Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Parallel Programming with Python
  • Table Of Contents Toc
  • Feedback & Rating feedback
Parallel Programming with Python

Parallel Programming with Python

By : Palach
3 (9)
close
close
Parallel Programming with Python

Parallel Programming with Python

3 (9)
By: Palach

Overview of this book

Starting with the basics of parallel programming, you will proceed to learn about how to build parallel algorithms and their implementation. You will then gain the expertise to evaluate problem domains, identify if a particular problem can be parallelized, and how to use the Threading and Multiprocessor modules in Python. The Python Parallel (PP) module, which is another mechanism for parallel programming, is covered in depth to help you optimize the usage of PP. You will also delve into using Celery to perform distributed tasks efficiently and easily. Furthermore, you will learn about asynchronous I/O using the asyncio module. Finally, by the end of this book you will acquire an in-depth understanding about what the Python language has to offer in terms of built-in and external modules for an effective implementation of Parallel Programming. This is a definitive guide that will teach you everything you need to know to develop and maintain high-performance parallel computing systems using the feature-rich Python.
Table of Contents (10 chapters)
close
close
9
Index

Setting up the environment

In this section, we will set up two machines in Linux. The first one, hostname foshan, will perform the client role, where app Celery will dispatch the tasks to be executed. The other machine, hostname Phoenix, will perform the role of a broker, result backend, and the queues consumed by workers.

Setting up the client machine

Let us start the setup of the client machine. In this machine, we will set up a virtual environment with Python 3.3, using the tool pyvenv. The goal of pyvenv is to not pollute Python present in the operating system with additional modules, but to separate the developing environments necessary for each project. We will execute the following command to create our virtual environment:

$pyvenv celery_env

The preceding line creates a directory called celery_env in the current path, which contains all the structures necessary to isolate the developing environment in Python. The following screenshot illustrates the structure created in the celery_env...

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
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon