How to work with the GIL

There are a few ways to deal with the GIL in your Python applications, which will be addressed as follows.

Implementing multiprocessing, rather than multithreading 

This is perhaps the most popular and easiest method to circumvent the GIL and achieve optimal speed in a concurrent program. As the GIL only prevents multiple threads from executing CPU-bound tasks simultaneously, processes executing over multiple cores of a system, each having its own memory space, are completely immune to the GIL.

Specifically, considering the preceding countdown example, let's compare the performance of that CPU-bound program when it is sequential, multithreading, and multiprocessing. Navigate to the Chapter22/example3.py file; the first part of the program is identical to what we saw earlier, but at the end we add in an implementation of a multiprocessing solution for the problem of counting down from 50,000,000, using two separate processes:

# Chapter22/example3.py

import time
import...