Hi Peter,
> Riko, any chance you could post the final code and a bit more detail on
> exactly how much Psyco contributed to the speedup? The former would be
> educational for all of us, while I'm personally very curious about the
> latter because my limited attempts to use Psyco in the past have
> resulted in speedups on the order of only 20% or so. (I blame my
> particular application, not Psyco per se, but I'd be happy to see a
> real-world case where Psyco gave a much bigger boost.)
the difference between running with and without psyco is about a factor
3 for my MC simulation. Without psyco the simulation runs for 62 sec,
with it for 19 secs (still using time instead of timeit, though!:) This
is for about 2300 and 10000 in for the inner and outer loop, respectively.
A factor 3 I consider worthwhile, especially since it doesn't really
cost you much.
This is on a Dell Lat D600 running Linux (Ubuntu 5.10) with a 1.6 GHz
Pentium M and 512 MB of RAM and python2.4.
The final code snipplet is attached. However, it is essentially
unchanged compared to the piece I posted earlier which already had most
of the global namespace look-up removed. Taking care of sqrt and random
as you suggested didn't improve much anymore. So it's probably not that
educational afterall.
Cheers,
Riko
-----------------------------------------------------
# import some modules
import string
import time
from math import sqrt
# accelerate:
import psyco
# random number init
from random import random, seed
seed(1)
# riskfunc(med, low, high):
# risk function for costs: triangular distribution
# implemented acoording to:
http://www.brighton-webs.co.uk/distributions/triangular.asp
def riskfunc(med, low, high):
if med != 0.0:
u = random()
try:
if u <= (med-low)/(high-low):
r = low+sqrt(u*(high-low)*(med-low))
else:
r = high - sqrt((1.0-u)*(high-low)*(high-med))
except ZeroDivisionError: # case high = low
r = med
else:
r = 0.0
return r
# doMC:
# run the MC of the cost analysis
#
def doMC(Ntrial = 1):
start = time.time()
print 'run MC with ', Ntrial, ' trials'
# now do MC simulation and calculate sums
for i in range(Ntrial):
summe = 0.0
# do MC experiments for all cost entries
for k in range(len(Gcost)):
x = riskfunc(Gcost[k], Gdown[k], Gup[k])
summe +=x
if i%(Ntrial/10) == 0:
print i, 'MC experiment processed, Summe = %10.2f' % (summe)
stop = time.time()
print 'Computing time: ', stop-start
####################################################################
####################################################################
if __name__ == '__main__':
fname_base = 'XFEL_budget-book_Master-2006-01-02_cost'
readCosts(fname_base+'.csv')
psyco.full()
n = 10000
doMC(n)
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