[SciPy-user] SciPy Ubuntu Dapper installation using Andrew Straw's packages

Ryan Krauss ryanlists at gmail.com
Thu Oct 12 16:22:49 CDT 2006

ryan at ubuntu:~$ ldd
/usr/lib/python2.4/site-packages/numpy/core/_dotblas.so  | grep blas
        libblas.so.3 => /usr/lib/atlas/sse2/libblas.so.3 (0xb7387000)

I think I am happy and I am done thinking about this.



On 10/11/06, David Cournapeau <david at ar.media.kyoto-u.ac.jp> wrote:
> Ryan Krauss wrote:
> > So, Andrew's packages worked beautifully.  The information I was
> > looking for is actually on
> > http://scipy.org/Installing_SciPy/Linux .  I kept searching the list
> > for the instructions but it is on the website (in case anyone else is
> > ever looking here and finds this message).
> >
> > Are the packages using optimized Atlas and Lapack if I have them
> > installed specific to my processor (sse2)?
> An 'easy' way to check if you are using sse2 optimized atlas is to check
> which libraries the loader loads when loading numpy and scipy libraries.
> For example, for numpy:
> ldd /usr/lib/python2.4/site-packages/numpy/core/_dotblas.so  | grep blas
> returns the atlas enabled libblas (in the /usr/lib/sse2 directory, but
> this is distribution dependent, I think)
> >   Would I see any
> > performance increases if I built from source?
> >
> Only if your atlas is faster than the shipped one (stating the obvious:)
> ). Concretely, this means that you are able to compile a faster atlas,
> with the correct extended lapack, and this is not trivial, specially if
> your cpu is not in the default. Recent (after 3.7.14, I think) atlas
> have a much easier to use configuration.
> If you have a new intel core 2 duo, last atlas give outstanding results
> according to the main developer of Atlas:
> http://sourceforge.net/mailarchive/forum.php?thread_id=30384800&forum_id=426
> David
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