Miks Pythonis mitme lõime kasutamine ei too kaasa märkimisväärset kiirendust CPU-sõltuvate raskete arvutuste teostamisel?

In the standard implementation of Python, CPython uses the GIL (Global Interpreter Lock), which is a global lock of the interpreter that allows only one Python bytecode to be executed at a time in a thread. Because of this, multithreading does not improve performance in CPU-bound heavy computations, as threads cannot truly run in parallel on multiple cores.

To overcome this limitation, the following are used:

• Multiprocessing (the multiprocessing module), where each process has its own interpreter and GIL.
• Use of C extensions that release the GIL during computations.
• Alternative Python implementations without GIL (e.g., Jython, IronPython).

Example of using multiprocessing for parallel computations:

from multiprocessing import Pool

def heavy_calc(x):
    # Heavy CPU-dependent function
    return x * x

if __name__ == '__main__':
    with Pool(4) as p:
        results = p.map(heavy_calc, range(10))
    print(results)