在矩阵向量乘法中使用 OpenMP“for simd”？

Question

I'm currently trying to get my matrix-vector multiplication function to compare favorably with BLAS by combining #pragma omp for with #pragma omp simd , but it's not getting any speedup improvement than if I were to just use the for construct. How do I properly vectorize the inner loop with OpenMP's SIMD construct?

vector dot(const matrix& A, const vector& x)
{
  assert(A.shape(1) == x.size());

  vector y = xt::zeros<double>({A.shape(0)});

  int i, j;
#pragma omp parallel shared(A, x, y) private(i, j)
  {
#pragma omp for // schedule(static)
    for (i = 0; i < y.size(); i++) { // row major
#pragma omp simd
      for (j = 0; j < x.size(); j++) {
        y(i) += A(i, j) * x(j);
      }
    }
  }

  return y;
}

Answer 1

Your directive is incorrect because there would introduce in a race condition (on y(i) ). You should use a reduction in this case. Here is an example:

vector dot(const matrix& A, const vector& x)
{
  assert(A.shape(1) == x.size());

  vector y = xt::zeros<double>({A.shape(0)});

  int i, j;

  #pragma omp parallel shared(A, x, y) private(i, j)
  {
    #pragma omp for // schedule(static)
    for (i = 0; i < y.size(); i++) { // row major
      decltype(y(0)) sum = 0;

      #pragma omp simd reduction(+:sum)
      for (j = 0; j < x.size(); j++) {
        sum += A(i, j) * x(j);
      }

      y(i) += sum;
    }
  }

  return y;
}

Note that it may not be necessary faster because some compilers are able to automatically vectorize the code (ICC for example). GCC and Clang often fail to perform (advanced) SIMD reductions automatically and such a directive help them a bit. You can check the assembly code to check how the code is vectorized or enable vectorization reports (see here for GCC).