为什么Matlab矩阵求逆比numpy更快? [英] Why Matlab matrix inversion is faster than numpy?
问题描述
这与讨论numpy与Matlab的速度的几个问题有关.但是,它们中的大多数都比单个运算具有多个矩阵运算.例如. numpy和matlab之间的性能差异
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. E.g. Difference on performance between numpy and matlab
对我来说,numpy用来反转随机矩阵所需的时间比matlab慢大约5倍.
For me, the time numpy take just to invert a random matrix is approximately 5 times slower than that of matlab.
这是matlab脚本,
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
这平均约为0.08秒.
This gives an average values of 0.08 seconds approximately.
尽管此python脚本给出了0.4秒(大约)的时间.
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
是否有任何理由,或者无论如何要提高python性能? 我已经在Python上实现了我的工作,我担心是否 我将必须将所有代码移植到matlab. 我正在使用Ubuntu 16.04,Python 2.7,Matlab R2016b.
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.
我已经阅读到time
不是执行时间比较的好模块,我觉得这还不止于此.
I have read that the time
is not a good module for execution time comparisons, I feel this is something more than that.
推荐答案
在我的计算机上(Windows,python 3.5,numpy 1.11.2):
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
或者,没有Ipython:
edit:
or, without Ipython:
>>>timeit.timeit('inv(a)','from __main__ import inv,a',number=100)/100
类似于Matlab.
要知道在后台使用了什么代码,请检查它:
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']
...
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