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@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@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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