Thanks, I didn't realize dot was not just calling dgemm or some
variant which I assume would be reasonably fast. I see dgemm appears
in the numpy code in various places such as the lapack_lite module.
I ran the svd test on the solaris setup and will check the OSX run
when back at my laptop. 8.4 seconds is slightly slower than matlab but
still seems reasonable.
$ ./test_03.py
No ATLAS:
(1000, 1000) (1000,) (1000, 1000)
8.17235898972
$ cat test_03.py
#!/usr/bin/env python3
import numpy
import time
try:
import numpy.core._dotblas
print('Using ATLAS:')
except ImportError:
print('No ATLAS:')
import numpy.linalg
N = 1000
x = numpy.random.random((N,N))
t = time.time()
(U, s, V) = numpy.linalg.svd(x)
print(U.shape, s.shape, V.shape)
# S = numpy.matrix(numpy.diag(s))
# y = U * S * V
#print(y.shape)
print(time.time()-t)
On Tue, Sep 6, 2011 at 2:59 PM, David Cournapeau <courn...@gmail.com> wrote:
> On Tue, Sep 6, 2011 at 2:38 PM, David Cottrell <david.cottr...@gmail.com>
> wrote:
>> I posted on stackoverflow but then noticed this message board:
>>
>> http://stackoverflow.com/questions/7311869/python-numpy-on-solaris-blas-slow-or-not-linked
>>
>> I'm reposting the full post below:
>>
>> Matrix-Matrix multiplies are very slow on my Solaris install (running
>> on a sparc server) compared to my OSX install (on a laptop!). The
>> laptop runs 100 times faster (for matrix-matrix multiplies of
>> 3000x3000 dense random matrices of doubles).
>>
>> It must be because the Solaris install is not using blas, but the
>> numpy scripts are reporting that the libs are 'found'.
>>
>> $python3 -c "import numpy.distutils.system_info as f; d =
>> f.get_info('blas',0); print(d); d = f.get_info('lapack',0); print(d)"
>> {'libraries': ['sunperf'], 'library_dirs':
>> ['/home/$myname/local/archive/SolarisStudio12.2-solaris-sparc-tar-ML/solstudio12.2/lib'],
>> 'language': 'f77'}
>> {'libraries': ['sunmath'], 'library_dirs':
>> ['/home/$myname/local/archive/SolarisStudio12.2-solaris-sparc-tar-ML/solstudio12.2/lib'],'language':
>> 'f77'}
>>
>> The following import FAILS on the Solaris install but succeeds on OSX:
>>
>> import numpy.core._dotblas
>>
>> There is no ATLAS available for the Solaris install. I wouldn't think
>> this would make such a huge different in computational efficiency.
>
> Actually, it will make a different for this exact case, because
> dotblas (which implements numpy.dot) depends on ATLAS, not on BLAS
> itself (this is a bug in numpy that I meant to fix for ages). Mac OS X
> includes ATLAS in its accelerate framework. If BLAS/LAPACK from
> Solaris was correctly linked, you should be able to see fast
> operations for pretty much everything else. For example, how fast is
> SVD or eig for a 1000x1000 matrix ?
>
> cheers,
>
> David
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
--
David Cottrell
+1 416 995 9860
http://ca.linkedin.com/in/dcottrell
_______________________________________________
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
