Well, here is the question that started this all. In the slow environment,
blas seems to be there and work well, but numpy doesn't use it!
In [1]: import time, numpy, scipy
In [2]: from scipy import linalg
In [3]: n=1000
In [4]: A = numpy.random.rand(n,n)
In [5]: B = numpy.random.rand(n,n)
In [6]: then = time.time(); C=scipy.dot(A,B); print time.time()-then
7.62005901337
In [7]: begin = time.time(); C=linalg.blas.dgemm(1.0,A,B);print time.time()
- begin
0.325305938721
In [8]: begin = time.time(); C=linalg.blas.ddot(A,B);print time.time() -
begin
0.0363020896912
On Sat, Jun 20, 2015 at 4:09 AM, Sebastian Berg <sebast...@sipsolutions.net>
wrote:
> On Fr, 2015-06-19 at 16:19 -0500, Elliot Hallmark wrote:
> > Debian Sid, 64-bit. I was trying to fix the problem of np.dot running
> > very slow.
> >
> >
> > I ended up uninstalling numpy, installing libatlas3-base through
> > apt-get and re-installing numpy. The performance of dot is greatly
> > improved! But I can't tell from any other method whether numpy is set
> > up correctly. Consider comparing the faster one to another in a
> > virtual env that is still slow:
> >
>
> Not that I really know this stuff, but one thing to be sure is probably
> checking `ldd /usr/lib/python2.7/dist-packages/numpy/core/_dotblas.so`.
> That is probably silly (I really never cared to learn this stuff), but I
> think it can't go wrong....
>
> About the other difference. Aside from CPU, etc. differences, I expect
> you got a newer numpy version then the other user. Not sure which part
> got much faster, but there were for example quite a few speedups in the
> code converting to array, so I expect it is very likely that this is the
> reason.
>
> - Sebastian
>
>
> > ###
> >
> > fast one
> > ###
> >
> > In [1]: import time, numpy
> >
> > In [2]: n=1000
> >
> > In [3]: A = numpy.random.rand(n,n)
> >
> > In [4]: B = numpy.random.rand(n,n)
> >
> > In [5]: then = time.time(); C=numpy.dot(A,B); print time.time()-then
> > 0.306427001953
> >
> > In [6]: numpy.show_config()
> > blas_info:
> > libraries = ['blas']
> > library_dirs = ['/usr/lib']
> > language = f77
> > lapack_info:
> > libraries = ['lapack']
> > library_dirs = ['/usr/lib']
> > language = f77
> > atlas_threads_info:
> > NOT AVAILABLE
> > blas_opt_info:
> > libraries = ['blas']
> > library_dirs = ['/usr/lib']
> > language = f77
> > define_macros = [('NO_ATLAS_INFO', 1)]
> > atlas_blas_threads_info:
> > NOT AVAILABLE
> > openblas_info:
> > NOT AVAILABLE
> > lapack_opt_info:
> > libraries = ['lapack', 'blas']
> > library_dirs = ['/usr/lib']
> > language = f77
> > define_macros = [('NO_ATLAS_INFO', 1)]
> > atlas_info:
> > NOT AVAILABLE
> > lapack_mkl_info:
> > NOT AVAILABLE
> > blas_mkl_info:
> > NOT AVAILABLE
> > atlas_blas_info:
> > NOT AVAILABLE
> > mkl_info:
> > NOT AVAILABLE
> >
> > ###
> >
> > slow one
> > ###
> >
> > In [1]: import time, numpy
> >
> > In [2]: n=1000
> >
> > In [3]: A = numpy.random.rand(n,n)
> >
> > In [4]: B = numpy.random.rand(n,n)
> >
> > In [5]: then = time.time(); C=numpy.dot(A,B); print time.time()-then
> > 7.88430500031
> >
> > In [6]: numpy.show_config()
> > blas_info:
> > libraries = ['blas']
> > library_dirs = ['/usr/lib']
> > language = f77
> > lapack_info:
> > libraries = ['lapack']
> > library_dirs = ['/usr/lib']
> > language = f77
> > atlas_threads_info:
> > NOT AVAILABLE
> > blas_opt_info:
> > libraries = ['blas']
> > library_dirs = ['/usr/lib']
> > language = f77
> > define_macros = [('NO_ATLAS_INFO', 1)]
> > atlas_blas_threads_info:
> > NOT AVAILABLE
> > openblas_info:
> > NOT AVAILABLE
> > lapack_opt_info:
> > libraries = ['lapack', 'blas']
> > library_dirs = ['/usr/lib']
> > language = f77
> > define_macros = [('NO_ATLAS_INFO', 1)]
> > atlas_info:
> > NOT AVAILABLE
> > lapack_mkl_info:
> > NOT AVAILABLE
> > blas_mkl_info:
> > NOT AVAILABLE
> > atlas_blas_info:
> > NOT AVAILABLE
> > mkl_info:
> > NOT AVAILABLE
> >
> > #####
> >
> >
> > Further, in the following comparison between Cpython and converting to
> > numpy array for one operation, I get Cpython being faster by the same
> > amount in both environments. But another user got numpy being faster.
> >
> > In [1]: import numpy as np
> >
> > In [2]: pts = range(100,1000)
> >
> > In [3]: pts[100] = 0
> >
> > In [4]: %timeit pts_arr = np.array(pts); mini = np.argmin(pts_arr)
> > 10000 loops, best of 3: 129 µs per loop
> >
> > In [5]: %timeit mini = sorted(enumerate(pts))[0][1]
> > 10000 loops, best of 3: 89.2 µs per loop
> >
> > The other user got
> >
> > In [29]: %timeit pts_arr = np.array(pts); mini = np.argmin(pts_arr)
> > 10000 loops, best of 3: 37.7 µs per loop
> >
> > In [30]: %timeit mini = sorted(enumerate(pts))[0][1]
> > 10000 loops, best of 3: 69.2 µs per loop
> >
> >
> > And I can't help but wonder if there is further configuration I need to
> make numpy faster, or if this is just a difference between out machines
> > In the future, should I ignore show_config() and just do this dot
> > product test?
> >
> >
> > Any guidance would be appreciated.
> >
> >
> > Thanks,
> >
> > Elliot
> > _______________________________________________
> > NumPy-Discussion mailing list
> > NumPy-Discussion@scipy.org
> > http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
>
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