Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
Arrow up icon
GO TO TOP
Parallel Programming with Python

You're reading from   Parallel Programming with Python Develop efficient parallel systems using the robust Python environment.

Arrow left icon
Product type Paperback
Published in Jun 2014
Publisher
ISBN-13 9781783288397
Length 124 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
 Palach Palach
Author Profile Icon Palach
Palach
Arrow right icon
View More author details
Toc

Table of Contents (10) Chapters Close

Preface 1. Contextualizing Parallel, Concurrent, and Distributed Programming FREE CHAPTER 2. Designing Parallel Algorithms 3. Identifying a Parallelizable Problem 4. Using the threading and concurrent.futures Modules 5. Using Multiprocessing and ProcessPoolExecutor 6. Utilizing Parallel Python 7. Distributing Tasks with Celery 8. Doing Things Asynchronously Index

Implementing multiprocessing communication

The multiprocessing module (http://docs.python.org/3/library/multiprocessing.html) allows two ways of communication among processes, both based on the message passing paradigm. As seen previously, the message passing paradigm is based on the lack of synchronizing mechanisms as copies of data are exchanged among processes.

Using multiprocessing.Pipe

A pipe consists of a mechanism that establishes communication between two endpoints (two processes in communication). It is a way to create a channel so as to exchange messages among processes.

Tip

The official Python documentation recommends the use of a pipe for every two endpoints since there is no guarantee of reading safety by another endpoint simultaneously.

In order to exemplify the use of the multiprocessing.Pipe object, we will implement a Python program that creates two processes, A and B. Process A sends a random integer value in intervals from 1 to 10 to process B, and process B will display it...

lock icon The rest of the chapter is locked
Visually different images
CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Parallel Programming with Python
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime
Modal Close icon
Modal Close icon