concurrency_in_python/single_process_vs_multi_process/main.py

from multiprocessing import Process
from random import randint
from time import time

# This example compare the speed of CPU-bound operations
# when using a sequential method and also when using a 
# multi-process method with the 'multiprocessing' Python
# package


def calculatePrimeFactors(n):
    primfac = []
    d = 2
    while d*d <= n:
        while (n % d) == 0:
            primfac.append(d)  # supposing you want multiple factors repeated
            n //= d
        d += 1
        if n > 1:
            primfac.append(n)
    return primfac


# Sequential Example


def seq_crunch():
    print("Starting sequential number crunching")
    t0 = time()
    for i in range(10000):
        rand = randint(20000, 100000000)
        calculatePrimeFactors(rand)
    t1 = time()
    return f'{t1-t0:.2f}'



# Multi-processing Example


def executeProc():
 for i in range(1000):
    rand = randint(20000, 100000000)
    calculatePrimeFactors(rand)


def multi_proc_crunch():
    print("Starting multi-process number crunching")
    t0 = time()
    processes = []
    # Here we create our processes and kick them off
    for i in range(10):
        proc = Process(target=executeProc, args=())
        processes.append(proc)
        proc.start()
    # Again we use the .join() method in order to wait for
    # execution to finish for all of our processes
    for process in processes:
        process.join()
    t1 = time()
    return f'{t1-t0:.2f}'

if __name__ == '__main__':
    seq_time = seq_crunch()
    multi_proc_time = multi_proc_crunch()
    print('\n==================================')
    print(f'Time to crunch prime factors sequentially: {seq_time}s')
    print(f'Time to crunch prime factors using multi-processing: {multi_proc_time}s')
    print('==================================')