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

[Nick Papior Andersen]

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?
>
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>


-- 
Kind regards Nick
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