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Wed Apr 25 18:45:36 EDT 2001 · http://mail.python.org/pipermail/python-list/2001-April/111136.html

At some point, Benyang Tang <btang at pacific.jpl.nasa.gov> wrote:

> Thanks. I added the lapack library to setup.py. It worked. The setup.py now looks like this:
> 
> # delete all but the first one in this list if using your own LAPACK/BLAS
> sourcelist = ['Src/lapack_litemodule.c',]
> # set these to use your own BLAS
> library_dirs_list = ['/usr/lib','/usr/local/lib','/usr/lib/gcc-lib/i386-redhat-linux/egcs-2.91.66']
> libraries_list = ['blas','lapack','g2c','m']
> 
> However, numpy does not get any speed gain by linking to the native blas/lapack. Here I tested the multiplication of 2 real*4 matrixes:
> Multiplication of 100X100 matrixes takes  0.01
> Multiplication of 200X200 matrixes takes  0.11
> Multiplication of 300X300 matrixes takes  0.41
> Multiplication of 400X400 matrixes takes  1.47
> Multiplication of 500X500 matrixes takes  3.10
> Multiplication of 600X600 matrixes takes  5.51
> 
> The timing is roughly the same as that when linking to the compiled blas_lite.
> 

Gee, really? Guess what: NumPy doesn't use BLAS for multiplying
matrices :-(. It uses it's own implementation, which (on my machine)
is about six times slower for 600x600 matrices. You'll have to write
your own wrapper around BLAS for matrix multiplication.

-- 
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|David M. Cooke
|cookedm(at)physics(dot)mcmaster(dot)ca

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building numpy20 on native blas/lapack