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Mastering Concurrency in Python

You're reading from   Mastering Concurrency in Python Create faster programs using concurrency, asynchronous, multithreading, and parallel programming

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781789343052
Length 446 pages
Edition 1st Edition
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Concepts
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Author (1):
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Quan Nguyen Quan Nguyen
Author Profile Icon Quan Nguyen
Quan Nguyen
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Table of Contents (22) Chapters Close

Preface 1. Advanced Introduction to Concurrent and Parallel Programming FREE CHAPTER 2. Amdahl's Law 3. Working with Threads in Python 4. Using the with Statement in Threads 5. Concurrent Web Requests 6. Working with Processes in Python 7. Reduction Operators in Processes 8. Concurrent Image Processing 9. Introduction to Asynchronous Programming 10. Implementing Asynchronous Programming in Python 11. Building Communication Channels with asyncio 12. Deadlocks 13. Starvation 14. Race Conditions 15. The Global Interpreter Lock 16. Designing Lock-Based and Mutex-Free Concurrent Data Structures 17. Memory Models and Operations on Atomic Types 18. Building a Server from Scratch 19. Testing, Debugging, and Scheduling Concurrent Applications 20. Assessments 21. Other Books You May Enjoy

The concept of deadlock

In the field of computer science, deadlock refers to a specific situation in concurrent programming, in which no progress can be made and the program becomes locked in its current state. In most cases, this phenomenon is caused by a lack of, or mishandled, coordination between different lock objects (for thread synchronization purposes). In this section, we will discuss a thought experiment commonly known as the Dining Philosophers problem, in order to illustrate the concept of deadlock and its causes; from there, you will learn how to simulate the problem in a Python concurrent program.

The Dining Philosophers problem

The Dining Philosophers problem was first introduced by Edgar Dijkstra (who, as you...

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