[yt-users] h5py execution time

[Jean-Claude Passy]

  Thanks guys, that is very   enlightening.

Best,

JC

On 13/08/10 17:02, Stephen Skory wrote:
> JC,
>
>> In my experience, interpreted languages are slow with loops.  If you use
>> the array syntax in numpy, all of the loops are done in C.  The same
>> thing happens in IDL, as well, where you dearly try to avoid loops.
>> This  page helped me long ago in  IDL...
>
> John is right about interpreted languages. To expand on the point about Numpy
> and C, if you have a Numpy array (which is what h5py outputs when you read data
> out of a file), and you operate on it using Numpy functions, the work is done in
> compiled C code. This means it's much faster. Here is some example code that
> does the same thing twice, once with a Python loop and the other in Numpy:
>
> ----------
>
> import time, math
> import numpy as np
>
> a = np.random.random(1e7).astype('float64')
> b = np.empty(1e7, dtype='float64')
> c = np.empty(1e7, dtype='float64')
>
> t0 = time.time()
> for i, item in enumerate(a):
>      b[i] = math.pow(item, 2.)
> t1 = time.time()
> print 'Python loop takes %f seconds' % (t1-t0)
>
> t0 = time.time()
> np.power(a, 2., c)
> t1 = time.time()
> print 'Numpy C loop takes %f seconds' % (t1-t0)
>
> print 'The arrays are the same?', (b == c).all()
>
> -----------
>
> Noting that I had to enforce 64-bit so the answers will match up, I get this
> when I run it:
>
> python timeme.py
> Python loop takes 9.354742 seconds
> Numpy C loop takes 0.584433 seconds
> The arrays are the same? True
>
> I think the advantages are obvious! You had a triple loop in your original code,
> so the effect is likely even worse than this.
>
> I hope this helps!
>
>   _______________________________________________________
> sskory at physics.ucsd.edu o__ Stephen Skory
> http://physics.ucsd.edu/~sskory/ _.>/ _Graduate Student
> ________________________________(_)_\(_)_______________
>
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