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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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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

The divide and conquer technique

When you face a complex issue, the first thing to be done is to decompose the problem in order to identify parts of it that may be handled independently. In general, the parallelizable parts in a solution are in pieces that can be divided and distributed for them to be processed by different workers. The technique of dividing and conquering involves splitting the domain recursively until an indivisible unit of the complete issue is found and solved. The sort algorithms, such as merge sort and quick sort , can be resolved by using this approach.

The following diagram shows the application of a merge sort in a vector of six elements, making the divide and conquer technique visible:

The divide and conquer technique

Merge sort (divide and conquer)

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