monte carlo method of estimating pi using mpi

pi_carlo_mpi.py

#!/usr/bin/env python3

''' 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
Adapted for python3 and mpi from:
https://gist.githubusercontent.com/amitsaha/2036026/raw/77e45e5a9f6c8e030a9fc7a8f5b6450bc8348a28/pi_mp.py
'''

from mpi4py import MPI
import random


def monte_carlo_pi_part(n):
    """
    caculate the number of points in the unit circle
    out of n points.
    """

    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 count


if __name__ == '__main__':

    comm = MPI.COMM_WORLD
    rank = comm.Get_rank()
    number_nodes=comm.Get_attr(MPI.UNIVERSE_SIZE)

    # Nummber of points to use for the Pi estimation
    n = 10000000 // number_nodes

    # iterable with a list of points to generate in each worker
    # each worker process gets n/np number of points to calculate Pi from

    # Do the calculation and send it to the first worker.
    comm.send(monte_carlo_pi_part(n), dest=0, tag=42)


    # Receive all the samples.
    count = 0
    if rank == 0:
        print(f'Number nodes: {number_nodes}')
        for node in range(number_nodes):
            print(f'node: {node}')
            count += comm.recv(source=node, tag=42)


        print("Esitmated value of Pi: ", count / (n * number_nodes) * 4)