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Parallel Programming with Python

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

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Product type Paperback
Published in Jun 2014
Publisher
ISBN-13 9781783288397
Length 124 pages
Edition 1st Edition
Languages
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Author (1):
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 Palach Palach
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Palach
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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

Using threading to obtain the Fibonacci series term with multiple inputs

Now it is time for the truth. The mission is to parallelize the execution of the terms of the Fibonacci series when multiple input values are given. For didactical purposes, we will fix the input values in the four elements and the four threads to process each element, simulating a perfect symmetry among workers and tasks to be executed. The algorithm will work as follows:

  1. First, a list will store the four values to be calculated and the values will be sent into a structure that allows synchronized access of threads.
  2. After the values are sent to the synchronized structure, the threads that calculate the Fibonacci series need to be advised that the values are ready to be processed. For this, we will use a thread synchronization mechanism called Condition. (The Condition mechanism is one of the Python objects that offer data access synchronization mechanisms shared among threads; you can learn more at http://docs.python...
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