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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

Formula and interpretation

Before we get into the formula for Amdahl's Law and its implications, let's explore the concept of speedup, through some brief analysis. Let's assume that there are N workers working on a given job that is fully parallelizable—that is, the job can be perfectly divided into N equal sections. This means that N workers working together to complete the job will only take 1/N of the time it takes one worker to complete the same job.

However, most computer programs are not 100% parallelizable: some parts of a program might be inherently sequential, while others are broken up into parallel tasks.

The formula for Amdahl's Law

Now, let B denote the fraction of the program that is...

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