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Learning Concurrency in Python

You're reading from   Learning Concurrency in Python Build highly efficient, robust, and concurrent applications

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787285378
Length 360 pages
Edition 1st Edition
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Concepts
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Author (1):
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Elliot Forbes Elliot Forbes
Author Profile Icon Elliot Forbes
Elliot Forbes
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Table of Contents (13) Chapters Close

Preface 1. Speed It Up! FREE CHAPTER 2. Parallelize It 3. Life of a Thread 4. Synchronization between Threads 5. Communication between Threads 6. Debug and Benchmark 7. Executors and Pools 8. Multiprocessing 9. Event-Driven Programming 10. Reactive Programming 11. Using the GPU 12. Choosing a Solution

Synchronization between threads


So you know what threads are and how to properly start and end them in Python, and hopefully, you are starting to realize at least some of the complexity that it takes to implement concurrent programs. But how do we make sure that we are implementing multithreading in a safe way without compromising the flow of our program? In this chapter, we'll be introducing some of the fundamental issues that can plague multithreaded applications if not guarded against.

Before we cover some of the key synchronization primitives, we must first have a look at some of the issues that can occur from using the said primitives. This leads us directly into one of the biggest and most feared issues one can face when designing concurrent systems, that is, deadlock. One of the best ways to illustrate this concept of deadlock is to look at the Dining Philosophers Problem.

The Dining Philosophers

The Dining Philosophers problem is one of the most famous illustration of some of the problems...

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