🧠 Systems Concepts (read-only)

multiprocessing — true parallelism

Bypass the GIL by spawning OS processes.

For CPU-bound work, spawn processes. Each has its own interpreter and GIL, so they run in parallel on multiple cores.

from multiprocessing import Pool

def square(n): return n * n

if __name__ == "__main__":
    with Pool(4) as p:
        print(p.map(square, range(8)))
# [0, 1, 4, 9, 16, 25, 36, 49]

**When to reach for it**

Heavy math / image processing → Pool or ProcessPoolExecutor.
Data must cross process boundaries → use Queue, Pipe, or shared memory.
Always guard the entry point with if __name__ == "__main__": on Windows / macOS spawn-start.

> 🧪 Concept-only here — the browser sandbox runs one process. Try the snippet in real CPython.