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

[Chris Colbert]

Ok, im getting the same error on an install of straight ubuntu 8.10

the guy in this thread got the same error as me, but its not clear how he
worked it out:
http://www.mail-archive.com/numpy-discussion@scipy.org/msg13565.html

from googling here:
http://sources.redhat.com/ml/binutils/2004-12/msg00033.html

it says that the library was not built correctly.

does this mean my atlas .so's (which i built via -> make ptshared) are
incorrect?

I suppose I could just grab atlas from the repositories, but that would be
admitting defeat.

Chris

On Fri, Mar 27, 2009 at 1:09 PM, Chris Colbert <sccolbert at gmail.com> wrote:

> 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
> system)
>
> 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.
>
> Chris
>
>
> On Fri, Mar 27, 2009 at 12:57 PM, Chris Colbert <sccolbert at 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 at 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
>>> _______________________________________________
>>> Numpy-discussion mailing list
>>> Numpy-discussion at scipy.org
>>> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>>>
>>
>>
>
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