Thanks for the results, I’ve added them to the graph.

As a further exercise I’m thinking of comparing pystones or another benchmark using different speedup techniques, i.e. psyco, cython etc,
shedskin looks very interesting: http://code.google.com/p/shedskin/

Xeon DP 2.66ghzx2 (E5430) = 8 cores total

multiprocessing test.pystones() benchmark

You have 8 CPU(s)

Processes Pystones Wall time pystones/s Speedup
1 500000 6.9 72727 1.0X
2 1000000 6.8 147449 2.0X
3 1500000 6.8 221729 3.0X
4 2000000 6.8 292912 4.0X
5 2500000 6.8 365283 5.0X
6 3000000 6.8 438340 6.0X
7 3500000 6.9 506806 7.0X
8 4000000 6.9 577868 7.9X
9 4500000 10.2 441739 6.1X
10 5000000 11.3 441384 6.1X

Simple multiprocessing pystone.pystones() benchmark ”’
”’ Anders Wallin 2008Jun15 anders.e.e.wallin (at) gmail.com ”’
from test import pystone
import processing
import time

STONES_PER_PROCESS= 10*pystone.LOOPS

def f(q):
t=pystone.pystones(STONES_PER_PROCESS)
q.put(t,block=True)

if __name__ == ‘__main__’:
processing.freezeSupport()

print ‘multiprocessing test.pystones() benchmark\n’
print ‘You have ‘+str(processing.cpuCount()) + ‘ CPU(s)\n’
print ‘Processes\tPystones\tWall time\tpystones/s\tSpeedup’

results=processing.Queue()
for N in range(1,processing.cpuCount()+3):
p=[]
q=processing.Queue()
results=[]

for m in range(1,N+1):
p.append( processing.Process(target=f,args=(q,)) )

start=time.time()
for pr in p:
pr.start()
for r in p:
results.append( q.get() )
stop=time.time()

cputime = stop-start
if N==1: singlecpurate = N*STONES_PER_PROCESS / cputime

print “%u\t\t%u\t\t%.1f\t\t%.f\t\t%.1fX” % (N, N*STONES_PER_PROCESS,
cputime, N*STONES_PER_PROCESS / cputime,
N*STONES_PER_PROCESS / (cputime * singlecpurate) )