Book Image

Python Parallel Programming Cookbook

By : Giancarlo Zaccone
Book Image

Python Parallel Programming Cookbook

By: Giancarlo Zaccone

Overview of this book

This book will teach you parallel programming techniques using examples in Python and will help you explore the many ways in which you can write code that allows more than one process to happen at once. Starting with introducing you to the world of parallel computing, it moves on to cover the fundamentals in Python. This is followed by exploring the thread-based parallelism model using the Python threading module by synchronizing threads and using locks, mutex, semaphores queues, GIL, and the thread pool. Next you will be taught about process-based parallelism where you will synchronize processes using message passing along with learning about the performance of MPI Python Modules. You will then go on to learn the asynchronous parallel programming model using the Python asyncio module along with handling exceptions. Moving on, you will discover distributed computing with Python, and learn how to install a broker, use Celery Python Module, and create a worker. You will understand anche Pycsp, the Scoop framework, and disk modules in Python. Further on, you will learnGPU programming withPython using the PyCUDA module along with evaluating performance limitations.
Table of Contents (13 chapters)
Python Parallel Programming Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Event loop management with Asyncio


The Python module Asyncio provides facilities to manage events, coroutines, tasks and threads, and synchronization primitives to write concurrent code. The main components and concepts of this module are:

  • An event loop: The Asyncio module allows a single event loop per process

  • Coroutines: This is the generalization of the subroutine concept. Also, a coroutine can be suspended during the execution so that it waits for external processing (some routine in I/O) and returns from the point at which it had stopped when the external processing was done.

  • Futures: This defines the Future object, such as the concurrent.futures module that represents a computation that has still not been accomplished.

  • Tasks: This is a subclass of Asyncio that is used to encapsulate and manage coroutines in a parallel mode.

In this recipe, the focus is on handling events. In fact, in the context of asynchronous programming, events are very important since they are inherently asynchronous...