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Parallel Pi Calculation using Python's multiprocessing module
''' 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
#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
if __name__=='__main__':
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
@zjuchenyuan

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zjuchenyuan commented Nov 4, 2017

for #L43, this may better for understanding:

part_count = [n/np] * np
@yegortimoshenko

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yegortimoshenko commented Feb 1, 2019

Rust implementation with pre-determined lightweight RNGs: https://gitlab.com/transumption/pi_mp

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