What\'s the Best Approach to Parallelize Independent Python Functions Asynchronously?

Parallel Programming in Python: A Comprehensive Guide

For C , OpenMP provides a straightforward mechanism for parallelizing code. However, Python users face challenges when seeking similar capabilities. This guide aims to address these challenges by presenting a solution tailored to Python, enabling programmers to harness the power of parallel processing to optimize their code.

Specifically, we will explore a scenario involving two independent functions, solve1(A) and solve2(B), which need to be executed in parallel rather than sequentially. The sample code provided highlights these functions as:

<code class="python">def solve(Q, G, n):
    ...
    setinneropt, partition, x = setinner(Q, G, n)
    ...
    if ...
        node1 = partition[0]
        node2 = partition[1]
    ...</code>

The key functions here are setinner and setouter, representing the independent tasks we aim to parallelize.

The recommended approach utilizes Python's multiprocessing module, particularly its processing pools. These pools employ generic worker processes, allocating one worker per CPU core on your machine. Consequently, multiple worker processes can concurrently execute the assigned parallel tasks.

For our specific scenario, the code would look like this:

<code class="python">from multiprocessing import Pool
pool = Pool()
result1 = pool.apply_async(setinner, [Q, G, n])  # Evaluate "setinner(Q, G, n)" asynchronously
result2 = pool.apply_async(setouter, [Q, G, n])  # Evaluate "setouter(Q, G, n)" asynchronously
answer1 = result1.get(timeout=10)
answer2 = result2.get(timeout=10)</code>

By creating a processing pool, we essentially delegate the execution of these independent functions to separate processes, effectively achieving parallel processing.

It's important to note that using threads for parallel programming in Python is not advisable due to the Global Interpreter Lock (GIL), which inhibits simultaneous operations on Python objects. Therefore, processes, rather than threads, are recommended for Python's parallel programming endeavors.

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