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Parallel Pi Calculation using Python's multiprocessing module
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''' listing 6: pi_mp.py | |
Multiprocessing based code to estimate the value of PI | |
using monte carlo sampling | |
Ref: http://math.fullerton.edu/mathews/n2003/montecarlopimod.html | |
Uses workers: | |
http://docs.python.org/library/multiprocessing.html#module-multiprocessing.pool | |
''' | |
import random | |
import multiprocessing | |
from multiprocessing import Pool | |
import timeit | |
#caculate the number of points in the unit circle | |
#out of n points | |
def monte_carlo_pi_part(n): | |
count = 0 | |
for i in range(n): | |
x=random.random() | |
y=random.random() | |
# if it is within the unit circle | |
if x*x + y*y <= 1: | |
count=count+1 | |
#return | |
return count | |
def calc(): | |
np = multiprocessing.cpu_count() | |
print 'You have {0:1d} CPUs'.format(np) | |
# Nummber of points to use for the Pi estimation | |
n = 10000000 | |
# iterable with a list of points to generate in each worker | |
# each worker process gets n/np number of points to calculate Pi from | |
part_count=[n/np for i in range(np)] | |
#Create the worker pool | |
# http://docs.python.org/library/multiprocessing.html#module-multiprocessing.pool | |
pool = Pool(processes=np) | |
# parallel map | |
count=pool.map(monte_carlo_pi_part, part_count) | |
print "Esitmated value of Pi:: ", sum(count)/(n*1.0)*4 | |
if __name__=='__main__': | |
time = timeit.Timer ("calc()","from __main__ import calc, monte_carlo_pi_part") | |
print(time.timeit(1)) | |
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