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 -- https://mail.python.org/mailman/listinfo/python-list
