Parallel Pi Calculation using Python's multiprocessing module

pi_mp.py

''' 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     

for #L43, this may better for understanding:

part_count = [n/np] * np

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