How to Efficiently Perform Array Reductions in OpenMP?

Solving Array Reductions in OpenMP

In OpenMP, directly reducing on arrays is not supported. However, alternative methods exist to achieve similar results.

First Method:

One approach involves creating private copies of the array for each thread and reducing them locally. After the parallel section, merge the private arrays into the original array using a critical section to prevent data races.

int S[10] = {0};

#pragma omp parallel
{
    int S_private[10] = {0};

    #pragma omp for
    for (int n = 0; n < 10; ++n)
    {
        for (int m = 0; m <= n; ++m)
        {
            S_private[n] += A[m];
        }
    }

    #pragma omp critical
    {
        for (int n = 0; n < 10; ++n)
        {
            S[n] += S_private[n];
        }
    }
}

Second Method:

Another way is to allocate a larger array with dimensions equal to the array size multiplied by the number of threads. Each thread then fills its portion of the array. After the parallel section, merge the values into the original array without using a critical section.

int S[10] = {0};
int *S_private;

#pragma omp parallel
{
    const int nthreads = omp_get_num_threads();
    const int ithread = omp_get_thread_num();

    #pragma omp single
    {
        S_private = new int[10 * nthreads];
        for (int i = 0; i < (10 * nthreads); i++)
        {
            S_private[i] = 0;
        }
    }

    #pragma omp for
    for (int n = 0; n < 10; ++n)
    {
        for (int m = 0; m <= n; ++m)
        {
            S_private[ithread * 10 + n] += A[m];
        }
    }

    #pragma omp for
    for (int i = 0; i < 10; i++)
    {
        for (int t = 0; t < nthreads; t++)
        {
            S[i] += S_private[10 * t + i];
        }
    }
}

The second method is more efficient, especially in scenarios involving multiple sockets, but it also requires careful memory handling to avoid cache issues.

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