How to Achieve Parallelism in Python Programs

Parallel Programming in Python: A Solution to Parallelism Challenges

Parallel programming aims to improve performance by executing multiple tasks concurrently. In Python, OpenMP, commonly used in C , is not readily applicable. This article addresses the question of implementing parallelism in Python programs, addressing the challenge of running independent functions concurrently.

The given code structure:

solve1(A)
solve2(B)

requires identifying independent functions within the code. In this example, solve1 and solve2 are two separate functions.

To parallelize this code, consider using the multiprocessing module in Python, which allows for process-based parallelism. In the given scenario, a processing pool can be utilized:

<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 utilizes multiple processes, one for each CPU core, to execute the independent functions concurrently. This division of labor can potentially reduce the running time of the program.

Another option for mapping a list to a single function is:

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

It's important to note that threads should not be used for parallelism in Python as the GIL (Global Interpreter Lock) prevents multiple threads from executing Python code simultaneously, rendering parallel execution ineffective.

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