CUDA中cudaSuccess如何表示

在CUDA中，cudaSuccess是一个枚举类型的常量，它的值为0，表示CUDA API的调用成功完成。当CUDA API调用失败时，会返回一个非零的错误代码。因此，可以使用如下语句来检查CUDA API调用是否成功完成：

cudaError_t status = some_cuda_api_call();
if(status != cudaSuccess){
    // 处理错误情况
}

相关问题

cudaSuccess在哪里定义的

cudaSuccess是CUDA API中定义的一个枚举类型，表示CUDA函数执行成功。在CUDA的头文件cudaError.h中定义了这个枚举类型。具体的定义如下：

typedef enum cudaError_enum {
    cudaSuccess                            = 0,
    cudaErrorInvalidValue                  = 1,
    cudaErrorMemoryAllocation              = 2,
    cudaErrorInitializationError           = 3,
    cudaErrorCudartUnloading               = 4,
    cudaErrorProfilerDisabled              = 5,
    cudaErrorProfilerNotInitialized        = 6,
    cudaErrorProfilerAlreadyStarted        = 7,
    cudaErrorProfilerAlreadyStopped        = 8,
    cudaErrorInvalidConfiguration          = 9,
    cudaErrorInvalidPitchValue             = 12,
    cudaErrorInvalidSymbol                 = 13,
    cudaErrorInvalidDevicePointer          = 17,
    cudaErrorInvalidMemcpyDirection        = 21,
    cudaErrorInsufficientDriver            = 35,
    cudaErrorMissingConfiguration          = 52,
    cudaErrorPriorLaunchFailure            = 53,
    cudaErrorInvalidDeviceFunction         = 98,
    cudaErrorNoDevice                      = 100,
    cudaErrorInvalidDevice                 = 101,
    cudaErrorInvalidImage                  = 200,
    cudaErrorInvalidContext                = 201,
    cudaErrorContextAlreadyCurrent         = 202,
    cudaErrorMapFailed                     = 205,
    cudaErrorUnmapFailed                   = 206,
    cudaErrorArrayIsMapped                 = 207,
    cudaErrorAlreadyMapped                 = 208,
    cudaErrorNoBinaryForGpu                = 209,
    cudaErrorAlreadyAcquired               = 210,
    cudaErrorNotMapped                     = 211,
    cudaErrorNotMappedAsArray              = 212,
    cudaErrorNotMappedAsPointer            = 213,
    cudaErrorECCUncorrectable              = 214,
    cudaErrorUnsupportedLimit              = 215,
    cudaErrorContextAlreadyInUse           = 216,
    cudaErrorPeerAccessUnsupported         = 217,
    cudaErrorInvalidPtx                    = 218,
    cudaErrorInvalidGraphicsContext        = 219,
    cudaErrorNvlinkUncorrectable           = 220,
    cudaErrorInvalidSource                 = 300,
    cudaErrorFileNotFound                  = 301,
    cudaErrorSharedObjectSymbolNotFound    = 302,
    cudaErrorSharedObjectInitFailed        = 303,
    cudaErrorOperatingSystem               = 304,
    cudaErrorInvalidResourceHandle         = 400,
    cudaErrorIllegalState                  = 401,
    cudaErrorSymbolNotFound                = 500,
    cudaErrorNotReady                      = 600,
    cudaErrorIllegalAddress                = 700,
    cudaErrorLaunchOutOfResources          = 701,
    cudaErrorLaunchTimeout                 = 702,
    cudaErrorLaunchIncompatibleTexturing   = 703,
    cudaErrorPeerAccessAlreadyEnabled      = 704,
    cudaErrorPeerAccessNotEnabled          = 705,
    cudaErrorPrimaryContextActive          = 708,
    cudaErrorContextIsDestroyed            = 709,
    cudaErrorAssert                        = 710,
    cudaErrorTooManyPeers                  = 711,
    cudaErrorHostMemoryAlreadyRegistered   = 712,
    cudaErrorHostMemoryNotRegistered       = 713,
    cudaErrorHardwareStackError            = 714,
    cudaErrorIllegalInstruction            = 715,
    cudaErrorMisalignedAddress             = 716,
    cudaErrorInvalidAddressSpace           = 717,
    cudaErrorInvalidPc                     = 718,
    cudaErrorIllegalAddressSpace           = 719,
    cudaErrorInvalidSourceSize             = 720,
    cudaErrorInvalidMemcpyHandle           = 721,
    cudaErrorInvalidKernelImage            = 722,
    cudaErrorDeviceUninitialized           = 723,
    cudaErrorMapBufferObjectFailed         = 724,
    cudaErrorUnmapBufferObjectFailed       = 725,
    cudaErrorArrayDoesNotExist             = 726,
    cudaErrorDeviceAlreadyInUse            = 727,
    cudaErrorProfilerDisabledForDevice     = 728,
    cudaErrorProfilerNotInitializedForDevice = 729,
    cudaErrorProfilerAlreadyStartedForDevice = 730,
    cudaErrorProfilerAlreadyStoppedForDevice = 731,
    cudaErrorAssertUnfiled                 = 732,
    cudaErrorAssertFileNotFound            = 733,
    cudaErrorAssertLineNotFound            = 734,
    cudaErrorAssertOffsetOutOfBounds       = 735,
    cudaErrorAssertStrideOutOfBounds       = 736,
    cudaErrorAssertOutOfMemory             = 737,
    cudaErrorAssertPreambleNotFound        = 738,
    cudaErrorAssertParamMismatch           = 739,
    cudaErrorTextureNotBound               = 740,
    cudaErrorTextureFetchFailed            = 741,
    cudaErrorTextureNotMapped              = 742,
    cudaErrorTextureNotInLinearMemory      = 743,
    cudaErrorTextureReadModeInvalid        = 744,
    cudaErrorInvalidSurface                = 2000,
    cudaErrorSurfaceAlreadyMapped          = 2001,
    cudaErrorSurfaceNotMapped              = 2002,
    cudaErrorSurfaceCudaMallocReturned     = 2003,
    cudaErrorSurfaceInvalidValue           = 2004,
    cudaErrorDuplicateVariableName         = 2010,
    cudaErrorDuplicateTextureName          = 2011,
    cudaErrorDuplicateSurfaceName          = 2012,
    cudaErrorDevicesUnavailable            = 2013,
    cudaErrorInvalidKernelArgument         = 2014,
    cudaErrorInvalidTexture               = 2015,
    cudaErrorInvalidSurface               = 2016,
    cudaErrorDuplicateLayerName            = 2017,
    cudaErrorIncompatibleDriverContext     = 2018,
    cudaErrorMissingSampleLocation         = 2019,
    cudaErrorInvalidFilterSetting          = 2020,
    cudaErrorInvalidNormSetting            = 2021,
    cudaErrorMixedDeviceExecution          = 2022,
    cudaErrorCudartStaticInitializationFailure = 2023,
    cudaErrorUnknown                       = 9999
} cudaError_t;

cudaError cudastatus

cudaError是一个枚举类型，用于表示CUDA函数调用的执行状态。其中，cudaError_t是一个错误码，用于表示函数执行期间的错误。如果函数执行成功，则返回cudaSuccess，否则返回相应的错误码。你可以使用cudaGetErrorString函数将错误码翻译成详细信息。

与内核相关的函数是cudaMemcpy，它用于在主机和设备之间进行数据交换。该函数可以在不同的传输方向上执行不同的操作，包括从主机到主机、从主机到设备、从设备到主机和从设备到设备。具体的操作是通过cudaMemcpyKind参数确定的。

请问你需要了解具体哪些错误码和数据传输方向的相关信息呢？