C ++ openMP并行矩阵乘法

时间:2013-05-23 11:21:57

标签: c++ openmp

我的openMP代码出了什么问题?它总是只需要1个线程,并且与非并行版本

同时工作
template <typename T>
Matrix<T>* Matrix<T>::OMPMultiplication(Matrix<T>* A, Matrix<T>* B){ 

    if(A->ySize != B->xSize)
      throw;

    Matrix<T>* C = new Matrix<T>(A->xSize, B->ySize);

    sizeType i, j, k;
    T element;

    #pragma omp parallel for private(i, j)
    {
      #pragma omp for private(i, j)
      for( i = 0; i < A->xSize; i++ )
          cout<<"There are "<<omp_get_num_threads()<<" threads"<<endl;

          for(j = 0; j < B->ySize; j++){

              C->matrix[i][j] = 0;
              for(k = 0; k < A->ySize; k++){
                  C->matrix[i][j] += A->matrix[i][k] * B->matrix[k][j]; 
              }   

      }   
    }   
    return C;
}

2 个答案:

答案 0 :(得分:1)

首先,您遗漏了{}循环的某些i,并且变量k需要对i循环的每次迭代设置为私有。但是,我认为您还混淆了parallelfor pragma的组合方式。要成功并行化for循环,需要将其放在parallel pragma中,然后放在for pragma中。为此,您可以将代码更改为

#pragma omp parallel private(i, j, k)
{
    #pragma omp for
    for( i = 0; i < A->xSize; i++ ) {
        cout<<"There are "<<omp_get_num_threads()<<" threads"<<endl;

        for(j = 0; j < B->ySize; j++) {

            C->matrix[i][j] = 0;
            for(k = 0; k < A->ySize; k++){
                C->matrix[i][j] += A->matrix[i][k] * B->matrix[k][j]; 
            }   

        }
    }
}

或使用合并的parallel for表示法

#pragma omp parallel for private(i, j, k)
for( i = 0; i < A->xSize; i++ ) {
    ...
}

另外,请确保告诉OpenMP在此处使用多个线程。这可以通过omp_set_num_threads(<number of threads here>)和设置OMP_NUM_THREADS等环境变量来完成。

希望你能并行化。 :)

答案 1 :(得分:1)

使用此代码,我的4核心得到的结果略快一些:

    omp_set_num_threads(4);
    #pragma omp parallel for
    for (i = 0; i < n; i++) {
        for (j = 0; j < n; j++) {
            c[i] += b[j] * a[j][i];
        }
    }

完整计划

#include <stdio.h>
#include <time.h>
#include <omp.h>
#include <stdlib.h>


int main() {
    int i, j, n, a[719][719], b[719], c[719];

    clock_t start = clock();

    n = 100; //Max 719

    printf("Matrix A\n");

    for (i = 0; i < n; ++i) {
        for (j = 0; j < n; ++j) {
            a[i][j] = 10;
            printf("%d ", a[i][j]);
        }
        printf("\n");
    }

    printf("\nMatrix B\n");

#pragma omp parallel private(i) shared(b)
    {
#pragma omp for
        for (i = 0; i < n; ++i) {
            b[i] = 5;
            printf("%d\n", b[i]);
        }
    }

    printf("\nA * B\n");

#pragma omp parallel private(i) shared(c)
    {
#pragma omp for
        for (i = 0; i < n; ++i) {
            c[i] = 0;
        }
    }

#pragma omp parallel private(i,j) shared(n,a,b,c)
    {
#pragma omp for schedule(dynamic)
        for (i = 0; i < n; ++i) {
            for (j = 0; j < n; ++j) {
                c[i] += b[j] * a[j][i];
            }
        }
    }


#pragma omp parallel private(i) shared(c)
    {
#pragma omp for
        for (i = 0; i < n; ++i) {
            printf("%d\n", c[i]);
        }
    }

    clock_t stop = clock();
    double elapsed = (double) (stop - start) / CLOCKS_PER_SEC;
    printf("\nTime elapsed: %.5f\n", elapsed);
    start = clock();
    printf("Matrix A\n");

    for (i = 0; i < n; ++i) {
        for (j = 0; j < n; ++j) {
            a[i][j] = 10;
            printf("%d ", a[i][j]);
        }
        printf("\n");
    }

    printf("\nMatrix B\n");

#pragma omp parallel private(i) shared(b)
    {
#pragma omp for
        for (i = 0; i < n; ++i) {
            b[i] = 5;
            printf("%d\n", b[i]);
        }
    }
    printf("\nA * B\n");
    omp_set_num_threads(4);
#pragma omp parallel for
    for (i = 0; i < n; i++) {
        for (j = 0; j < n; j++) {
            c[i] += b[j] * a[j][i];
        }
    }
    stop = clock();
    elapsed = (double) (stop - start) / CLOCKS_PER_SEC;
    printf("\nTime elapsed: %.5f\n", elapsed);
    return 0;
}

第一种方法需要

  

已过去的时间:0.03442

第二种方法

  

已过去的时间:0.02630