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

CUDA


In the upcoming sections of this chapter, we'll cover libraries that rely heavily on the use of CUDA. Therefore, it's very important that you are familiar with what CUDA is precisely in the grand scheme of things and how this relates to the libraries that we'll ultimately use.

CUDA is a parallel computing platform and an API that was conceived by the NVIDIA Corporation. It's designed to make our lives as programmers simpler and help us fully leverage the power of the incredibly powerful parallelism that our GPUs have to offer for general purpose programming.

With CUDA, we are able to craft our Python programs in a way that is familiar to us while sprinkling in some of the keywords that CUDA has to offer in order to fully utilize the GPU. These keywords allow us to map appropriate sections of our code base that deal with the particularly computationally expensive calculations to massively parallel hardware and thus drastically improve performance.

Working with CUDA without a NVIDIA graphics...