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

Introducing PyOpenCL

PyOpenCL is a sister project to PyCUDA. It is a binding library that provides full access to OpenCL's API from Python and is also by Andreas Klöckner. It features many of the same concepts as PyCUDA, including cleanup for out-of-scope objects, partial abstraction over data structures, and error handling, all with minimal overhead. The project is available under the MIT license; its documentation is very good and plenty of guides and tutorials can be found online.

The main focus of PyOpenCL is to provide a lightweight connection between Python and OpenCL, but it also includes support for templates and metaprograms. The flow of a PyOpenCL program is almost exactly the same as a C or C++ program for OpenCL. The host program prepares the call of the device program, launches it, and then waits for the result.