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