The stackoverflow link above contains a simple testcase: >>> from scipy.linalg import get_blas_funcs>>> gemm = get_blas_funcs("gemm", >>> [X, Y])>>> np.all(gemm(1, X, Y) == np.dot(X, Y))True
It would be of interest to benchmark gemm against np.dot. Maybe np.dot doesn't use blas at al for whatever reason? Am Di., 23. Feb. 2021 um 20:46 Uhr schrieb David Menéndez Hurtado < davidmen...@gmail.com>: > On Tue, 23 Feb 2021, 7:41 pm Roman Yurchak, <rth.yurc...@gmail.com> wrote: > >> For the first benchmark apparently A.dot(B) with A real and B complex is >> a known issue performance wise >> https://github.com/numpy/numpy/issues/10468 > > > I splitted B into a vector of size (N, 2) for the real and imaginary part, > and that makes the multiplication twice as fast. > > > My configuration (also in Fedora 33) np.show_config() > > > > blas_mkl_info: > NOT AVAILABLE > blis_info: > NOT AVAILABLE > openblas_info: > libraries = ['openblas', 'openblas'] > library_dirs = ['/usr/local/lib'] > language = c > define_macros = [('HAVE_CBLAS', None)] > blas_opt_info: > libraries = ['openblas', 'openblas'] > library_dirs = ['/usr/local/lib'] > language = c > define_macros = [('HAVE_CBLAS', None)] > lapack_mkl_info: > NOT AVAILABLE > openblas_lapack_info: > libraries = ['openblas', 'openblas'] > library_dirs = ['/usr/local/lib'] > language = c > define_macros = [('HAVE_CBLAS', None)] > lapack_opt_info: > libraries = ['openblas', 'openblas'] > library_dirs = ['/usr/local/lib'] > language = c > define_macros = [('HAVE_CBLAS', None)] > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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