This document provides an introduction to the Python multiprocessing module. It discusses how multiprocessing allows parallel code execution by leveraging all CPUs to improve performance and scalability compared to threading. Multiprocessing uses separate processes that do not share memory, making them better for CPU-bound tasks compared to threads that share memory and are limited by the Global Interpreter Lock. The document outlines some key considerations for multiprocessing like ensuring worker arguments are picklable and provides resources for further information.