on cluster A 1.9.0 and on cluster B 1.8.2 2015-04-29 17:18 GMT+02:00 Nick Papior Andersen <nickpap...@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 <afy...@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@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> > > > -- > Kind regards Nick > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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