Benchmarking scipy and numpy, to be measured with MKL, OpenBLAS and Netlib.

scipy_benchmark.py

from __future__ import print_function
import sys
from numpy import array
from numpy.random import rand
from timeit import Timer
import pickle
from os import system

if len(sys.argv) is not 2:
  print ("usage: numpy_benchmark.py whichpython")
  sys.exit(0)
else:
  python_version = sys.argv[1]

s = 2000
repetition = 20
A, B = rand(s, s), rand(s, s)
t = Timer("A.dot(B)", "import numpy; from __main__ import A, B")
dp = array(t.repeat(repetition, 1))
print ("dot product for ", s, "x", s, "matrices :", "%.2f+/-%.4f seconds" %(dp.mean(), dp.std()))

setup = "import numpy;\
        import scipy.linalg as linalg;\
        x = numpy.random.random((%d,%d));\
        z = numpy.dot(x, x.T)" % (s, s)

t = Timer("linalg.cholesky(z, lower=True)", setup=setup)
ch = array(t.repeat(repetition, 1))
print ("cholesky decomposition for ", s, "x", s, "matrices :", "%.2f+/-%.4f seconds" %(ch.mean(), ch.std()))
t = Timer("linalg.svd(z)", setup=setup)
sv = array(t.repeat(repetition, 1))
print ("SVD decomposition for ", s, "x", s, "matrices :", "%.2f+/-%.4f seconds" %(sv.mean(), sv.std()))

fn = "scipy_"+python_version+".pck"
with open(fn, 'w') as f:
  o = {'dot':dp, 'chol':ch, 'svd':sv}
  pickle.dump(o, f)