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Mastering Concurrency in Python

Mastering Concurrency in Python

By : Quan Nguyen
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Mastering Concurrency in Python

Mastering Concurrency in Python

1 (1)
By: Quan Nguyen

Overview of this book

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
Table of Contents (22 chapters)
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Amdahl's Law's relationship to the law of diminishing returns

Amdahl's Law is often conflated with the law of diminishing returns, which is a rather popular concept in economics. However, the law of diminishing returns is only a special case of applying Amdahl's Law, depending on the order of improvement. If the order of separate tasks in the program is chosen to be improved in an optimal way, a monotonically decreasing improvement in execution time will be observed, demonstrating diminishing returns. An optimal method indicates first applying those improvements that will result in the greatest speedups, and leaving those improvements yielding smaller speedups for later.

Now, if we were to reverse this sequence for choosing resources, in which we improve less optimal components of our program before more optimal components, the speedup achieved through the...

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