arrays

Working with big data in python and numpy, not enough ram, how to save partial results on disc?

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I am trying to implement algorithms for 1000-dimensional data with 200k+ datapoints in python. I want to use numpy, scipy, sklearn, networkx, and other useful libraries. I want to perform operations such as pairwise distance between all of the points and do clustering on all of the points. I have implemented working algorithms that perform what I want with reasonable complexity but when I try to scale them to all of my data I run out of RAM. Of course, I do, creating the matrix for pairwise distances on 200k+ data takes a lot of memory.

Why is numpy’s einsum faster than numpy’s built in functions?

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Lets start with three arrays of dtype=np.double. Timings are performed on a intel CPU using numpy 1.7.1 compiled with icc and linked to intel’s mkl. A AMD cpu with numpy 1.6.1 compiled with gcc without mkl was also used to verify the timings. Please note the timings scale nearly linearly with system size and are not due to the small overhead incurred in the numpy functions if statements these difference will show up in microseconds not milliseconds: