[Numpy-discussion] speed of numpy.ndarray compared to Numeric.array

[EMMEL Thomas]

Hi,

There are some discussions on the speed of numpy compared to Numeric in this list, however I have a topic
I don't understand in detail, maybe someone can enlighten me...
I use python 2.6 on a SuSE installation and test this:

#Python 2.6 (r26:66714, Mar 30 2010, 00:29:28)
#[GCC 4.3.2 [gcc-4_3-branch revision 141291]] on linux2
#Type "help", "copyright", "credits" or "license" for more information.

import timeit

#creation of arrays and tuples (timeit number=1000000 by default)

timeit.Timer('a((1.,2.,3.))','from numpy import array as a').timeit()
#8.2061841487884521
timeit.Timer('a((1.,2.,3.))','from Numeric import array as a').timeit()
#9.6958281993865967
timeit.Timer('a((1.,2.,3.))','a=tuple').timeit()
#0.13814711570739746

#Result: tuples - of course - are much faster than arrays and numpy is a bit faster in creating arrays than Numeric

#working with arrays

timeit.Timer('d=x1-x2;sum(d*d)','from Numeric import array as a; x1=a((1.,2.,3.));x2=a((2.,4.,6.))').timeit()
#3.263314962387085
timeit.Timer('d=x1-x2;sum(d*d)','from numpy import array as a; x1=a((1.,2.,3.));x2=a((2.,4.,6.))').timeit()
#9.7236979007720947

#Result: Numeric is three times faster than numpy! Why?

#working with components:

timeit.Timer('d0=x1[0]-x2[0];d1=x1[1]-x2[1];d2=x1[2]-x2[2];d0*d0+d1*d1+d2*d2','a=tuple; x1=a((1.,2.,3.));x2=a((2.,4.,6.))').timeit()
#0.64785194396972656
timeit.Timer('d0=x1[0]-x2[0];d1=x1[1]-x2[1];d2=x1[2]-x2[2];d0*d0+d1*d1+d2*d2','from numpy import array as a; x1=a((1.,2.,3.));x2=a((2.,4.,6.))').timeit()
#3.4181499481201172
timeit.Timer('d0=x1[0]-x2[0];d1=x1[1]-x2[1];d2=x1[2]-x2[2];d0*d0+d1*d1+d2*d2','from Numeric import array as a; x1=a((1.,2.,3.));x2=a((2.,4.,6.))').timeit()
#0.97426199913024902

Result: tuples are again the fastest variant, Numeric is faster than numpy and both are faster than the variant above using the high-level functions!
Why?

For various reasons I need to use numpy in the future where I used Numeric before.
Is there any better solution in numpy I missed?

Kind regards and thanks in advance

Thomas



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