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
Parallel Programming with Python

Parallel Programming with Python

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

Parallel Programming with Python

3 (9)
By: Jan 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

Crawling the Web

Another problem to be studied throughout this book is the implementation of a parallel Web crawler. A Web crawler consists of a computer program that browses the Web to search for information on pages. The scenario to be analyzed is a problem in which a sequential Web crawler is fed by a variable number of Uniform Resource Locators (URLs), and it has to search all the links within each URL provided. Imagining that the number of input URLs may be relatively large, we could plan a solution looking for parallelism in the following way:

  1. Group all the input URLs in a data structure.
  2. Associate data URLs with tasks that will execute the crawling by obtaining information from each URL.
  3. Dispatch the tasks for execution in parallel workers.
  4. The result from the previous stage must be passed to the next stage, which will improve raw collected data, thereby saving them and relating them to the original URLs.

As we can observe in the numbered steps for a proposed solution, there is a combination...

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