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

Dealing with PyCUDA

PyCUDA is a binding library that provides access to CUDA's Python API by Andreas Klöckner. The main features include automatic cleanup, which is tied to an object's lifetime, thus preventing leaks, convenient abstraction over modules and buffers, full access to the driver, and built-in error handling. It is also very light. 

The project is open source under the MIT license, the documentation is very clear, and many different sources found online can provide help and support. The main purpose of PyCUDA is to let a developer invoke CUDA with minimal abstraction from Python, and it also supports CUDA metaprogramming and templatization.

Getting ready

Please follow the instructions on the Andreas...