C++ 矩阵乘以较慢的 OpenACC

Question

当我在使用 GCC 编译期间启用 OpenACC 时，我的代码速度变慢是否有原因？我目前在 Windows 10 上使用 GCC 6.3.0。我真的不确定为什么会这样。

这是我正在编译的命令：g++ -fopenacc -o a Example.cpp

这是我的 C++ 代码：

#include <stdlib.h>
#include <cassert>
#include <chrono>


double *A, *B, *C;


int main(int argc, char* argv[]) {

    long long N = 100;

    A = new double[N * N];
    B = new double[N * N];
    C = new double[N * N];

    srand(42);

    for (int i = 0; i < N; i++) {

        for (int j = 0; j < N; j++) {
            A[i * N + j] = rand();
            B[i * N + j] = rand();
        }
    }

    
    for (int x = 0; x < 10; x++) {
        auto start_time = std::chrono::high_resolution_clock::now();
        #pragma acc kernels 
        {
            #pragma acc loop independent
            for (int i = 0; i < N; i++) {
                #pragma acc loop independent
                for (int j = 0; j < N; j++) {
                    double total = 0;
                    #pragma acc loop independent reduction (+: total)
                    for (int k = 0; k < N; k++) {
                        total += A[i * N + j] * B[k * N + j];
                    }
                    C[i * N + j] = total;
                }
            }
        }
        


        auto end_time = std::chrono::high_resolution_clock::now();
        std::chrono::duration<double> duration = end_time - start_time;
        printf("%f seconds\n", duration.count());
    
    }

    

    return 0;
}

Answer 1

不确定 GNU 6.3 是否支持 OpenACC，至少不是很好，我也不知道 Windows 是否支持它。我在 Linux 上使用 GNU 10.2，其中 OpenACC 支持更好。

但是，GNU 仍然不能很好地处理“内核”指令，所以我建议改用并行。此外，您缺少数据区域，因此如果卸载此代码，您将收到运行时错误。

例如：

% g++ --version
g++ (GCC) 10.2.0
Copyright (C) 2020 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
%
% cat mm.cpp
#include <stdlib.h>
#include <stdio.h>
#include <cassert>
#include <chrono>

#ifdef USE_PARALLEL
#define ACC_TYPE parallel
#else
#define ACC_TYPE kernels
#endif

double *A, *B, *C;


int main(int argc, char* argv[]) {

    long long N = 100;

    A = new double[N * N];
    B = new double[N * N];
    C = new double[N * N];

    srand(42);

    for (int i = 0; i < N; i++) {

        for (int j = 0; j < N; j++) {
            A[i * N + j] = rand();
            B[i * N + j] = rand();
        }
    }

    #pragma acc data copyin(A[:N*N],B[:N*N]) copyout(C[:N*N])
    for (int x = 0; x < 10; x++) {
        auto start_time = std::chrono::high_resolution_clock::now();
        #pragma acc ACC_TYPE
        {
            #pragma acc loop independent
            for (int i = 0; i < N; i++) {
                #pragma acc loop independent
                for (int j = 0; j < N; j++) {
                    double total = 0;
                    #pragma acc loop independent reduction (+: total)
                    for (int k = 0; k < N; k++) {
                        total += A[i * N + j] * B[k * N + j];
                    }
                    C[i * N + j] = total;
                }
            }
        }
        auto end_time = std::chrono::high_resolution_clock::now();
        std::chrono::duration<double> duration = end_time - start_time;
        printf("%f seconds\n", duration.count());
    }
    return 0;
}
% g++ -fopenacc mm.cpp -Ofast ; a.out
0.020057 seconds
0.020025 seconds
0.020022 seconds
0.020021 seconds
0.019538 seconds
0.018271 seconds
0.018270 seconds
0.018264 seconds
0.018274 seconds
0.018270 seconds
% g++ -fopenacc mm.cpp -Ofast -DUSE_PARALLEL ; a.out
0.000123 seconds
0.000086 seconds
0.000081 seconds
0.000078 seconds
0.000078 seconds
0.000077 seconds
0.000077 seconds
0.000076 seconds
0.000076 seconds
0.000076 seconds

C++ 矩阵乘以较慢的 OpenACC

C++ Matrix Multiply Slower OpenACC

c++

parallel-processing

gcc

matrix-multiplication

openacc