How to Achieve Parallelism in Python for Enhanced Efficiency?

Parallel Programming in Python

Traditionally, OpenMP is utilized for parallel programming in C . However, Python does not support OpenMP. This raises the question: how can we parallelize specific sections of Python code to enhance efficiency?

Consider the following code structure:

solve1(A)
solve2(B)

Where solve1 and solve2 are independent functions. Our goal is to execute these functions concurrently, reducing the overall running time.

In Python, we can harness the multiprocessing module to achieve parallelism. For the given scenario, a processing pool can be employed:

<code class="python">from multiprocessing import Pool

pool = Pool()
result1 = pool.apply_async(solve1, [A])  # Evaluate "solve1(A)" asynchronously
result2 = pool.apply_async(solve2, [B])  # Evaluate "solve2(B)" asynchronously
answer1 = result1.get(timeout=10)
answer2 = result2.get(timeout=10)</code>

This approach creates processes that handle specific tasks concurrently. Since no processes are specified, the code utilizes the available CPU cores, allowing each core to execute a process simultaneously.

For mapping a list to a single function, we can use:

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

Note that using threads is not recommended as the GIL (Global Interpreter Lock) restricts operations on Python objects. Utilizing processes circumvents this limitation, enabling true concurrency.

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