Mastering Concurrency in Python: Create faster programs using concurrency, asynchronous, multithreading, and parallel programming

by Quan Nguyen

Overview

Python is one of the most popular programming languages, with numerous libraries and frameworks that facilitate high-performance computing. Concurrency and parallelism in Python are essential when it comes to multiprocessing and multithreading; they behave differently, but their common aim is to reduce the execution time. This book serves as a comprehensive introduction to various advanced concepts in concurrent engineering and programming.
Mastering Concurrency in Python starts by introducing the concepts and principles in concurrency, right from Amdahl's Law to multithreading programming, followed by elucidating multiprocessing programming, web scraping, and asynchronous I/O, together with common problems that engineers and programmers face in concurrent programming. Next, the book covers a number of advanced concepts in Python concurrency and how they interact with the Python ecosystem, including the Global Interpreter Lock (GIL). Finally, you'll learn how to solve real-world concurrency problems through examples.
By the end of the book, you will have gained extensive theoretical knowledge of concurrency and the ways in which concurrency is supported by the Python language

Product Details

ISBN-13:	9781789341362
Publisher:	Packt Publishing
Publication date:	11/27/2018
Sold by:	Barnes & Noble
Format:	eBook
Pages:	446
File size:	14 MB
Note:	This product may take a few minutes to download.

About the Author

Quan Nguyen is a Python enthusiast and data scientist. He is currently a data analysis engineer at Micron Technology, Inc. With a strong background in mathematics and statistics, Quan is interested in the fields of scientific computing and machine learning. With data analysis being his focus, Quan also enjoys incorporating technology automation into everyday tasks through programming. Quan's passion for Python programming has led him to be heavily involved in the Python community. He started as a primary contributor for the book Python for Scientists and Engineers and various open source projects on GitHub. Quan is also a writer for the Python Software Foundation and an occasional content contributor for DataScience (part of Oracle).

Table of Contents

Table of Contents

1. Concurrent and Parallel Programming - An Advanced Introduction
2. Amdahl’s Law
3. Working with Threads in Python
4. Using the ‘with’ Statement in Threads
5. Concurrent Web Scraping
6. Working with Processes in Python
7. The Reduction Operation in Processes
8. Concurrent Image Processing
9. Introduction to Asynchronous I/O
10. Asyncio: Pros and Cons
11. TCP with Asyncio
12. Deadlock
13. Starvation
14. Race Conditions
15. The Global Interpreter Lock
16. Designing Lock-Free and Lock-Based Concurrent Data Structures
17. Memory Models and Operations on Atomic Types
18. Building a Server from Scratch
19. Testing, Debugging, and Scheduling Concurrent Applications