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Advanced Python Programming

You're reading from   Advanced Python Programming Build high performance, concurrent, and multi-threaded apps with Python using proven design patterns

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Product type Course
Published in Feb 2019
Publisher Packt
ISBN-13 9781838551216
Length 672 pages
Edition 1st Edition
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Authors (3):
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Quan Nguyen Quan Nguyen
Author Profile Icon Quan Nguyen
Quan Nguyen
Sakis Kasampalis Sakis Kasampalis
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Sakis Kasampalis
Dr. Gabriele Lanaro Dr. Gabriele Lanaro
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Dr. Gabriele Lanaro
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Table of Contents (41) Chapters Close

Title Page
Copyright
About Packt
Contributors
Preface
Benchmarking and Profiling FREE CHAPTER Pure Python Optimizations Fast Array Operations with NumPy and Pandas C Performance with Cython Exploring Compilers Implementing Concurrency Parallel Processing Advanced Introduction to Concurrent and Parallel Programming Amdahl's Law Working with Threads in Python Using the with Statement in Threads Concurrent Web Requests Working with Processes in Python Reduction Operators in Processes Concurrent Image Processing Introduction to Asynchronous Programming Implementing Asynchronous Programming in Python Building Communication Channels with asyncio Deadlocks Starvation Race Conditions The Global Interpreter Lock The Factory Pattern The Builder Pattern Other Creational Patterns The Adapter Pattern The Decorator Pattern The Bridge Pattern The Facade Pattern Other Structural Patterns The Chain of Responsibility Pattern The Command Pattern The Observer Pattern 1. Appendix 2. Other Books You May Enjoy Index

Introduction to parallel programming


In order to parallelize a program, it is necessary to divide the problem into subunits that can run independently (or almost independently) from each other.

A problem where the subunits are totally independent from each other is called embarrassingly parallel. An element-wise operation on an array is a typical example--the operation needs to only know the element it is handling at the moment. Another example is our particle simulator. Since there are no interactions, each particle can evolve independently from the others. Embarrassingly parallel problems are very easy to implement and perform very well on parallel architectures.

Other problems may be divided into subunits but have to share some data to perform their calculations. In those cases, the implementation is less straightforward and can lead to performance issues because of the communication costs.

We will illustrate the concept with an example. Imagine that you have a particle simulator, but this...

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