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)
