我基本上已经回答了这个问题

I essentially already answer this question parallelizing-matrix-times-a-vector-by-columns-and-by-rows-with-openmp.

写入results[y]时，您处于竞争状态.要解决此问题并仍然并行化内部循环，您必须制作私有版本的results[y]，并行填充它们，然后将其合并到关键部分.

You have a race condition when you write to results[y]. To fix this, and still parallelize the inner loop, you have to make private versions of results[y], fill them in parallel, and then merge them in a critical section.

在下面的代码中，我假设您正在使用double，将其替换为float或int或您使用的任何数据类型(请注意，您的内部循环遍历了matrix[i][y]的第一个索引缓存不友好).

In the code below I assume you're using double, replace it with float or int or whatever datatype you're using (note that your inner loop goes over the first index of matrix[i][y] which is cache unfriendly).

#pragma omp parallel num_threads(4)
{
    int y,i;
    double* results_private = (double*)calloc(matrix_size, sizeof(double));
    for(y = 0; y < matrix_size ; y++) {
        #pragma omp for
        for(i = 0; i < matrix_size; i++) {
            results_private[y] += vector[i]*matrix[i][y];   
        }
    }
    #pragma omp critical
    {
        for(y=0; y<matrix_size; y++) results[y] += results_private[y];
    }
    free(results_private);
}

如果这是家庭作业，并且您想给老师留下深刻的印象，则可以在没有关键部分的情况下进行合并.请参阅此链接以获取有关操作的想法

If this is homework assignment and you want to really impress your instructor then it's possible to do the merging without a critical section. See this link to get an idea on what to do fill-histograms-array-reduction-in-parallel-with-openmp-without-using-a-critic though I can't promise it will be faster.