How Can I Parallelize Simple Python Loops to Overcome the GIL Limitation?

Parallelizing Simple Python Loops

The global interpreter lock (GIL) in CPython prevents concurrent execution of Python code by multiple threads. Therefore, utilizing processes is more effective for CPU-bound workloads like the one showcased in the sample loop.

The Python standard library provides two straightforward methods for creating process pools:

1. multiprocessing Module:

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

2. concurrent.futures.ProcessPoolExecutor:

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

Using either method, the calc_stuff function is executed in parallel, and the results are collected into the output lists out1, out2, and out3. This approach effectively parallelizes the computation and utilizes multiple processors on the system.

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