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

Heterogeneous Computing

This chapter will help us to explore the Graphics Processing Unit (GPU) programming techniques through the Python language. The continuous evolution of GPUs is revealing how these architectures can bring great benefits to performing complex calculations.

GPUs certainly cannot replace CPUs. However, they are a well-structured and heterogeneous code that is able to exploit the strengths of both types of processors that can, in fact, bring considerable advantages.

We will examine the main development environments for heterogeneous programming, namely, the PyCUDA and Numba environments for Compute Unified Device Architecture (CUDA) and PyOpenCL environments, which are for Open Computing Language (OpenCL) frameworks in their Python version.

In this chapter, we will cover the following recipes:

  • Understanding...