[Numpy-discussion] I can't tell if Numpy is configured properly with show_config()

[Elliot Hallmark]

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:

###
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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