Thanks for all your help.
Using Istvan Albert's suggestion, I have recompared the speed of the
following funciton:
matlab:
function [z]=bench2(n)
for i=1:n,
j=(0:999);
z=log(j+1);
end
python:
from numpy import arange, log
def bench4(n):
for i in xrange(n):
nums = arange( n )
a = log( nums + 1)
Python actually is faster than Matlab.
tic; z=bench2(1000); toc
Elapsed time is 0.159485 seconds.
Python:
import timeit
t=timeit.Timer("bench1.bench4(1000)","import bench1")
t.repeat(1,1)
[0.10052953657517924]
Thanks again.
Frank
From: ajaksu <[EMAIL PROTECTED]>
To: python-list@python.org
Subject: Re: Speed of Python
Date: Fri, 07 Sep 2007 19:27:45 -0000
On Sep 7, 2:37 pm, "wang frank" <[EMAIL PROTECTED]> wrote:
> I am not familiar with python, so I just simply try to reproduce the
same
> code in python.
Seems almost correct, but from what I guess of MatLab, George's
suggestions make it a bit more fair.
> If you think that my python script is not efficient, could you tell me
how
> to make it more efficient?
In pure Python? No idea (besides using Roberto's and George's
suggestions). If you allow for extensions, Istvan has the answer. If
you allow compiling Python to C++ (using ShedSkin:
http://shed-skin.blogspot.com/),
here's a small report:
----------
[EMAIL PROTECTED]:~/sandbox$ cat bench.py
import math
n = 1
def bench1(n):
for i in range(n):
for j in range(1000):
m=j+1
z=math.log(m)
z1=math.log(m+1)
z2=math.log(m+2)
z3=math.log(m+3)
z4=math.log(m+4)
z5=math.log(m+5)
z6=math.log(m+6)
z7=math.log(m+7)
z8=math.log(m+8)
z9=math.log(m+9)
return z9
a = bench1(10)
[EMAIL PROTECTED]:~/sandbox$ ss -e bench.py
*** SHED SKIN Python-to-C++ Compiler 0.0.22 ***
Copyright 2005-2007 Mark Dufour; License GNU GPL version 2 (See
LICENSE)
(Please send bug reports here: [EMAIL PROTECTED])
[iterative type analysis..]
**
iterations: 2 templates: 44
[generating c++ code..]
[EMAIL PROTECTED]:~/sandbox$ make bench.so
g++ -O3 -s -pipe -fomit-frame-pointer -I/home/ajaksu/shedskin-0.0.22/
lib -g -fPIC -I/usr/include/python2.5 -D__SS_BIND /home/ajaksu/
shedskin-0.0.22/lib/builtin.cpp /home/ajaksu/shedskin-0.0.22/lib/
math.cpp bench.cpp -lgc -shared -Xlinker -export-dynamic -lpython2.5 -
o bench.so
[EMAIL PROTECTED]:~/sandbox$ mv bench.py pbench.py
[EMAIL PROTECTED]:~/sandbox$ ipython
Python 2.5.1 (r251:54863, May 2 2007, 16:56:35)
[...]
In [1]: from pbench import bench1 as pbench1
In [2]: from bench import bench1
In [3]: %timeit a = bench1(10)
100 loops, best of 3: 10.2 ms per loop
In [4]: %timeit a = pbench1(10)
10 loops, best of 3: 92.8 ms per loop
----------
I guess you'd also see nice improvements from Pyrex or Cython, Blitz
and other tools. Check
http://wiki.python.org/moin/PythonSpeed/PerformanceTips
for the general ideas and http://scipy.org/PerformancePython for an
insight on available tools that even compares their speeds to Matlab.
And-if-you-run-more-benchmarks-please-do-post-them-ly yrs,
Daniel
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