Question

我写了一个简短的CUDA程序，使用highly-optimized CUB library来证明旧的四核Intel Q6600处理器（所有四个据称能够达到~30 GFLOPS /秒）的一个核心可以进行包容性扫描（或者说累积/前缀总和，如果您更喜欢）比Nvidia 750 Ti（假设能够达到单精度1306 GFLOPS /秒）的100,000个元素更快。为什么会这样？

源代码是：

#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 %d\n", 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: %s\n", 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;
}

我在机器上运行时的输出是：

平均。 GPU模拟时间：0.0011999999405816197 [sim / sec]

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

GPU / CPU时序：2x

此外，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:\Users\Owner\Documents\Visual Studio 2013\Projects\speedTest\speedTest>"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v6.5\bin\nvcc.exe" -gencode=arch=compute_50,code=\"sm_50,compute_50\" --use-local-env --cl-version 2013 -ccbin "C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\bin" -rdc=true -I"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v6.5\include" -I"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v6.5\include"  -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 Debug\kernel.cu.obj "C:\Users\Owner\Documents\Visual Studio 2013\Projects\speedTest\speedTest\kernel.cu" 
1>  kernel.cu
1>  
1>  C:\Users\Owner\Documents\Visual Studio 2013\Projects\speedTest\speedTest>"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v6.5\bin\nvcc.exe" -dlink -o Debug\speedTest.device-link.obj -Xcompiler "/EHsc /W3 /nologo /Od /Zi /RTC1 /MDd  " -L"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v6.5\lib\Win32" 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 Debug\kernel.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:\Users\Owner\Documents\Visual Studio 2013\Projects\speedTest\Debug\speedTest.exe
1>  copy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v6.5\bin\cudart*.dll" "C:\Users\Owner\Documents\Visual Studio 2013\Projects\speedTest\Debug\"
1>  C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v6.5\bin\cudart32_65.dll
1>  C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v6.5\bin\cudart64_65.dll
1>          2 file(s) copied.
========== Build: 1 succeeded, 0 failed, 0 up-to-date, 0 skipped ==========

Answer 1

感谢Robert Crovella，事实证明我正在使用＆＃34; Debug＆＃34;模式是出了名的慢而不是＆＃34;发布＆＃34;模式。

为什么我的包容性扫描码在CPU上比在GPU上快2倍？

1 个答案: