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
