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Advanced Python Programming

You're reading from   Advanced Python Programming Build high performance, concurrent, and multi-threaded apps with Python using proven design patterns

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Product type Course
Published in Feb 2019
Publisher Packt
ISBN-13 9781838551216
Length 672 pages
Edition 1st Edition
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Authors (3):
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Quan Nguyen Quan Nguyen
Author Profile Icon Quan Nguyen
Quan Nguyen
Sakis Kasampalis Sakis Kasampalis
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Sakis Kasampalis
Dr. Gabriele Lanaro Dr. Gabriele Lanaro
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Dr. Gabriele Lanaro
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Table of Contents (41) Chapters Close

Title Page
Copyright
About Packt
Contributors
Preface
Benchmarking and Profiling FREE CHAPTER Pure Python Optimizations Fast Array Operations with NumPy and Pandas C Performance with Cython Exploring Compilers Implementing Concurrency Parallel Processing Advanced Introduction to Concurrent and Parallel Programming Amdahl's Law Working with Threads in Python Using the with Statement in Threads Concurrent Web Requests Working with Processes in Python Reduction Operators in Processes Concurrent Image Processing Introduction to Asynchronous Programming Implementing Asynchronous Programming in Python Building Communication Channels with asyncio Deadlocks Starvation Race Conditions The Global Interpreter Lock The Factory Pattern The Builder Pattern Other Creational Patterns The Adapter Pattern The Decorator Pattern The Bridge Pattern The Facade Pattern Other Structural Patterns The Chain of Responsibility Pattern The Command Pattern The Observer Pattern 1. Appendix 2. Other Books You May Enjoy Index

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 strictly serial, and consider the following:

  • B * T(1) is the time it takes to execute the parts of the program that are inherently sequential.
  • T(1) - B * T(1) = (1 - B) * T(1) is the time it takes to execute the parts of the program that are parallelizable, with one processor:
    • Then, (1 - B) * T(1) ...
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