Parallel execution of a list of functions

Question

So using the multiprocess module it is easy to run a function in parallel with different arguments like this:

from multiprocessing import Pool

def f(x):
    ret         


        

Answer 1

You can use Pool.apply_async() for that. You bundle up tasks in the form of (function, argument_tuple) and feed every task to apply_async().

from multiprocessing import Pool
from itertools import repeat


def f(x):
    for _ in range(int(50e6)): # dummy computation
        pass
    return x ** 2


def g(x):
    for _ in range(int(50e6)): # dummy computation
        pass
    return x ** 3


def parallelize(n_workers, functions, arguments):
    # if you need this multiple times, instantiate the pool outside and
    # pass it in as dependency to spare recreation all over again
    with Pool(n_workers) as pool:
        tasks = zip(functions, repeat(arguments))
        futures = [pool.apply_async(*t) for t in tasks]
        results = [fut.get() for fut in futures]
    return results


if __name__ == '__main__':

    N_WORKERS = 2

    functions = f, g
    results = parallelize(N_WORKERS, functions, arguments=(10,))
    print(results)

Example Output:

[100, 1000]

Process finished with exit code 0