What methods and tools for implementing parallelism and asynchrony are applicable in Python?

In Python, several main approaches are used to implement parallelism and asynchrony:

1. Multithreading (threading) — allows running multiple threads within a single process. Due to GIL (Global Interpreter Lock) in CPython, threads cannot execute bytecode simultaneously, so multithreading is effective for I/O-bound tasks.

import threading

def worker():
    print("Work in thread")

thread = threading.Thread(target=worker)
thread.start()
thread.join()

2. Multiprocessing — creates separate processes, each with its own memory, which helps bypass GIL limitations and efficiently utilize multiple CPU cores.

from multiprocessing import Process

def worker():
    print("Work in process")

process = Process(target=worker)
process.start()
process.join()

3. Asynchronous programming (asyncio) — allows writing non-blocking code using coroutines, event loops, and async/await keywords. Suitable for a large number of I/O operations.

import asyncio

async def main():
    print("Start")
    await asyncio.sleep(1)
    print("End")

asyncio.run(main())

4. Libraries and frameworks — for example, concurrent.futures for easy work with threads and processes, gevent or twisted for cooperative multitasking.

The choice of method depends on the task: for CPU-intensive tasks — multiprocessing, for I/O — threading or asyncio.