Improving performance in matrix operations

[Steven D'Aprano]

On Thursday 10 March 2016 07:09, Drimades wrote:

> 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? 

I don't know what counts as acceptable. Do you have a thousand of these 
systems to solve by next Tuesday? Or one a month? Can you adjust your 
workflow to start the calculation and then go off to lunch, or do you 
require interactive use?


> Any suggestions to improve? 

Use smaller matrices? :-) Use a faster computer?


This may give you some ideas:

https://www.ibm.com/developerworks/community/blogs/jfp/entry/A_Comparison_Of_C_Julia_Python_Numba_Cython_Scipy_and_BLAS_on_LU_Factorization?lang=en



> Does C++ perform better with matrices? 

Specifically on your computer? I don't know, you'll have to try it. The 
actual time taken by a program will depend on the hardware you run it on, 
not just the language it is written in.


> 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.

You might get better answers for technical questions like that from 
dedicated numpy and scipy mailing lists.



-- 
Steve