为什么我的包含扫描码在 CPU 上比在 GPU 上快 2 倍

Question

我写了一个简短的 CUDA 程序，它使用高度优化的 CUB 库来演示来自旧的四核英特尔 Q6600 处理器的一个内核（所有四个处理器都应该能够达到 ~30 GFLOPS/秒）可以比 Nvidia 750 Ti（据说能够达到 1306 GFLOPS/秒的单精度）对 100,000 个元素进行包容性扫描（或累积/前缀总和）。 为什么会这样呢？

源代码为：

#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <cub/cub.cuh>
#include <stdio.h>
#include <time.h>
#include <algorithm>

#define gpuErrchk(ans) { gpuAssert((ans), __FILE__, __LINE__); }
inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort = true)
{
    if (code != cudaSuccess)
    {
        fprintf(stderr, "GPUassert: %s %s %dn", cudaGetErrorString(code), file, line);
        if (abort) exit(code);
    }
}
void fillArrayWithRandom(float* inputArray, int inputN)
{
    for (int i = 0; i < inputN; i++)
    {
        inputArray[i] = (float)rand() / float(RAND_MAX);
    }
}
void inclusiveSum_CPU(float *inputArray, float *inputSummedArray, int inputN)
{
    for (int i = 0; i < inputN; i++)
    {
        if (i > 0)
        {
            inputSummedArray[i] = inputSummedArray[i - 1] + inputArray[i];
        }
        else
        {
            inputSummedArray[i] = inputArray[i];
        }
    }
}
int main()
{
    int N = 100000; //1 hundred thousand elements
    float numSimulations = 10000;
    //Make Host Arrays
    float* testArray_CPU = (float *)malloc(sizeof(float)*N);
    fillArrayWithRandom(testArray_CPU, N);
    float* testArrayOutput_CPU = (float *)malloc(sizeof(float)*N);
    //Make GPU Arrays
    float* testArray_GPU;
    gpuErrchk(cudaMalloc(&testArray_GPU, N*sizeof(float)));
    gpuErrchk(cudaMemcpy(testArray_GPU, testArray_CPU, N*sizeof(float), cudaMemcpyHostToDevice));
    float* testArrayOutput_GPU;
    gpuErrchk(cudaMalloc(&testArrayOutput_GPU, N*sizeof(float)));
    //Initiate the benchmark variables
    clock_t begin_CPU, end_CPU;
    float time_spent_GPU, time_spent_CPU;
    //GPU prep
    void     *d_temp_storage = NULL;
    size_t   temp_storage_bytes = 0;
    cub::DeviceScan::InclusiveSum(d_temp_storage, temp_storage_bytes, testArray_GPU, testArrayOutput_GPU, N);
    gpuErrchk(cudaMalloc(&d_temp_storage, temp_storage_bytes));
    //GPU Timing
    cudaEvent_t start, stop;
    gpuErrchk(cudaEventCreate(&start));
    gpuErrchk(cudaEventCreate(&stop));
    gpuErrchk(cudaEventRecord(start, 0));
    for (int i = 0; i < numSimulations; i++)
    {
        cub::DeviceScan::InclusiveSum(d_temp_storage, temp_storage_bytes, testArray_GPU, testArrayOutput_GPU, N);
    }
    gpuErrchk(cudaDeviceSynchronize());
    gpuErrchk(cudaEventRecord(stop, 0));
    gpuErrchk(cudaEventSynchronize(stop));
    gpuErrchk(cudaEventElapsedTime(&time_spent_GPU, start, stop));
    cudaError_t error = cudaGetLastError();
    if (error != cudaSuccess)
    {
        printf("CUDA error: %sn", cudaGetErrorString(error));
        exit(-1);
    }
    time_spent_GPU = (float)(time_spent_GPU / 1000);
    float avg_GPU = time_spent_GPU / numSimulations;
    printf("Avg. GPU Simulation Time: %.17g [sim/sec]n", avg_GPU);
    //CPU Timing
    begin_CPU = clock();
    for (int i = 0; i < numSimulations; i++)
    {
        inclusiveSum_CPU(testArray_CPU, testArrayOutput_CPU, N);
    }
    end_CPU = clock();
    time_spent_CPU = (float)(end_CPU - begin_CPU) / CLOCKS_PER_SEC;
    float avg_CPU = time_spent_CPU / numSimulations;
    printf("Avg. CPU Simulation Time: %.17g [sim/sec]n", avg_CPU);
    printf("GPU/CPU Timing:%.17gx n", avg_GPU / avg_CPU);
    return 0;
}

当我在我的机器上运行它时，输出是：

平均 GPU 仿真时间：0.0011999999405816197 [sim/sec]

平均 CPU 模拟时间：0.00059999997029080987 [sim/sec]

图形处理器/中央处理器时序：2x

另外，这是我的编译标志和输出：

1>------ Build started: Project: speedTest, Configuration: Debug Win32 ------
1>  Compiling CUDA source file kernel.cu...
1>  
1>  C:UsersOwnerDocumentsVisual Studio 2013ProjectsspeedTestspeedTest>"C:Program FilesNVIDIA GPU Computing ToolkitCUDAv6.5binnvcc.exe" -gencode=arch=compute_50,code="sm_50,compute_50" --use-local-env --cl-version 2013 -ccbin "C:Program Files (x86)Microsoft Visual Studio 12.0VCbin" -rdc=true -I"C:Program FilesNVIDIA GPU Computing ToolkitCUDAv6.5include" -I"C:Program FilesNVIDIA GPU Computing ToolkitCUDAv6.5include"  -G   --keep-dir Debug -maxrregcount=0  --machine 32 --compile -cudart static  -g   -DWIN32 -D_DEBUG -D_CONSOLE -D_MBCS -Xcompiler "/EHsc /W3 /nologo /Od /Zi /RTC1 /MDd  " -o Debugkernel.cu.obj "C:UsersOwnerDocumentsVisual Studio 2013ProjectsspeedTestspeedTestkernel.cu" 
1>  kernel.cu
1>  
1>  C:UsersOwnerDocumentsVisual Studio 2013ProjectsspeedTestspeedTest>"C:Program FilesNVIDIA GPU Computing ToolkitCUDAv6.5binnvcc.exe" -dlink -o DebugspeedTest.device-link.obj -Xcompiler "/EHsc /W3 /nologo /Od /Zi /RTC1 /MDd  " -L"C:Program FilesNVIDIA GPU Computing ToolkitCUDAv6.5libWin32" cudart.lib kernel32.lib user32.lib gdi32.lib winspool.lib comdlg32.lib advapi32.lib shell32.lib ole32.lib oleaut32.lib uuid.lib odbc32.lib odbccp32.lib  -gencode=arch=compute_50,code=sm_50 -G --machine 32 Debugkernel.cu.obj 
1>  cudart.lib
1>  kernel32.lib
1>  user32.lib
1>  gdi32.lib
1>  winspool.lib
1>  comdlg32.lib
1>  advapi32.lib
1>  shell32.lib
1>  ole32.lib
1>  oleaut32.lib
1>  uuid.lib
1>  odbc32.lib
1>  odbccp32.lib
1>  kernel.cu.obj
1>  speedTest.vcxproj -> C:UsersOwnerDocumentsVisual Studio 2013ProjectsspeedTestDebugspeedTest.exe
1>  copy "C:Program FilesNVIDIA GPU Computing ToolkitCUDAv6.5bincudart*.dll" "C:UsersOwnerDocumentsVisual Studio 2013ProjectsspeedTestDebug"
1>  C:Program FilesNVIDIA GPU Computing ToolkitCUDAv6.5bincudart32_65.dll
1>  C:Program FilesNVIDIA GPU Computing ToolkitCUDAv6.5bincudart64_65.dll
1>          2 file(s) copied.
========== Build: 1 succeeded, 0 failed, 0 up-to-date, 0 skipped ==========

Answer 1

感谢Robert Crovella，事实证明我使用的是众所周知的慢速"调试"模式，而不是"发布"模式。