Why does using threads in Python not allow full parallel execution of tasks?

In Python, the use of threads is limited by the Global Interpreter Lock (GIL). The GIL ensures that only one thread executes Python bytecode at a time, even if there are multiple threads.

Because of this, multithreading in Python does not allow efficient utilization of multiple CPU cores for parallel execution of CPU-intensive tasks. Threads can switch and work in parallel at the I/O level, but not for computations.

For parallel execution of CPU tasks in Python, the multiprocessing module is usually used, which launches multiple processes with their own interpreter and memory, bypassing the GIL limitation.

Example of using threads for I/O, but not for CPU:

import threading

def io_task():
    # for example, a network request
    pass

threads = [threading.Thread(target=io_task) for _ in range(5)]
for t in threads:
    t.start()
for t in threads:
    t.join()