Python_Multiprocessing_Concurrent_Futures.py

# Python multiprocessing - Process-based parallelism
# The following scripts are written to demonstrate multiprocessing (Process-based parallelism) 
# using Python.
# Multiprocessing is a Python package that supports spawning processes using an API similar to 
# the threading module. The multiprocessing package offers both local and remote concurrency, 
# effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. 
# Due to this, the multiprocessing module allows the programmer to fully leverage multiple 
# processors on a given machine. It runs on both Unix and Windows.
# The multiprocessing module also introduces APIs which do not have analogs in the threading module. 
# A prime example of this is the Pool object which offers a convenient means of parallelizing the 
# execution of a function across multiple input values, distributing the input data across processes 
# (data parallelism). 
# ProcessPoolExecutor.
# The ProcessPoolExecutor class is an Executor subclass that uses a pool of processes to execute calls 
# asynchronously. 
# ProcessPoolExecutor uses the multiprocessing module, which allows it to side-step the Global Interpreter 
# Lock but also means that only picklable objects can be executed and returned.

import concurrent.futures
import math

PRIMES = [
    112272535095293,
    112582705942171,
    112272535095293,
    115280095190773,
    115797848077099,
    1099726899285419]

def is_prime(n):

    if n % 2 == 0:
        return False

    sqrt_n = int(math.floor(math.sqrt(n)))

    for i in range(3, sqrt_n + 1, 2):
        if n % i == 0:
            return False
    return True

def main():

    with concurrent.futures.ProcessPoolExecutor() as executor:

        for number, prime in zip(PRIMES, executor.map(is_prime, PRIMES)):
            print('%d is prime: %s' % (number, prime))

if __name__ == '__main__':
    main()