Book Image

Python Parallel Programming Cookbook - Second Edition

By : Giancarlo Zaccone
Book Image

Python Parallel Programming Cookbook - Second Edition

By: Giancarlo Zaccone

Overview of this book

<p>Nowadays, it has become extremely important for programmers to understand the link between the software and the parallel nature of their hardware so that their programs run efficiently on computer architectures. Applications based on parallel programming are fast, robust, and easily scalable. </p><p> </p><p>This updated edition features cutting-edge techniques for building effective concurrent applications in Python 3.7. The book introduces parallel programming architectures and covers the fundamental recipes for thread-based and process-based parallelism. You'll learn about mutex, semaphores, locks, queues exploiting the threading, and multiprocessing modules, all of which are basic tools to build parallel applications. Recipes on MPI programming will help you to synchronize processes using the fundamental message passing techniques with mpi4py. Furthermore, you'll get to grips with asynchronous programming and how to use the power of the GPU with PyCUDA and PyOpenCL frameworks. Finally, you'll explore how to design distributed computing systems with Celery and architect Python apps on the cloud using PythonAnywhere, Docker, and serverless applications. </p><p> </p><p>By the end of this book, you will be confident in building concurrent and high-performing applications in Python.</p>
Table of Contents (16 chapters)
Title Page
Dedication

Introducing distributed computing

Parallel and distributed computing are similar technologies designed to increase the amount of processing power available for a specific task. Generally, these methods are used to solve problems that require large computational capabilities.

When the problem is divided into many small pieces, individual sections of the problem can be calculated by many processors simultaneously. This allows more processing power to be exercised on the problem than can be provided by a single processor.

The main difference between parallel and distributed processing is that parallel configurations include many processors within a single system, while distributed configurations exploit the processing power of many computers simultaneously.

Let's look at the other differences:

Parallel processing Distributed processing
Parallel processing has the...