Book Image

Advanced Python Programming - Second Edition

By : Quan Nguyen
Book Image

Advanced Python Programming - Second Edition

By: Quan Nguyen

Overview of this book

Python's powerful capabilities for implementing robust and efficient programs make it one of the most sought-after programming languages. In this book, you'll explore the tools that allow you to improve performance and take your Python programs to the next level. This book starts by examining the built-in as well as external libraries that streamline tasks in the development cycle, such as benchmarking, profiling, and optimizing. You'll then get to grips with using specialized tools such as dedicated libraries and compilers to increase your performance at number-crunching tasks, including training machine learning models. The book covers concurrency, a major solution to making programs more efficient and scalable, and various concurrent programming techniques such as multithreading, multiprocessing, and asynchronous programming. You'll also understand the common problems that cause undesirable behavior in concurrent programs. Finally, you'll work with a wide range of design patterns, including creational, structural, and behavioral patterns that enable you to tackle complex design and architecture challenges, making your programs more robust and maintainable. By the end of the book, you'll be exposed to a wide range of advanced functionalities in Python and be equipped with the practical knowledge needed to apply them to your use cases.
Table of Contents (32 chapters)
1
Section 1: Python-Native and Specialized Optimization
8
Section 2: Concurrency and Parallelism
18
Section 3: Design Patterns in Python

Working with the GIL

There are a few ways to deal with the GIL in your Python applications, and these will be addressed in the following sections.

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 Chapter15/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, as...