What is multiprocessing?

Multiprocessing runs work in multiple processes.

• Each process has its own Python interpreter and memory space.
• This can use multiple CPU cores.

Why multiprocessing helps CPU-heavy work

In CPython, threads are limited by the GIL for CPU-bound code.

Multiprocessing avoids this because:

• each process has its own GIL

When to use multiprocessing

Best for CPU-bound tasks:

• heavy number crunching
• image processing
• data transformations
• simulations

For I/O-bound tasks, threading or async may be better.

Important note (Windows / notebooks)

When using multiprocessing, always guard code with:

if __name__ == "__main__":
    # start processes here
    pass

if __name__ == "__main__":
    # start processes here
    pass

This avoids infinite child-process spawning on some platforms.

🧪 Try It Yourself

Exercise 1 – Start a Process

Exercise 2 – Process Pool map()

Exercise 3 – Multiprocessing Queue