[Numpy-discussion] performance of numpy.array()

[Nick Papior Andersen]

You could try and install your own numpy to check whether that resolves the
problem.

2015-04-29 17:40 GMT+02:00 simona bellavista <afylot at gmail.com>:

> 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
>> libraries.
>>
>> 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
>>> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>>>
>>>
>>
>>
>> --
>> Kind regards Nick
>>
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>>
>
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-- 
Kind regards Nick
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