Speed of Python
wang frank
fw3 at hotmail.co.jp
Sat Sep 8 00:59:26 CEST 2007
I also have tried to use numpy to speed it up. However, surprisingly, it is
slower than the pure python code.
Here is the code:
import numpy
arange=numpy.arange
nlog=numpy.log
def bench6(n):
for i in xrange(n):
for j in xrange(1000):
m=j+1
z=nlog(m)
z1=nlog(m+1)
z2=nlog(m+2)
z3=nlog(m+3)
z4=nlog(m+4)
z5=nlog(m+5)
z6=nlog(m+6)
z7=nlog(m+7)
z8=nlog(m+8)
z9=nlog(m+9)
return z9
from math import log
def bench3(n):
for i in xrange(n):
for j in xrange(1000):
# m=j+1
z=log(j+1)
z1=log(j+2)
z2=log(j+3)
z3=log(j+4)
z4=log(j+5)
z5=log(j+6)
z6=log(j+7)
z7=log(j+8)
z8=log(j+9)
z9=log(j+10)
return z9
Here is the result:
>>> t6=timeit.Timer("bench1.bench6(10)", "import bench1")
>>> t6.repeat(1,1)
[0.73878858905254674]
>>> t3=timeit.Timer("bench1.bench3(10)", "import bench1")
>>> t3.repeat(1,1)
[0.056632337350038142]
Anyone know why?
Thanks
Frank
>From: "Kurt Smith" <kwmsmith at gmail.com>
>To: "wang frank" <fw3 at hotmail.co.jp>
>Subject: Re: Speed of Python
>Date: Fri, 7 Sep 2007 16:49:05 -0500
>
>On 9/7/07, wang frank <fw3 at hotmail.co.jp> wrote:
> > Hi,
> > Here is the matlab code:
> > function [z]=bench1(n)
> > for i=1:n,
> > for j=1:1000,
> > z=log(j);
> > z1=log(j+1);
> > z2=log(j+2);
> > z3=log(j+3);
> > z4=log(j+4);
> > z5=log(j+5);
> > z6=log(j+6);
> > z7=log(j+7);
> > z8=log(j+8);
> > z9=log(j+9);
> > end
> > end
> > z = z9;
> >
> > I am not familiar with python, so I just simply try to reproduce the
same
> > code in python.
> > If you think that my python script is not efficient, could you tell me
how
> > to make it more efficient?
>
>One thing you can do is bind math.log to the function's namespace thusly:
>
>import math
>def bench1_opt(n):
> log = math.log
> for i in range(n):
> for j in range(1000):
> m=j+1
> z=log(m)
> z1=log(m+1)
> z2=log(m+2)
> z3=log(m+3)
> z4=log(m+4)
> z5=log(m+5)
> z6=log(m+6)
> z7=log(m+7)
> z8=log(m+8)
> z9=log(m+9)
> return z9
>
>On my system I get about a 20% speedup over the 'unoptimized' version
>(even though this optimization is rather trivial and may even help
>readability). Still not matlab speed, but better. You might be able
>to do better using xrange instead of range, but the loop overhead
>isn't the main slowdown (only about 1% ).
>
>For comparisons in real-world usage (if you are doing numerical work),
>I urge you to take a look at a specifically numerical package --
>numpy/scipy or their equivalents: http://www.scipy.org/ Python is a
>*very* general language not suited for heavy numerical work out of the
>box -- dedicated numerical packages adapt python to this specialized
>envirornment, and are becoming more and more competitive with Matlab.
>The best part is you can put your time-critical code in FORTRAN or C
>and wrap it with pyrex, f2py, weave, etc. pretty easily, and still
>have the beauty of Python gluing everything together.
>
>Kurt
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