Question

在上次发布失败后，我无法找到启动cuFFT处理的方法。

这是一个最小的例子。主要思想如下：我们创建一个简单的cuFTT处理器，可以管理其资源（设备内存和cuFFT计划）。我们检查这个处理器是否做了FFT。然后我们要求创建太多计划，因此我们强制执行cuFFT错误。然后我们释放所有资源并尝试重复成功启动。但是，处理器在发生故障后无能为力。

首先，这是一个相当长的序言：

#include <iostream>
using std::cout;
using std::cerr;
using std::endl;

#include <vector>
using std::vector;

#include "cuda_runtime.h"
#include "cufft.h"

// cuFFT API errors
static char* _cufftGetErrorEnum( cufftResult_t error )
{
    switch ( error )
    {
        case CUFFT_SUCCESS:
        return "CUFFT_SUCCESS";

        case CUFFT_INVALID_PLAN:
        return "cuFFT was passed an invalid plan handle";

        case CUFFT_ALLOC_FAILED:
        return "cuFFT failed to allocate GPU or CPU memory";

        // No longer used
        case CUFFT_INVALID_TYPE:
        return "CUFFT_INVALID_TYPE";

        case CUFFT_INVALID_VALUE:
        return "User specified an invalid pointer or parameter";

        case CUFFT_INTERNAL_ERROR:
        return "Driver or internal cuFFT library error";

        case CUFFT_EXEC_FAILED:
        return "Failed to execute an FFT on the GPU";

        case CUFFT_SETUP_FAILED:
        return "The cuFFT library failed to initialize";

        case CUFFT_INVALID_SIZE:
        return "User specified an invalid transform size";

        // No longer used
        case CUFFT_UNALIGNED_DATA:
        return "CUFFT_UNALIGNED_DATA";

        case CUFFT_INCOMPLETE_PARAMETER_LIST:
        return "Missing parameters in call";

        case CUFFT_INVALID_DEVICE:
        return "Execution of a plan was on different GPU than plan creation";

        case CUFFT_PARSE_ERROR:
        return "Internal plan database error";

        case CUFFT_NO_WORKSPACE:
        return "No workspace has been provided prior to plan execution";

        case CUFFT_NOT_IMPLEMENTED:
        return "CUFFT_NOT_IMPLEMENTED";

        case CUFFT_LICENSE_ERROR:
        return "CUFFT_LICENSE_ERROR";
    }

    return "<unknown>";
}

// check cuda runtime calls
bool cudaCheck( cudaError_t err )
{
    if ( err != cudaSuccess )
    {
        cudaDeviceSynchronize();
        cerr << cudaGetErrorString( cudaGetLastError() ) << endl;
        return false;
    }

    return true;
}

// check cuFFT calls
bool cufftCheck( cufftResult_t err )
{
    if ( err != CUFFT_SUCCESS )
    {
        cerr << _cufftGetErrorEnum( err ) << endl;
        return false;
    }

    return true;
}

接下来，我们定义一个简单的cuFFT处理器，它可以管理其资源（设备内存和cuFFT计划）

class CCuFFT_Processor
{
    vector<cufftHandle> _plans;
    cufftComplex *_data;
    size_t _data_bytes;

    // Release resouces
    bool ReleaseAll();
    bool ReleaseMemory();
    bool ReleasePlans();

public:

    CCuFFT_Processor() :
    _data( NULL ),
    _data_bytes( 0 )
    {
        _plans.reserve( 32 );
        _plans.clear();
    }

    ~CCuFFT_Processor()
    {
        ReleaseAll();
    }

    bool Run();
    bool Alloc( size_t data_len, size_t batch_len );
};

以下是我们发布资源的方式：

bool     CCuFFT_Processor::ReleaseMemory()
{
    bool chk = true;

    if ( _data != NULL )
    {
        chk         = cudaCheck( cudaFree( _data ) );
        _data       = NULL;
        _data_bytes = 0;
    }

    return chk;
}

bool CCuFFT_Processor::ReleasePlans()
{
    bool chk = true;

    for ( auto & p : _plans )
        chk = chk && cufftCheck( cufftDestroy( p ) );

    _plans.clear();

    return chk;
}

bool CCuFFT_Processor::ReleaseAll()
{
    bool chk = true;

    chk = chk && cudaCheck( cudaDeviceSynchronize() );
    chk = chk && ReleaseMemory();
    chk = chk && ReleasePlans();
    chk = chk && cudaCheck( cudaDeviceReset() );

    return chk;
}

以下是主要功能的实现：

bool CCuFFT_Processor::Alloc( size_t data_len, size_t batch_len )
{
    bool   chk   = true;
    size_t bytes = sizeof( cufftComplex ) * data_len * batch_len;

    // CUDA resources

    if ( _data_bytes < bytes )
        chk = chk && ReleaseMemory();

    if ( _data == NULL )
    {
        chk         = chk && cudaCheck( cudaMalloc( (void **)&_data, bytes ) );
        _data_bytes = bytes;
    }

    // cuFFT resources

    chk = chk && ReleasePlans();

    for ( size_t b = 1; chk && ( b <= batch_len ); b *= 2 )
    {
        cufftHandle new_plan;

        chk = cufftCheck(
            cufftPlan1d( &new_plan, int(data_len), CUFFT_C2C, int(b) ) );

        if ( chk )
            _plans.push_back( new_plan );
    }

    if ( !chk )
        ReleaseAll();

    return chk;
}

bool CCuFFT_Processor::Run()
{
    bool chk = true;

    chk = cufftCheck(
        cufftExecC2C( *_plans.rbegin(), _data, _data, CUFFT_FORWARD ) );

    if ( !chk )
        ReleaseAll();

    chk = chk && cudaCheck( cudaDeviceSynchronize() );

    return chk;
}

最后，程序

int main()
{
    size_t batch  = 1 << 5;
    size_t length = 1 << 21;

    CCuFFT_Processor proc;

    // Normal run
    if ( proc.Alloc( length, batch ) )
        proc.Run();

    // Run with error
    length *= 4;

    if ( proc.Alloc( length, batch ) )
        proc.Run();

    // Normal run : check recovery
    length /= 4;

    if ( proc.Alloc( length, batch ) )
        proc.Run();

    return EXIT_SUCCESS;
}

如果我使用小length = 1 << 18，则不会发生错误。但是，对于较大的length = 1 << 21出现两个错误：

cuFFT failed to allocate GPU or CPU memory
Failed to execute an FFT on the GPU

第一个错误是预期错误，我们故意这样做。但第二个不是。虽然设备已重置且新资源已成功分配，但cuFFT无法执行FFT。

我使用GTX 970.我尝试了所有组合：cuda 6.5，cuda 7.5,32位平台，64位平台等，但都没有成功。

错误后cuFFT无法恢复

0 个答案: