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

Collective communication using Alltoall

The Alltoall collective communication combines the scatter and gather functionalities.

How to do it...

In the following example, we'll see an mpi4py implementation of comm.Alltoall. We'll consider a communicator a group of processes, where each process sends and receives an array of numerical data from the other processes defined in the group:

  1. For this example, the relevant mpi4py and numpy libraries must be imported:
from mpi4py import MPI 
import numpy 
  1. As in the previous example, we need to set the same parameters, comm, size, and rank:
size = comm.Get_size() 
rank = comm.Get_rank() 
  1. Hence, we must define the data that each process...