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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

Chapter 14


What is a reduction operator? What conditions must be satisfied so that an operator can be a reduction operator?

An operator is a reduction operator if it satisfies the following conditions:

  • The operator can reduce an array of elements into one scalar value
  • The end result (the scalar value) is obtained through creating and computing partial tasks

What properties do reduction operators have that are equivalent to the required conditions?

The communicative and associative properties are considered to be equivalent to the requirements for a reduction operator.

What is the connection between reduction operators and concurrent programming?

Reduction operators require communicative and associative properties. Consequently, their sub-tasks have to be able to be processed independently, which makes concurrency and parallelism applicable.

What are some of the considerations that must be made when working with multiprocessing programs that facilitate interprocess communication in Python?

Some considerations include implementing the poison-pill technique, so that sub-tasks are distributed across all consumer processes; calling task_done() on the task queue each time the get() function is called, to ensure that the join() function will not block indefinitely; and avoiding using the qsize() method, which is unreliable and is not implemented on Unix operating systems.

What are some real-life applications of concurrent reduction operators?

Some real-life applications include heavy number-crunching operators and complex programs that utilize logic operators.

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