我在MATLAB和Python中设置了两个关于广播矩阵乘法的相同测试。对于Python，我使用NumPy，对于MATLAB，我使用了使用BLAS的mtimesx库。在

MATLABclose all; clear;

N = 1000 + 100; % a few initial runs to be trimmed off at the end

a = 100;

b = 30;

c = 40;

d = 50;

A = rand(b, c, a);

B = rand(c, d, a);

C = zeros(b, d, a);

times = zeros(1, N);

for ii = 1:N

tic

C = mtimesx(A,B);

times(ii) = toc;

end

times = times(101:end) * 1e3;

plot(times);

grid on;

title(median(times));

Python

^{pr2}$

mypython是从使用OpenBLAS的pip安装的默认Python。在

我用的是英特尔NUC i3。在

MATLAB代码在1ms内运行，而Python在5.8ms内运行，我不明白为什么，因为它们似乎都在使用BLAS。在

编辑

来自水蟒：In [7]: np.__config__.show()

mkl_info:

libraries = ['mkl_rt']

library_dirs = [...]

define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]

include_dirs = [...]

blas_mkl_info:

libraries = ['mkl_rt']

library_dirs = [...]

define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]

include_dirs = [...]

blas_opt_info:

libraries = ['mkl_rt']

library_dirs = [...]

define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]

include_dirs = [...]

lapack_mkl_info:

libraries = ['mkl_rt']

library_dirs = [...]

define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]

include_dirs = [...]

lapack_opt_info:

libraries = ['mkl_rt']

library_dirs = [...]

define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]

include_dirs = [...]

从numpy使用pipIn [2]: np.__config__.show()

blas_mkl_info:

NOT AVAILABLE

blis_info:

NOT AVAILABLE

openblas_info:

library_dirs = [...]

libraries = ['openblas']

language = f77

define_macros = [('HAVE_CBLAS', None)]

blas_opt_info:

library_dirs = [...]

libraries = ['openblas']

language = f77

define_macros = [('HAVE_CBLAS', None)]

lapack_mkl_info:

NOT AVAILABLE

openblas_lapack_info:

library_dirs = [...]

libraries = ['openblas']

language = f77

define_macros = [('HAVE_CBLAS', None)]

lapack_opt_info:

library_dirs = [...]

libraries = ['openblas']

language = f77

define_macros = [('HAVE_CBLAS', None)]

编辑2

我试着用np.matmul(A, B, out=C)代替C = A @ B，结果得到了2倍的时间，比如11毫秒左右，真的很奇怪。在