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Parallel Programming with Python

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Parallel Programming with Python

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Parallel Programming with Python
Jun, 2014 | 124 pages
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Table of Contents (16 chapters)
Parallel Programming with Python
Parallel Programming with Python
Credits
Credits
About the Author
About the Author
Acknowledgments
Acknowledgments
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Contextualizing Parallel, Concurrent, and Distributed Programming
Contextualizing Parallel, Concurrent, and Distributed Programming
Why use parallel programming?
Exploring common forms of parallelization
Communicating in parallel programming
Identifying parallel programming problems
Discovering Python's parallel programming tools
Taking care of Python GIL
Summary
2
Designing Parallel Algorithms
Designing Parallel Algorithms
The divide and conquer technique
Using data decomposition
Decomposing tasks with pipeline
Processing and mapping
Summary
3
Identifying a Parallelizable Problem
Identifying a Parallelizable Problem
Obtaining the highest Fibonacci value for multiple inputs
Crawling the Web
Summary
4
Using the threading and concurrent.futures Modules
Using the threading and concurrent.futures Modules
Defining threads
Using threading to obtain the Fibonacci series term with multiple inputs
Crawling the Web using the concurrent.futures module
Summary
5
Using Multiprocessing and ProcessPoolExecutor
Using Multiprocessing and ProcessPoolExecutor
Understanding the concept of a process
Implementing multiprocessing communication
Using multiprocessing to compute Fibonacci series terms with multiple inputs
Crawling the Web using ProcessPoolExecutor
Summary
6
Utilizing Parallel Python
Utilizing Parallel Python
Understanding interprocess communication
Discovering PP
Using PP to calculate the Fibonacci series term on SMP architecture
Using PP to make a distributed Web crawler
Summary
7
Distributing Tasks with Celery
Distributing Tasks with Celery
Understanding Celery
Understanding Celery's architecture
Setting up the environment
Dispatching a simple task
Using Celery to obtain a Fibonacci series term
Defining queues by task types
Using Celery to make a distributed Web crawler
Summary
8
Doing Things Asynchronously
Doing Things Asynchronously
Understanding blocking, nonblocking, and asynchronous operations
Understanding event loop
Using asyncio
Summary
Index
Index

Chapter 4. Using the threading and concurrent.futures Modules

In the previous chapter, we presented some potential problems that may be solved with parallelism. In this chapter, we will analyze the implementation of the solutions of each problem using the threading module from the Python language.

This chapter covers the following topics:

  • Defining threads

  • Choosing between threading and _thread

  • Using threading to obtain the Fibonacci series term for multiple inputs

  • Crawling the Web using the concurrent.futures module

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