How can multithreaded processing of CPU-bound tasks be performed in Python?

In Python, for multithreaded processing of CPU-bound tasks, the standard threading module is inefficient due to the GIL (Global Interpreter Lock), which prevents multiple threads from executing Python bytecode simultaneously.

To effectively utilize multiple CPU cores, it is better to use the multiprocessing module, which creates separate processes with their own Python interpreter, bypassing the GIL limitation.

Example of using multiprocessing:

from multiprocessing import Pool

def cpu_bound_task(x):
    # example computational task
    return x * x

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

This approach allows parallel execution of CPU-intensive tasks and significantly speeds up their execution.