Have you had experience with multithreading in Python and how did you implement parallel task execution?

In Python, the modules threading and multiprocessing are often used to implement parallel task execution.

• threading is suitable for I/O-bound tasks, as threads do not execute simultaneously on multiple cores due to the GIL (Global Interpreter Lock), especially for CPU-intensive tasks.
• multiprocessing creates separate processes, bypassing the GIL limitation, and is suitable for CPU-intensive tasks.

Example using threading for parallel function execution:

import threading

def task(name):
    print(f"Task {name} started")
    # simulate work
    import time
    time.sleep(2)
    print(f"Task {name} completed")

threads = []
for i in range(3):
    t = threading.Thread(target=task, args=(i,))
    threads.append(t)
    t.start()

for t in threads:
    t.join()

For CPU-intensive tasks, it is better to use multiprocessing:

from multiprocessing import Process

def task(name):
    print(f"Process {name} started")
    # heavy computation
    result = sum(i*i for i in range(10**6))
    print(f"Process {name} finished with result {result}")

processes = []
for i in range(3):
    p = Process(target=task, args=(i,))
    processes.append(p)
    p.start()

for p in processes:
    p.join()

Thus, the choice of approach depends on the task type: I/O or CPU.