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

The reduction operation

Similar to comm.gather, comm.reduce takes an array of input elements in each process and returns an array of output elements to the root process. The output elements contain the reduced result.

Getting ready

In mpi4py, we define the reduction operation through the following statement:

comm.Reduce(sendbuf, recvbuf, rank_of_root_process, op = type_of_reduction_operation) 

We must note that the difference with the comm.gather statement resides in the op parameter, which is the operation that you wish to apply to your data, and the mpi4py module contains a set of reduction operations that can be used. 

How to do it...