How does the use of multithreading affect the efficiency of executing CPU-bound tasks in Python?

In Python, due to the Global Interpreter Lock (GIL), multithreading is inefficient for CPU-bound tasks, which are those that heavily utilize the processor.

The GIL allows only one thread to execute Python bytecode at a time, so even with multiple threads, computations will not run in parallel on multiple cores.

As a result, multithreading in Python is more suitable for I/O-bound tasks (e.g., network requests, file operations), where threads often wait for input/output completion.

For effective parallelism in CPU-bound tasks in Python, the multiprocessing module is used, which launches multiple processes, each with its own interpreter and GIL.

Example of using multiprocessing:

from multiprocessing import Pool

def f(x):
    return x * x

with Pool(4) as p:
    print(p.map(f, [1, 2, 3, 4]))