOURCE_FLAGS_WRITE_DISCARD = 0x02
enum CUgraphicsRegisterFlags

Flags to register a graphics resource

Values
CU_GRAPHICS_REGISTER_FLAGS_NONE = 0x00
CU_GRAPHICS_REGISTER_FLAGS_READ_ONLY = 0x01
CU_GRAPHICS_REGISTER_FLAGS_WRITE_DISCARD = 0x02
CU_GRAPHICS_REGISTER_FLAGS_SURFACE_LDST = 0x04
CU_GRAPHICS_REGISTER_FLAGS_TEXTURE_GATHER = 0x08
enum CUipcMem_flags

CUDA Ipc Mem Flags

Values
CU_IPC_MEM_LAZY_ENABLE_PEER_ACCESS = 0x1
Automatically enable peer access between remote devices as needed
enum CUjitInputType

Device code formats

Values
CU_JIT_INPUT_CUBIN = 0
Compiled device-class-specific device code Applicable options: none
CU_JIT_INPUT_PTX
PTX source code Applicable options: PTX compiler options
CU_JIT_INPUT_FATBINARY
Bundle of multiple cubins and/or PTX of some device code Applicable options: PTX compiler options, CU_JIT_FALLBACK_STRATEGY
CU_JIT_INPUT_OBJECT
Host object with embedded device code Applicable options: PTX compiler options, CU_JIT_FALLBACK_STRATEGY
CU_JIT_INPUT_LIBRARY
Archive of host objects with embedded device code Applicable options: PTX compiler options, CU_JIT_FALLBACK_STRATEGY
CU_JIT_NUM_INPUT_TYPES
enum CUjit_cacheMode

Caching modes for dlcm

Values
CU_JIT_CACHE_OPTION_NONE = 0
Compile with no -dlcm flag specified
CU_JIT_CACHE_OPTION_CG
Compile with L1 cache disabled
CU_JIT_CACHE_OPTION_CA
Compile with L1 cache enabled
enum CUjit_fallback

Cubin matching fallback strategies

Values
CU_PREFER_PTX = 0
Prefer to compile ptx if exact binary match not found
CU_PREFER_BINARY
Prefer to fall back to compatible binary code if exact match not found
enum CUjit_option

Online compiler and linker options

Values
CU_JIT_MAX_REGISTERS = 0
Max number of registers that a thread may use. Option type: unsigned int Applies to: compiler only
CU_JIT_THREADS_PER_BLOCK
IN: Specifies minimum number of threads per block to target compilation for OUT: Returns the number of threads the compiler actually targeted. This restricts the resource utilization fo the compiler (e.g. max registers) such that a block with the given number of threads should be able to launch based on register limitations. Note, this option does not currently take into account any other resource limitations, such as shared memory utilization. Cannot be combined with CU_JIT_TARGET. Option type: unsigned int Applies to: compiler only
CU_JIT_WALL_TIME
Overwrites the option value with the total wall clock time, in milliseconds, spent in the compiler and linker Option type: float Applies to: compiler and linker
CU_JIT_INFO_LOG_BUFFER
Pointer to a buffer in which to print any log messages that are informational in nature (the buffer size is specified via option CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES) Option type: char * Applies to: compiler and linker
CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES
IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator) OUT: Amount of log buffer filled with messages Option type: unsigned int Applies to: compiler and linker
CU_JIT_ERROR_LOG_BUFFER
Pointer to a buffer in which to print any log messages that reflect errors (the buffer size is specified via option CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES) Option type: char * Applies to: compiler and linker
CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES
IN: Log buffer size in bytes. Log messages will be capped at this size (including null terminator) OUT: Amount of log buffer filled with messages Option type: unsigned int Applies to: compiler and linker
CU_JIT_OPTIMIZATION_LEVEL
Level of optimizations to apply to generated code (0 - 4), with 4 being the default and highest level of optimizations. Option type: unsigned int Applies to: compiler only
CU_JIT_TARGET_FROM_CUCONTEXT
No option value required. Determines the target based on the current attached context (default) Option type: No option value needed Applies to: compiler and linker
CU_JIT_TARGET
Target is chosen based on supplied CUjit_target. Cannot be combined with CU_JIT_THREADS_PER_BLOCK. Option type: unsigned int for enumerated type CUjit_target Applies to: compiler and linker
CU_JIT_FALLBACK_STRATEGY
Specifies choice of fallback strategy if matching cubin is not found. Choice is based on supplied CUjit_fallback. This option cannot be used with cuLink* APIs as the linker requires exact matches. Option type: unsigned int for enumerated type CUjit_fallback Applies to: compiler only
CU_JIT_GENERATE_DEBUG_INFO
Specifies whether to create debug information in output (-g) (0: false, default) Option type: int Applies to: compiler and linker
CU_JIT_LOG_VERBOSE
Generate verbose log messages (0: false, default) Option type: int Applies to: compiler and linker
CU_JIT_GENERATE_LINE_INFO
Generate line number information (-lineinfo) (0: false, default) Option type: int Applies to: compiler only
CU_JIT_CACHE_MODE
Specifies whether to enable caching explicitly (-dlcm) Choice is based on supplied CUjit_cacheMode_enum. Option type: unsigned int for enumerated type CUjit_cacheMode_enum Applies to: compiler only
CU_JIT_NEW_SM3X_OPT
The below jit options are used for internal purposes only, in this version of CUDA
CU_JIT_FAST_COMPILE
CU_JIT_NUM_OPTIONS
enum CUjit_target

Online compilation targets

Values
CU_TARGET_COMPUTE_10 = 10
Compute device class 1.0
CU_TARGET_COMPUTE_11 = 11
Compute device class 1.1
CU_TARGET_COMPUTE_12 = 12
Compute device class 1.2
CU_TARGET_COMPUTE_13 = 13
Compute device class 1.3
CU_TARGET_COMPUTE_20 = 20
Compute device class 2.0
CU_TARGET_COMPUTE_21 = 21
Compute device class 2.1
CU_TARGET_COMPUTE_30 = 30
Compute device class 3.0
CU_TARGET_COMPUTE_32 = 32
Compute device class 3.2
CU_TARGET_COMPUTE_35 = 35
Compute device class 3.5
CU_TARGET_COMPUTE_37 = 37
Compute device class 3.7
CU_TARGET_COMPUTE_50 = 50
Compute device class 5.0
CU_TARGET_COMPUTE_52 = 52
Compute device class 5.2
CU_TARGET_COMPUTE_53 = 53
Compute device class 5.3
CU_TARGET_COMPUTE_60 = 60
Compute device class 6.0. This must be removed for CUDA 7.0 toolkit. See bug 1518217.
CU_TARGET_COMPUTE_61 = 61
Compute device class 6.1. This must be removed for CUDA 7.0 toolkit.
CU_TARGET_COMPUTE_62 = 62
Compute device class 6.2. This must be removed for CUDA 7.0 toolkit.
enum CUlimit

Limits

Values
CU_LIMIT_STACK_SIZE = 0x00
GPU thread stack size
CU_LIMIT_PRINTF_FIFO_SIZE = 0x01
GPU printf FIFO size
CU_LIMIT_MALLOC_HEAP_SIZE = 0x02
GPU malloc heap size
CU_LIMIT_DEV_RUNTIME_SYNC_DEPTH = 0x03
GPU device runtime launch synchronize depth
CU_LIMIT_DEV_RUNTIME_PENDING_LAUNCH_COUNT = 0x04
GPU device runtime pending launch count
CU_LIMIT_MAX
enum CUmemAttach_flags

CUDA Mem Attach Flags

Values
CU_MEM_ATTACH_GLOBAL = 0x1
Memory can be accessed by any stream on any device
CU_MEM_ATTACH_HOST = 0x2
Memory cannot be accessed by any stream on any device
CU_MEM_ATTACH_SINGLE = 0x4
Memory can only be accessed by a single stream on the associated device
enum CUmem_advise

Memory advise values

Values
CU_MEM_ADVISE_SET_READ_MOSTLY = 1
Data will mostly be read and only occassionally be written to
CU_MEM_ADVISE_UNSET_READ_MOSTLY = 2
Undo the effect of CU_MEM_ADVISE_SET_READ_MOSTLY
CU_MEM_ADVISE_SET_PREFERRED_LOCATION = 3
Set the preferred location for the data as the specified device
CU_MEM_ADVISE_UNSET_PREFERRED_LOCATION = 4
Clear the preferred location for the data
CU_MEM_ADVISE_SET_ACCESSED_BY = 5
Data will be accessed by the specified device, so prevent page faults as much as possible
CU_MEM_ADVISE_UNSET_ACCESSED_BY = 6
Let the Unified Memory subsystem decide on the page faulting policy for the specified device
enum CUmemorytype

Memory types

Values
CU_MEMORYTYPE_HOST = 0x01
Host memory
CU_MEMORYTYPE_DEVICE = 0x02
Device memory
CU_MEMORYTYPE_ARRAY = 0x03
Array memory
CU_MEMORYTYPE_UNIFIED = 0x04
Unified device or host memory
enum CUoccupancy_flags

Occupancy calculator flag

Values
CU_OCCUPANCY_DEFAULT = 0x0
Default behavior
CU_OCCUPANCY_DISABLE_CACHING_OVERRIDE = 0x1
Assume global caching is enabled and cannot be automatically turned off
enum CUpointer_attribute

Pointer information

Values
CU_POINTER_ATTRIBUTE_CONTEXT = 1
The CUcontext on which a pointer was allocated or registered
CU_POINTER_ATTRIBUTE_MEMORY_TYPE = 2
The CUmemorytype describing the physical location of a pointer
CU_POINTER_ATTRIBUTE_DEVICE_POINTER = 3
The address at which a pointer's memory may be accessed on the device
CU_POINTER_ATTRIBUTE_HOST_POINTER = 4
The address at which a pointer's memory may be accessed on the host
CU_POINTER_ATTRIBUTE_P2P_TOKENS = 5
A pair of tokens for use with the nv-p2p.h Linux kernel interface
CU_POINTER_ATTRIBUTE_SYNC_MEMOPS = 6
Synchronize every synchronous memory operation initiated on this region
CU_POINTER_ATTRIBUTE_BUFFER_ID = 7
A process-wide unique ID for an allocated memory region
CU_POINTER_ATTRIBUTE_IS_MANAGED = 8
Indicates if the pointer points to managed memory
enum CUresourceViewFormat

Resource view format

Values
CU_RES_VIEW_FORMAT_NONE = 0x00
No resource view format (use underlying resource format)
CU_RES_VIEW_FORMAT_UINT_1X8 = 0x01
1 channel unsigned 8-bit integers
CU_RES_VIEW_FORMAT_UINT_2X8 = 0x02
2 channel unsigned 8-bit integers
CU_RES_VIEW_FORMAT_UINT_4X8 = 0x03
4 channel unsigned 8-bit integers
CU_RES_VIEW_FORMAT_SINT_1X8 = 0x04
1 channel signed 8-bit integers
CU_RES_VIEW_FORMAT_SINT_2X8 = 0x05
2 channel signed 8-bit integers
CU_RES_VIEW_FORMAT_SINT_4X8 = 0x06
4 channel signed 8-bit integers
CU_RES_VIEW_FORMAT_UINT_1X16 = 0x07
1 channel unsigned 16-bit integers
CU_RES_VIEW_FORMAT_UINT_2X16 = 0x08
2 channel unsigned 16-bit integers
CU_RES_VIEW_FORMAT_UINT_4X16 = 0x09
4 channel unsigned 16-bit integers
CU_RES_VIEW_FORMAT_SINT_1X16 = 0x0a
1 channel signed 16-bit integers
CU_RES_VIEW_FORMAT_SINT_2X16 = 0x0b
2 channel signed 16-bit integers
CU_RES_VIEW_FORMAT_SINT_4X16 = 0x0c
4 channel signed 16-bit integers
CU_RES_VIEW_FORMAT_UINT_1X32 = 0x0d
1 channel unsigned 32-bit integers
CU_RES_VIEW_FORMAT_UINT_2X32 = 0x0e
2 channel unsigned 32-bit integers
CU_RES_VIEW_FORMAT_UINT_4X32 = 0x0f
4 channel unsigned 32-bit integers
CU_RES_VIEW_FORMAT_SINT_1X32 = 0x10
1 channel signed 32-bit integers
CU_RES_VIEW_FORMAT_SINT_2X32 = 0x11
2 channel signed 32-bit integers
CU_RES_VIEW_FORMAT_SINT_4X32 = 0x12
4 channel signed 32-bit integers
CU_RES_VIEW_FORMAT_FLOAT_1X16 = 0x13
1 channel 16-bit floating point
CU_RES_VIEW_FORMAT_FLOAT_2X16 = 0x14
2 channel 16-bit floating point
CU_RES_VIEW_FORMAT_FLOAT_4X16 = 0x15
4 channel 16-bit floating point
CU_RES_VIEW_FORMAT_FLOAT_1X32 = 0x16
1 channel 32-bit floating point
CU_RES_VIEW_FORMAT_FLOAT_2X32 = 0x17
2 channel 32-bit floating point
CU_RES_VIEW_FORMAT_FLOAT_4X32 = 0x18
4 channel 32-bit floating point
CU_RES_VIEW_FORMAT_UNSIGNED_BC1 = 0x19
Block compressed 1
CU_RES_VIEW_FORMAT_UNSIGNED_BC2 = 0x1a
Block compressed 2
CU_RES_VIEW_FORMAT_UNSIGNED_BC3 = 0x1b
Block compressed 3
CU_RES_VIEW_FORMAT_UNSIGNED_BC4 = 0x1c
Block compressed 4 unsigned
CU_RES_VIEW_FORMAT_SIGNED_BC4 = 0x1d
Block compressed 4 signed
CU_RES_VIEW_FORMAT_UNSIGNED_BC5 = 0x1e
Block compressed 5 unsigned
CU_RES_VIEW_FORMAT_SIGNED_BC5 = 0x1f
Block compressed 5 signed
CU_RES_VIEW_FORMAT_UNSIGNED_BC6H = 0x20
Block compressed 6 unsigned half-float
CU_RES_VIEW_FORMAT_SIGNED_BC6H = 0x21
Block compressed 6 signed half-float
CU_RES_VIEW_FORMAT_UNSIGNED_BC7 = 0x22
Block compressed 7
enum CUresourcetype

Resource types

Values
CU_RESOURCE_TYPE_ARRAY = 0x00
Array resoure
CU_RESOURCE_TYPE_MIPMAPPED_ARRAY = 0x01
Mipmapped array resource
CU_RESOURCE_TYPE_LINEAR = 0x02
Linear resource
CU_RESOURCE_TYPE_PITCH2D = 0x03
Pitch 2D resource
enum CUresult

Error codes

Values
CUDA_SUCCESS = 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 cuEventQuery() and cuStreamQuery()).
CUDA_ERROR_INVALID_VALUE = 1
This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
CUDA_ERROR_OUT_OF_MEMORY = 2
The API call failed because it was unable to allocate enough memory to perform the requested operation.
CUDA_ERROR_NOT_INITIALIZED = 3
This indicates that the CUDA driver has not been initialized with cuInit() or that initialization has failed.
CUDA_ERROR_DEINITIALIZED = 4
This indicates that the CUDA driver is in the process of shutting down.
CUDA_ERROR_PROFILER_DISABLED = 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.
CUDA_ERROR_PROFILER_NOT_INITIALIZED = 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 cuProfilerStart or cuProfilerStop without initialization.

CUDA_ERROR_PROFILER_ALREADY_STARTED = 7
Deprecated

This error return is deprecated as of CUDA 5.0. It is no longer an error to call cuProfilerStart() when profiling is already enabled.

CUDA_ERROR_PROFILER_ALREADY_STOPPED = 8
Deprecated

This error return is deprecated as of CUDA 5.0. It is no longer an error to call cuProfilerStop() when profiling is already disabled.

CUDA_ERROR_NO_DEVICE = 100
This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
CUDA_ERROR_INVALID_DEVICE = 101
This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
CUDA_ERROR_INVALID_IMAGE = 200
This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.
CUDA_ERROR_INVALID_CONTEXT = 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.
CUDA_ERROR_CONTEXT_ALREADY_CURRENT = 202
Deprecated

This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via cuCtxPushCurrent().

This indicated that the context being supplied as a parameter to the API call was already the active context.

CUDA_ERROR_MAP_FAILED = 205
This indicates that a map or register operation has failed.
CUDA_ERROR_UNMAP_FAILED = 206
This indicates that an unmap or unregister operation has failed.
CUDA_ERROR_ARRAY_IS_MAPPED = 207
This indicates that the specified array is currently mapped and thus cannot be destroyed.
CUDA_ERROR_ALREADY_MAPPED = 208
This indicates that the resource is already mapped.
CUDA_ERROR_NO_BINARY_FOR_GPU = 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.
CUDA_ERROR_ALREADY_ACQUIRED = 210
This indicates that a resource has already been acquired.
CUDA_ERROR_NOT_MAPPED = 211
This indicates that a resource is not mapped.
CUDA_ERROR_NOT_MAPPED_AS_ARRAY = 212
This indicates that a mapped resource is not available for access as an array.
CUDA_ERROR_NOT_MAPPED_AS_POINTER = 213
This indicates that a mapped resource is not available for access as a pointer.
CUDA_ERROR_ECC_UNCORRECTABLE = 214
This indicates that an uncorrectable ECC error was detected during execution.
CUDA_ERROR_UNSUPPORTED_LIMIT = 215
This indicates that the CUlimit passed to the API call is not supported by the active device.
CUDA_ERROR_CONTEXT_ALREADY_IN_USE = 216
This indicates that the CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.
CUDA_ERROR_PEER_ACCESS_UNSUPPORTED = 217
This indicates that peer access is not supported across the given devices.
CUDA_ERROR_INVALID_PTX = 218
This indicates that a PTX JIT compilation failed.
CUDA_ERROR_INVALID_GRAPHICS_CONTEXT = 219
This indicates an error with OpenGL or DirectX context.
CUDA_ERROR_NVLINK_UNCORRECTABLE = 220
This indicates that an uncorrectable NVLink error was detected during the execution.
CUDA_ERROR_INVALID_SOURCE = 300
This indicates that the device kernel source is invalid.
CUDA_ERROR_FILE_NOT_FOUND = 301
This indicates that the file specified was not found.
CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND = 302
This indicates that a link to a shared object failed to resolve.
CUDA_ERROR_SHARED_OBJECT_INIT_FAILED = 303
This indicates that initialization of a shared object failed.
CUDA_ERROR_OPERATING_SYSTEM = 304
This indicates that an OS call failed.
CUDA_ERROR_INVALID_HANDLE = 400
This indicates that a resource handle passed to the API call was not valid. Resource handles are opaque types like CUstream and CUevent.
CUDA_ERROR_NOT_FOUND = 500
This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
CUDA_ERROR_NOT_READY = 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 CUDA_SUCCESS (which indicates completion). Calls that may return this value include cuEventQuery() and cuStreamQuery().
CUDA_ERROR_ILLEGAL_ADDRESS = 700
While executing a kernel, the device encountered a load or store instruction on an invalid memory address. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.
CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES = 701
This indicates that a launch did not occur because it did not have appropriate resources. This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.
CUDA_ERROR_LAUNCH_TIMEOUT = 702
This indicates that the device kernel took too long to execute. This can only occur if timeouts are enabled - see the device attribute CU_DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.
CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING = 703
This error indicates a kernel launch that uses an incompatible texturing mode.
CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED = 704
This error indicates that a call to cuCtxEnablePeerAccess() is trying to re-enable peer access to a context which has already had peer access to it enabled.
CUDA_ERROR_PEER_ACCESS_NOT_ENABLED = 705
This error indicates that cuCtxDisablePeerAccess() is trying to disable peer access which has not been enabled yet via cuCtxEnablePeerAccess().
CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE = 708
This error indicates that the primary context for the specified device has already been initialized.
CUDA_ERROR_CONTEXT_IS_DESTROYED = 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.
CUDA_ERROR_ASSERT = 710
A device-side assert triggered during kernel execution. The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.
CUDA_ERROR_TOO_MANY_PEERS = 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 cuCtxEnablePeerAccess().
CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED = 712
This error indicates that the memory range passed to cuMemHostRegister() has already been registered.
CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED = 713
This error indicates that the pointer passed to cuMemHostUnregister() does not correspond to any currently registered memory region.
CUDA_ERROR_HARDWARE_STACK_ERROR = 714
While executing a kernel, the device encountered a stack error. This can be due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.
CUDA_ERROR_ILLEGAL_INSTRUCTION = 715
While executing a kernel, the device encountered an illegal instruction. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.
CUDA_ERROR_MISALIGNED_ADDRESS = 716
While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.
CUDA_ERROR_INVALID_ADDRESS_SPACE = 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.
CUDA_ERROR_INVALID_PC = 718
While executing a kernel, the device program counter wrapped its address space. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.
CUDA_ERROR_LAUNCH_FAILED = 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. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.
CUDA_ERROR_NOT_PERMITTED = 800
This error indicates that the attempted operation is not permitted.
CUDA_ERROR_NOT_SUPPORTED = 801
This error indicates that the attempted operation is not supported on the current system or device.
CUDA_ERROR_UNKNOWN = 999
This indicates that an unknown internal error has occurred.
enum CUsharedconfig

Shared memory configurations

Values
CU_SHARED_MEM_CONFIG_DEFAULT_BANK_SIZE = 0x00
set default shared memory bank size
CU_SHARED_MEM_CONFIG_FOUR_BYTE_BANK_SIZE = 0x01
set shared memory bank width to four bytes
CU_SHARED_MEM_CONFIG_EIGHT_BYTE_BANK_SIZE = 0x02
set shared memory bank width to eight bytes
enum CUstreamBatchMemOpType

Operations for cuStreamBatchMemOp

Values
CU_STREAM_MEM_OP_WAIT_VALUE_32 = 1
Represents a cuStreamWaitValue32 operation
CU_STREAM_MEM_OP_WRITE_VALUE_32 = 2
Represents a cuStreamWriteValue32 operation
CU_STREAM_MEM_OP_FLUSH_REMOTE_WRITES = 3
This has the same effect as CU_STREAM_WAIT_VALUE_FLUSH, but as a standalone operation.
enum CUstreamWaitValue_flags
Values
CU_STREAM_WAIT_VALUE_GEQ = 0x0
Wait until (int32_t)(*addr - value) >= 0. Note this is a cyclic comparison which ignores wraparound. (Default behavior.)
CU_STREAM_WAIT_VALUE_EQ = 0x1
Wait until *addr == value.
CU_STREAM_WAIT_VALUE_AND = 0x2
Wait until (*addr & value) != 0.
CU_STREAM_WAIT_VALUE_FLUSH = 1<<30
Follow the wait operation with a flush of outstanding remote writes. This means that, if a remote write operation is guaranteed to have reached the device before the wait can be satisfied, that write is guaranteed to be visible to downstream device work. The device is permitted to reorder remote writes internally. For example, this flag would be required if two remote writes arrive in a defined order, the wait is satisfied by the second write, and downstream work needs to observe the first write.
enum CUstreamWriteValue_flags
Values
CU_STREAM_WRITE_VALUE_DEFAULT = 0x0
Default behavior
CU_STREAM_WRITE_VALUE_NO_MEMORY_BARRIER = 0x1
Permits the write to be reordered with writes which were issued before it, as a performance optimization. Normally, cuStreamWriteValue32 will provide a memory fence before the write, which has similar semantics to __threadfence_system() but is scoped to the stream rather than a CUDA thread.
enum CUstream_flags

Stream creation flags

Values
CU_STREAM_DEFAULT = 0x0
Default stream flag
CU_STREAM_NON_BLOCKING = 0x1
Stream does not synchronize with stream 0 (the NULL stream)