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
About the Author
About the Reviewers

How to exchange objects between processes

The development of parallel applications has the need for the exchange of data between processes. The multiprocessing library has two communication channels with which it can manage the exchange of objects: queues and pipes.

Communication channels in the multiprocessing module

Using queue to exchange objects

As explained before, it is possible for us to share data with the queue data structure.

A queue returns a process shared queue, is thread and process safe, and any serializable object (Python serializes an object using the pickable module) can be exchanged through it.

How to do it...

In the following example, we show you how to use a queue for a producer-consumer problem. The producer class creates the item and queues and then, the consumer class provides the facility to remove the inserted item:

import multiprocessing
import random
import time

class producer(multiprocessing.Process):
    def __init__(self, queue):