DoonopsEdTech
Language

Multithreading & Multiprocessing · Lesson 54 of 56

Multiprocessing Basics

Source: 16-Multithreading and Multiprocessing/multi_processing.py

Start here — no coding background needed

What you will learn

True parallel CPU work using multiple processes.

In simple words

Heavier than threads but uses all CPU cores for heavy math.

Do many tasks at once — advanced; understand ideas first, code locally later.

Easy example — try this first

Easy example — run this first. Change values and press Run again.

Python

Runs in your browser via Pyodide — no server. First run may take a few seconds.

Reference notes (from full bootcamp)

Optional — deeper detail for when you are ready

Reference script from the bootcamp repo. Read the code below; run a simplified version in the playground when marked runnable.

Example HCL
HCL
## PRocesses that run in parallel
### CPU-Bound Tasks-Tasks that are heavy on CPU usage (e.g., mathematical computations, data processing).
## PArallel execution- Multiple cores of the CPU

import multiprocessing

import time

def square_numbers():
    for i in range(5):
        time.sleep(1)
        print(f"Square: {i*i}")

def cube_numbers():
    for i in range(5):
        time.sleep(1.5)
        print(f"Cube: {i * i * i}")

if __name__=="__main__":

    ## create 2 processes
    p1=multiprocessing.Process(target=square_numbers)
    p2=multiprocessing.Process(target=cube_numbers)
    t=time.time()

    ## start the process
    p1.start()
    p2.start()

    ## Wait for the process to complete
    p1.join()
    p2.join()

    finished_time=time.time()-t
    print(finished_time)

Browser practice only — full example needs Python on your computer (files, Flask, threads, etc.).

Practice test — try yourself

Write code, press Check. Wrong answer shows the correct code to copy & run.

You learned "Multiprocessing Basics". Use print() to show: Done: Multiprocessing Basics

Hint: Use one print() with the exact text.

Python