sigu4wa02 at sneakemail
Mar 4, 2005, 8:14 AM
Post #1 of 2
(536 views)
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[Numeric] column vector faster than row vector in mat multiply?
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Hi,
I did a small benchmark of matrix-vector multiply operation using
Numeric module. I'm a bit suprised to find matrix*col-vector is much
faster than row-vector*matrix. I wonder whether other people have
observed this fact too, and why?
Below is the code I used, with output from my machine.
python bench.py
running 1000 iterations of matrix multiply of row 1000-vector
10.5609340668 sec
running 1000 iterations of matrix operation of column 1000-vector
4.11953210831 sec
-----code begin-----
import random
import time
from Numeric import *
n = 1000
k = 1000
r = array([ random.gauss(0, 1) for i in range(n)])
c = array([ [random.gauss(0, 1)] for i in range(n)])
M = zeros((n, n), Float)
for i in range(n):
for j in range(n):
M[i][j] = random.gauss(0, 1)
print 'running %d iterations of matrix multiply of row %d-vector' % (k,
n)
t = time.time()
for i in xrange(k):
matrixmultiply(r, M)
print time.time()-t, 'sec'
print 'running %d iterations of matrix operation of column %d-vector' %
(k, n)
t = time.time()
for i in xrange(k):
matrixmultiply(M, c)
print time.time()-t, 'sec'
------code end----------
Zhang Le
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