Improving performance in matrix operations

Newsgroups	comp.lang.python
Date	2016-03-09 12:09 -0800
Message-ID	<1b1ef48f-c60f-4c56-ae55-376e8a117337@googlegroups.com> (permalink)
Subject	Improving performance in matrix operations
From	Drimades <e.zhupa@gmail.com>

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I'm doing some tests with operations on numpy matrices in Python. As an example, it takes about 3000 seconds to compute eigenvalues and eigenvectors using scipy.linalg.eig(a) for a matrix 6000x6000. Is it an acceptable time? Any suggestions to improve? Does C++ perform better with matrices? Another thing to consider is that matrices I'm processing are heavily sparse.
Do they implement any parallelism? While my code is running, one of my cores is 100% busy, the other one 30% busy.

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Improving performance in matrix operations Drimades <e.zhupa@gmail.com> - 2016-03-09 12:09 -0800
  Re: Improving performance in matrix operations Fabien <fabien.maussion@gmail.com> - 2016-03-09 21:16 +0100
  Re: Improving performance in matrix operations Steven D'Aprano <steve+comp.lang.python@pearwood.info> - 2016-03-10 19:25 +1100
  Re: Improving performance in matrix operations Oscar Benjamin <oscar.j.benjamin@gmail.com> - 2016-03-14 18:35 +0000

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