Have you paid attention to the use of multiprocessing in your projects?

Multiprocessing in Python is used to run multiple processes in parallel, which helps to bypass the GIL (Global Interpreter Lock) limitation and efficiently utilize multiple CPU cores. In projects, multiprocessing is useful for tasks that require intensive computations or parallel data processing.

For example, when processing large volumes of data or performing independent computational tasks, a pool of processes can be created:

from multiprocessing import Pool

def compute(x):
    return x * x

if __name__ == '__main__':
    with Pool(processes=4) as pool:
        results = pool.map(compute, range(10))
    print(results)

In my projects, I used multiprocessing to speed up data processing and perform tasks in parallel when multithreading was inefficient due to GIL.