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Parallel Programming with Python

You're reading from  Parallel Programming with Python

Product type Book
Published in Jun 2014
Publisher
ISBN-13 9781783288397
Pages 124 pages
Edition 1st Edition
Languages

Table of Contents (16) Chapters

Parallel Programming with Python
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
1. Contextualizing Parallel, Concurrent, and Distributed Programming 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

Obtaining the highest Fibonacci value for multiple inputs


It is known that the Fibonacci sequence is defined as follows:

In practical terms, calculating the Fibonacci value for the terms 0 to 10, the result would be 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, and 55.

An example of Python code to calculate Fibonacci returning the highest value using the iterative method is as follows:

def fibonacci(input):
    a, b = 0, 1
    foritem in range(input):
        a, b = b, a + b
    return a

The Fibonacci function calculates the highest Fibonacci value for a specific piece of input data. Let us picture a hypothetical scenario in which it is necessary to calculate Fibonacci values, and this website will receive several inputs from a user. Suppose the user provides an array of values as input, so making these calculations sequentially would be interesting. But, what if 1 million users are connected at the same time to make requests? In this case, some users would have to wait for quite a long time until they...

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