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Mastering Concurrency in Python

Mastering Concurrency in Python

By : Quan Nguyen
1 (1)
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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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Summary

Amdahl's Law offers us a method to estimate the potential speedup in execution time of a task that we can expect from a system when its resources are improved. It illustrates that, as the resources of the system are improved, so is the execution time. However, the differential speedup when incrementing the resources strictly decreases, and the throughput speedup is limited by the sequential overhead of its program.

You also saw that in specific situations (namely, when only the number of processors increases), Amdahl's Law resembles the law of diminishing returns. Specifically, as the number of processors increases, the efficiency gained through the improvement decreases, and the speedup curve flattens out.

Lastly, this chapter showed that improvement through concurrency and parallelism is not always desirable, and detailed specifications are needed for an effective...

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