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

Understanding the GPU architecture

A GPU is a specialized CPU/core for vector processing of graphical data to render images from polygonal primitives. The task of a good GPU program is to make the most of the great level of parallelism and mathematical capabilities offered by the graphics card and minimize all the disadvantages presented by it, such as the delay in the physical connection between the host and device.

GPUs are characterized by a highly parallel structure that allows you to manipulate large datasets in an efficient manner. This feature is combined with rapid improvements in hardware performance programs, bringing the attention of the scientific world to the possibility of using GPUs for purposes other than just rendering images.

A GPU (refer to the following diagram) is composed of several processing units called Streaming Multiprocessors...