[Numpy-discussion] performance of numpy.array()
afylot at gmail.com
Wed Apr 29 11:40:21 EDT 2015
on cluster A 1.9.0 and on cluster B 1.8.2
2015-04-29 17:18 GMT+02:00 Nick Papior Andersen <nickpapior at gmail.com>:
> Compile it yourself to know the limitations/benefits of the dependency
> Otherwise, have you checked which versions of numpy they are, i.e. are
> they the same version?
> 2015-04-29 17:05 GMT+02:00 simona bellavista <afylot at gmail.com>:
>> I work on two distinct scientific clusters. I have run the same python
>> code on the two clusters and I have noticed that one is faster by an order
>> of magnitude than the other (1min vs 10min, this is important because I run
>> this function many times).
>> I have investigated with a profiler and I have found that the cause of
>> this is that (same code and same data) is the function numpy.array that is
>> being called 10^5 times. On cluster A it takes 2 s in total, whereas on
>> cluster B it takes ~6 min. For what regards the other functions, they are
>> generally faster on cluster A. I understand that the clusters are quite
>> different, both as hardware and installed libraries. It strikes me that on
>> this particular function the performance is so different. I would have
>> though that this is due to a difference in the available memory, but
>> actually by looking with `top` the memory seems to be used only at 0.1% on
>> cluster B. In theory numpy is compiled with atlas on cluster B, and on
>> cluster A it is not clear, because numpy.__config__.show() returns NOT
>> AVAILABLE for anything.
>> Does anybody has any insight on that, and if I can improve the
>> performance on cluster B?
>> NumPy-Discussion mailing list
>> NumPy-Discussion at scipy.org
> Kind regards Nick
> NumPy-Discussion mailing list
> NumPy-Discussion at scipy.org
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