为什么Matlab矩阵求逆比numpy更快？

Question

This is related to several questions that discuss the speed of numpy vs Matlab. However most of them have several matrix operations than a single operation. Eg Difference on performance between numpy and matlab

For me, the time numpy take just to invert a random matrix is approximately 5 times slower than that of matlab.

Here is the matlab script,

N = 1000;

B = randn(N,N);
h = tic;
T = 40;
for i=1:40

    Rinv = (B)^(-1);
end
toc(h)/40

This gives an average values of 0.08 seconds approximately.

While this python script gives 0.4 seconds (approx).

import numpy as np 
from numpy import linalg as LA
import time 

N=1000
R = np.random.random((N,N))
T=40

t1 = time.clock()
for i in range(0,T):
    Rinv = LA.inv(R)
t2 = time.clock()
print 'avg time for inverse ',(t2-t1)/T

Is there any reason for this, or anyway to improve python performance ? I have already implemented my work on Python and I am worried whether I will have to port all my code to matlab. I am working on Ubuntu 16.04, Python 2.7, Matlab R2016b.

I have read that the time is not a good module for execution time comparisons, I feel this is something more than that.

Answer 1

On my computer (Windows, python 3.5, numpy 1.11.2) :

In [6]: %timeit inv(a)
10 loops, best of 3: 86 ms per loop

edit:

or, without Ipython:

>>>timeit.timeit('inv(a)','from __main__ import inv,a',number=100)/100

which is similar to Matlab.

to know what code is used in the background, check it :

In [12]: np.__config__.show() 
blas_mkl_info:
include_dirs = ['c:/users/bruno/miniconda3\\Library\\include']
libraries = ['mkl_core_dll', 'mkl_intel_lp64_dll', 'mkl_intel_thread_dll']
...