[Numpy-discussion] numpy vs numeric benchmarks (original) (raw)

Eric Jonas jonas at mwl.mit.edu
Fri Jun 2 08:58:50 EDT 2006

Hello! I've been using numeric for a while, and the recent list traffic prompted me to finally migrate all my old code. On a whim, we were benchmarking numpy vs numeric and have been lead to the conclusion that numpy is at least 50x slower; a 1000x1000 matmul takes 16 sec in numpy but 300 ms in numeric.

Now, of course, I don't believe this, but I can't figure out what we're doing wrong; I'm not the only person who has looked at this code, so can anyone tell me what we're doing wrong?

We run both benchmarks twice to try and mitigate any start-up and cache effects. This is with debian-amd64's packaged numeric 24.2-2 and a locally built numpy-0.9.8.

/usr/bin/python import time import numpy import random import Numeric

def numpytest(): N = 1000 x = numpy.zeros((N,N),'f') y = numpy.zeros((N,N),'f')

for i in range(N): for j in range(N): x[i, j] = random.random() y[i, j] = random.random()

t1 = time.clock() z = numpy.matrixmultiply(x, y) t2 = time.clock()

print (((t2 - t1)*1000))

def numerictest(): N = 1000

x = Numeric.zeros((N,N),'f') y = Numeric.zeros((N,N),'f')

for i in range(N): for j in range(N): x[i, j] = random.random() y[i, j] = random.random()

t1 = time.clock() z = Numeric.matrixmultiply(x, y) t2 = time.clock()

print (((t2 - t1)*1000))

numerictest() numpytest() numpytest() numerictest()

on our hardware a call to numerictest() takes 340 ms and a numpytest takes around 13 sec (!).

Any advice on what we're doing wrong would be very helpful. ...Eric

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