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)

Practical applications of Amdahl's Law

As we have discussed, by analyzing the sequential and parallelizable portion of a given program or system with Amdahl's Law, we can determine, or at least estimate, the upper limit of any potential improvements in speed resulting from parallel computing. Upon obtaining this estimation, we can then make an informed decision on whether an improved execution time is worth an increase in processing power.

From our examples, we can see that Amdahl's Law is applied when you have a concurrent program that is a mixture of both sequentially and executed-in-parallels instructions. By performing analysis using Amdahl's Law, we can determine the speedup through each incrementation of the number of cores available to perform the execution, as well as how close that incrementation is to helping the program achieve the best possible...