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 the scatter function

The scatter functionality is very similar to a scatter broadcast, but with one major difference: while comm.bcast sends the same data to all listening processes, comm.scatter can send chunks of data in an array to different processes.

The following diagram illustrates the scatter functionality:

Scattering data from process 0 to processes 1, 2, 3, and 4

The comm.scatter function takes the elements of the array and distributes them to the processes according to their rank, for which the first element will be sent to process 0, the second element to process 1, and so on. The function implemented in mpi4py is as follows:

recvbuf  = comm.scatter(sendbuf, rank_of_root_process) 

How to do it...