[Numpy-discussion] Built Lapack, Atlas from source.... now numpy.linalg.eig() hangs at 100% CPU

Chris Colbert sccolbert@gmail....
Fri Mar 27 12:09:59 CDT 2009

some other things I might mention, though I doubt they would have an effect:

When i built Atlas, I had to force it to use a 32-bit pointer length (I
assume this is correct for a 32-bit OS as gcc.stub_64 wasnt found on my

in numpy's site.cfg I only linked to the pthread .so's. Should I have also
linked to the single threaded counterparts in the section above? (I assumed
one would be overridden by the other)

Other than those, I followed closely the instructions on scipy.org.


On Fri, Mar 27, 2009 at 12:57 PM, Chris Colbert <sccolbert@gmail.com> wrote:

> this is true. but not nearly as good of a learning experience :)
> I'm a mechanical engineer, so all of this computer science stuff is really
> new and interesting to me. So i'm trying my best to get a handle on exactly
> what is going on behind the scenes.
> Chris
> On Fri, Mar 27, 2009 at 12:36 PM, David Cournapeau <
> david@ar.media.kyoto-u.ac.jp> wrote:
>> Chris Colbert wrote:
>> > forgive my ignorance, but wouldn't installing atlas from the
>> > repositories defeat the purpose of installing atlas at all, since the
>> > build process optimizes it to your own cpu timings?
>> Yes and no. Yes, it will be slower than a cutom-build atlas, but it will
>> be reasonably faster than blas/lapack. Please also keep in mind that
>> this mostly matters for linear algebra and big matrices.
>> Thinking from another POV: how many 1000x1000 matrices could have you
>> inverted while wasting your time on this already :)
>> cheers,
>> David
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