How to Parallelize Python Functions Using Multiprocessing and Parallel Maps?

Parallel Programming in Python

In Python, parallel programming allows certain sections of a program to execute concurrently, potentially enhancing performance. To achieve parallelism in Python, the multiprocessing module is a popular choice.

Example:

Consider a code structure involving two independent functions, solve1 and solve2. To parallelize these functions:

<code class="python">from multiprocessing import Pool
pool = Pool()
result1 = pool.apply_async(solve1, [A])  # Asynchronously evaluate solve1(A)
result2 = pool.apply_async(solve2, [B])  # Asynchronously evaluate solve2(B)
answer1 = result1.get(timeout=10)
answer2 = result2.get(timeout=10)</code>

This code creates a processing pool that spawns processes to handle the asynchronous execution of solve1 and solve2. Each process leverages a different CPU core for simultaneous execution.

Alternative Parallelization Options:

Another option for parallelizing sections of code is to use a parallel map. In such cases, you would have a list of arguments and apply a single function to each argument in parallel:

<code class="python">args = [A, B]
results = pool.map(solve1, args)</code>

Considerations:

While threads can also be used for concurrency, the Global Interpreter Lock (GIL) in Python prevents parallel execution of Python objects, rendering threads ineffective for parallelizing Python code.

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