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)

Summary

Image processing is the task of analyzing and manipulating digital image files to create new versions of the images or to extract important data from them. These digital images are represented by tables of pixels, which are RGB values, or in essence, tuples of numbers. Therefore, digital images are simply multidimensional matrices of numbers, which results in the fact that image processing tasks typically come down to heavy number-crunching.

Since images can be analyzed and processed independently from each other in an image processing application, concurrent and parallel programming specifically multiprocessing provides a way to achieve significant improvements in execution time for the application. Additionally, there are a number of good practices to follow while implementing your own concurrent image processing program.

So far in this book, we have...