nikcleju@7: """
nikcleju@7: Benchmark script to be used to evaluate the performance improvement of the MKL
nikcleju@7: """
nikcleju@7: 
nikcleju@7: import os
nikcleju@7: import sys
nikcleju@7: import timeit
nikcleju@7: 
nikcleju@7: import numpy
nikcleju@7: from numpy.random import random
nikcleju@7: 
nikcleju@7: def test_eigenvalue():
nikcleju@7:     """
nikcleju@7:     Test eigen value computation of a matrix
nikcleju@7:     """
nikcleju@7:     i = 500
nikcleju@7:     data = random((i,i))
nikcleju@7:     result = numpy.linalg.eig(data)
nikcleju@7: 
nikcleju@7: def test_svd():
nikcleju@7:     """
nikcleju@7:     Test single value decomposition of a matrix
nikcleju@7:     """
nikcleju@7:     i = 1000
nikcleju@7:     data = random((i,i))
nikcleju@7:     result = numpy.linalg.svd(data)
nikcleju@7:     result = numpy.linalg.svd(data, full_matrices=False)
nikcleju@7: 
nikcleju@7: def test_inv():
nikcleju@7:     """
nikcleju@7:     Test matrix inversion
nikcleju@7:     """
nikcleju@7:     i = 1000
nikcleju@7:     data = random((i,i))
nikcleju@7:     result = numpy.linalg.inv(data)
nikcleju@7: 
nikcleju@7: def test_det():
nikcleju@7:     """
nikcleju@7:     Test the computation of the matrix determinant
nikcleju@7:     """
nikcleju@7:     i = 1000
nikcleju@7:     data = random((i,i))
nikcleju@7:     result = numpy.linalg.det(data)
nikcleju@7: 
nikcleju@7: def test_dot():
nikcleju@7:     """
nikcleju@7:     Test the dot product
nikcleju@7:     """
nikcleju@7:     i = 1000
nikcleju@7:     a = random((i, i))
nikcleju@7:     b = numpy.linalg.inv(a)
nikcleju@7:     result = numpy.dot(a, b) - numpy.eye(i)
nikcleju@7: 
nikcleju@7: # Test to start. The dict is the value I had with the MKL using EPD 6.0 and without MKL using EPD 5.1
nikcleju@7: tests = {test_eigenvalue : (752., 3376.),
nikcleju@7:          test_svd : (4608., 15990.),
nikcleju@7:          test_inv : (418., 1457.),
nikcleju@7:          test_det : (186.0, 400.),
nikcleju@7:          test_dot : (666., 2444.) }
nikcleju@7: 
nikcleju@7: # Setting the following environment variable in the shell executing the script allows
nikcleju@7: # you limit the maximal number threads used for computation
nikcleju@7: THREADS_LIMIT_ENV = 'OMP_NUM_THREADS'
nikcleju@7: 
nikcleju@7: def start_benchmark():
nikcleju@7:     print """Benchmark is made against EPD 6.0,NumPy 1.4 with MKL and EPD 5.1, NumPy 1.3 with ATLAS
nikcleju@7: on a Thinkpad T60 with Intel CoreDuo 2Ghz CPU on Windows Vista 32 bit
nikcleju@7:     """
nikcleju@7:     if os.environ.has_key(THREADS_LIMIT_ENV):
nikcleju@7:         print "Maximum number of threads used for computation is : %s" % os.environ[THREADS_LIMIT_ENV]
nikcleju@7:     print ("-" * 80)
nikcleju@7:     print "Starting timing with numpy %s\nVersion: %s" % (numpy.__version__, sys.version)
nikcleju@7:     print "%20s : %10s - %5s / %5s" % ("Function", "Timing [ms]", "MKL", "No MKL")
nikcleju@7:     for fun, bench in tests.items():
nikcleju@7:         t = timeit.Timer(stmt="%s()" % fun.__name__, setup="from __main__ import %s" % fun.__name__)
nikcleju@7:         res = t.repeat(repeat=3, number=1)
nikcleju@7:         timing =  1000.0 * sum(res)/len(res)
nikcleju@7:         print "%20s : %7.1f ms -  %3.2f / %3.2f" % (fun.__name__, timing, bench[0]/timing, bench[1]/timing)
nikcleju@7: 
nikcleju@7: if __name__ == '__main__':
nikcleju@7:     start_benchmark()