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
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