view tests/testnumpy.py @ 39:0ff08ae833be

utils.py:
 Mark the areas with less than 10% error in the figures.
 Joined loadsavematrices() and loadshowmatrices() together in the same function loadmatrices()

"""
Benchmark script to be used to evaluate the performance improvement of the MKL
"""

import os
import sys
import timeit

import numpy
from numpy.random import random

def test_eigenvalue():
    """
    Test eigen value computation of a matrix
    """
    i = 500
    data = random((i,i))
    result = numpy.linalg.eig(data)

def test_svd():
    """
    Test single value decomposition of a matrix
    """
    i = 1000
    data = random((i,i))
    result = numpy.linalg.svd(data)
    result = numpy.linalg.svd(data, full_matrices=False)

def test_inv():
    """
    Test matrix inversion
    """
    i = 1000
    data = random((i,i))
    result = numpy.linalg.inv(data)

def test_det():
    """
    Test the computation of the matrix determinant
    """
    i = 1000
    data = random((i,i))
    result = numpy.linalg.det(data)

def test_dot():
    """
    Test the dot product
    """
    i = 1000
    a = random((i, i))
    b = numpy.linalg.inv(a)
    result = numpy.dot(a, b) - numpy.eye(i)

# Test to start. The dict is the value I had with the MKL using EPD 6.0 and without MKL using EPD 5.1
tests = {test_eigenvalue : (752., 3376.),
         test_svd : (4608., 15990.),
         test_inv : (418., 1457.),
         test_det : (186.0, 400.),
         test_dot : (666., 2444.) }

# Setting the following environment variable in the shell executing the script allows
# you limit the maximal number threads used for computation
THREADS_LIMIT_ENV = 'OMP_NUM_THREADS'

def start_benchmark():
    print """Benchmark is made against EPD 6.0,NumPy 1.4 with MKL and EPD 5.1, NumPy 1.3 with ATLAS
on a Thinkpad T60 with Intel CoreDuo 2Ghz CPU on Windows Vista 32 bit
    """
    if os.environ.has_key(THREADS_LIMIT_ENV):
        print "Maximum number of threads used for computation is : %s" % os.environ[THREADS_LIMIT_ENV]
    print ("-" * 80)
    print "Starting timing with numpy %s\nVersion: %s" % (numpy.__version__, sys.version)
    print "%20s : %10s - %5s / %5s" % ("Function", "Timing [ms]", "MKL", "No MKL")
    for fun, bench in tests.items():
        t = timeit.Timer(stmt="%s()" % fun.__name__, setup="from __main__ import %s" % fun.__name__)
        res = t.repeat(repeat=3, number=1)
        timing =  1000.0 * sum(res)/len(res)
        print "%20s : %7.1f ms -  %3.2f / %3.2f" % (fun.__name__, timing, bench[0]/timing, bench[1]/timing)

if __name__ == '__main__':
    start_benchmark()