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 <sebastian@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

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