Book Image

Mastering Concurrency in Python

By : Quan Nguyen
Book Image

Mastering Concurrency in Python

By: Quan Nguyen

Overview of this book

Python is one of the most popular programming languages, with numerous libraries and frameworks that facilitate high-performance computing. Concurrency and parallelism in Python are essential when it comes to multiprocessing and multithreading; they behave differently, but their common aim is to reduce the execution time. This book serves as a comprehensive introduction to various advanced concepts in concurrent engineering and programming. Mastering Concurrency in Python starts by introducing the concepts and principles in concurrency, right from Amdahl's Law to multithreading programming, followed by elucidating multiprocessing programming, web scraping, and asynchronous I/O, together with common problems that engineers and programmers face in concurrent programming. Next, the book covers a number of advanced concepts in Python concurrency and how they interact with the Python ecosystem, including the Global Interpreter Lock (GIL). Finally, you'll learn how to solve real-world concurrency problems through examples. By the end of the book, you will have gained extensive theoretical knowledge of concurrency and the ways in which concurrency is supported by the Python language
Table of Contents (22 chapters)

Real-life applications of concurrent reduction operators

The communicative and associative nature of the way reduction operators process their data enables the subtasks of an operator to be processed independently, and is thus highly connected to concurrency and parallelism. Consequently, various topics in concurrent programming could be related to reduction operators, and by applying the same principles of reduction operators, problems regarding those topics could be made more intuitive and efficient.

As we have seen, add and multiply operators are reduction operators. More generally, number-crunching problems that usually involve communicative and associative operators are prime candidates for applying concurrency and parallelism. This is actually a true case for the famous, and arguably one of the most used modules in Python—NumPy, whose code is implemented to be as...