Book Image

Learning Concurrency in Python

By : Elliot Forbes
Book Image

Learning Concurrency in Python

By: Elliot Forbes

Overview of this book

Python is a very high level, general purpose language that is utilized heavily in fields such as data science and research, as well as being one of the top choices for general purpose programming for programmers around the world. It features a wide number of powerful, high and low-level libraries and frameworks that complement its delightful syntax and enable Python programmers to create. This book introduces some of the most popular libraries and frameworks and goes in-depth into how you can leverage these libraries for your own high-concurrent, highly-performant Python programs. We'll cover the fundamental concepts of concurrency needed to be able to write your own concurrent and parallel software systems in Python. The book will guide you down the path to mastering Python concurrency, giving you all the necessary hardware and theoretical knowledge. We'll cover concepts such as debugging and exception handling as well as some of the most popular libraries and frameworks that allow you to create event-driven and reactive systems. By the end of the book, you'll have learned the techniques to write incredibly efficient concurrent systems that follow best practices.
Table of Contents (20 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Index

Computer memory architecture styles


When we start to speed up our programs by introducing concepts such as concurrency and parallelism, we start to face new challenges that must be thought about and addressed appropriately. One of the biggest challenges we start to face is the speed at which we can access data. It's important to note at this stage that if we cannot access data fast enough, then this becomes a bottleneck for our programs, and no matter how expertly we design our systems, we'll never see any performance gains.

Computer designers have been increasingly looking for ways to improve the ease with which we can develop new parallel solutions to problems. One of the ways they have managed to improve things is by providing a single physical address space that all of our multiple cores can access within a processor. This removes a certain amount of complexity away from us, as programmers, and allows us to instead focus on ensuring that our code is thread safe. There are a number of...