gil_example.py

import threading
import time


def cpu_bound_task(iterations):
    """
    A simple CPU-bound function.
    It performs a large number of computations to keep the CPU busy.
    """
    total = 0
    for _ in range(iterations):
        # Some repetitive math operation
        for i in range(1000000):
            total += i**2
    return total


def run_single_thread(iterations):
    start = time.time()
    cpu_bound_task(iterations)
    end = time.time()
    print(f"Single-threaded: finished in {end - start:.2f} seconds.")


def run_multi_thread(iterations):
    """
    Run two threads doing the same CPU-bound work.
    Because of the GIL, only one thread can execute Python bytecode at a time.
    We will see that this does not provide the same speedup you'd expect
    if threads truly ran in parallel on multiple cores.
    """
    start = time.time()

    # Create two threads
    t1 = threading.Thread(target=cpu_bound_task, args=(iterations,))
    t2 = threading.Thread(target=cpu_bound_task, args=(iterations,))

    # Start threads
    t1.start()
    t2.start()

    # Wait for both threads to finish
    t1.join()
    t2.join()

    end = time.time()
    print(f"Multi-threaded: finished in {end - start:.2f} seconds.")


if __name__ == "__main__":
    # Adjust the number of iterations as needed (higher = more CPU-bound work)
    iterations = 50

    run_single_thread(iterations)
    run_multi_thread(iterations)