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Parallel Programming with Python

Parallel Programming with Python

By : Palach
3 (9)
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Parallel Programming with Python

Parallel Programming with Python

3 (9)
By: 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)
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9
Index

Using PP to calculate the Fibonacci series term on SMP architecture

Time to get into action! Let's solve our case study involving the Fibonacci series for multiple inputs using PP in the SMP architecture. I am using a notebook armed with a two-core processor and four threads.

We will import only two modules for this implementation, os and pp. The os module will be used only to obtain a PID of the processes in execution. We will have a list called input_list with the values to be calculated and a dictionary to group the results, which we will call result_dict. Then, we go to the chunk of code as follows:

import os, pp
input_list = [4, 3, 8, 6, 10]
result_dict = {}

Then, we define a function called fibo_task, which will be executed by parallel processes. It will be our func argument passed by the submit method of the Server class. The function does not feature major changes in relation to previous chapters, except that the return is now done by using a tuple to encapsulate the value received...

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