Description
I'm trying to use cblas_domatcopy
to transpose large row-major matrices.
I'm finding that the function is slower than a simple loop of cblas_dcopy
calls parallelized with OpenMP (with number of threads set to number of logical cores, otherwise OMP loop is much slower).
Function cblas_domatcopy
appears to be especially slower when the inputs have more columns than rows - in this sense, in a dcopy
loop, there's also a large timing difference according to whether the copies are by rows of the input or of the output, and I'm guessing that perhaps omatcopy
always follows the same order.
(code is provided at the end of this post)
-
Timings in seconds on an intel 12700H, average of 7 runs:
- Input size: 100,000 x 5x000
- OpenBLAS
cblas_domatcopy
: 3.12 - OpenMP
dcopy
loop: 2.38 - MKL
MKL_Domatcopy
: 1.26
- OpenBLAS
- Input size: 5,000 x 100,000
- OpenBLAS
cblas_domatcopy
: 3.74 - OpenMP
dcopy
loop: 1.23 - MKL
MKL_Domatcopy
: 1.27
- OpenBLAS
- Input size: 100,000 x 5x000
-
Timings in seconds on an amd ryzen 7840HS, average of 7 runs:
- Input size: 100,000 x 5x000
- OpenBLAS
cblas_domatcopy
: 0.922 - OpenMP
dcopy
loop: 0.586 - MKL
MKL_Domatcopy
: 0.560
- OpenBLAS
- Input size: 5,000 x 100,000
- OpenBLAS
cblas_domatcopy
: 1.12 - OpenMP
dcopy
loop: 0.402 - MKL
MKL_Domatcopy
: 0.516
- OpenBLAS
- Input size: 100,000 x 5x000
OpenBLAS version: 0.3.26, OpenMP variant.
Code that I'm using for the OMP dcopy
loop:
void transpose_mat(const double *A, const int nrows, const int ncols, double *B, int nthreads)
{
if (nrows >= ncols)
{
#pragma omp parallel for schedule(static) num_threads(nthreads)
for (int row = 0; row < nrows; row++)
cblas_dcopy(ncols, A + (size_t)row*(size_t)ncols, 1, B + row, nrows);
}
else
{
#pragma omp parallel for schedule(static) num_threads(nthreads
for (int col = 0; col < ncols; col++)
cblas_dcopy(nrows, A + col, ncols, B + (size_t)col*(size_t)nrows, 1);
}
}