How Can I Easily Parallelize a Simple Python Loop Using Multiprocessing?

Parallelizing a Simple Python Loop

The provided Python loop iterates over a range and performs a computation for each iteration. While there are multiple ways to parallelize this loop, the question specifies a preference for the easiest approach. Two straightforward methods using multi-processing are explained below.

Multiprocessing with the multiprocessing Module

The multiprocessing module provides a ProcessPool class for creating a pool of processes. The code can be rewritten as follows:

import multiprocessing

pool = multiprocessing.Pool(4)
out1, out2, out3 = zip(*pool.map(calc_stuff, range(0, 10 * offset, offset)))

Here, a pool of four processes is created. The pool.map() method applies the calc_stuff function to each element in the iterable and returns a tuple of results.

Multiprocessing with concurrent.futures.ProcessPoolExecutor

Alternatively, the concurrent.futures module provides a ProcessPoolExecutor class that simplifies the process creation and management. The code becomes:

from concurrent.futures import ProcessPoolExecutor

with ProcessPoolExecutor() as pool:
    out1, out2, out3 = zip(*pool.map(calc_stuff, range(0, 10 * offset, offset)))

Both approaches utilize the multiprocessing module internally and provide an easy way to parallelize the loop in both Linux and other operating systems.

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