Python Mastery: Multiprocessing and Threading

Using multiprocessing and concurrent.futures for Parallelism

In Python, parallelism can improve performance by allowing multiple tasks to run concurrently, especially on multi-core systems. The multiprocessing module and concurrent.futures library provide tools for managing parallel processes and threads, making it easier to perform tasks that are CPU-bound or I/O-bound.

This chapter will cover the basics of parallelism in Python, how to use multiprocessing to run multiple processes, and how to work with concurrent.futures for both process-based and thread-based parallelism.

1. Introduction to Parallelism in Python

Parallelism is the practice of running multiple tasks simultaneously, often to make programs faster by distributing work across available CPU cores. In Python:

• Multiprocessing is ideal for CPU-bound tasks, as it bypasses the Global Interpreter Lock (GIL) by creating separate memory spaces for each process.
• Multithreading is better suited for I/O-bound tasks, as it allows threads to run concurrently even though they share memory space.

2. Using multiprocessing for CPU-Bound Tasks