Book Image

Mastering Python 2E - Second Edition

By : Rick van Hattem
5 (1)
Book Image

Mastering Python 2E - Second Edition

5 (1)
By: Rick van Hattem

Overview of this book

Even if you find writing Python code easy, writing code that is efficient, maintainable, and reusable is not so straightforward. Many of Python’s capabilities are underutilized even by more experienced programmers. Mastering Python, Second Edition, is an authoritative guide to understanding advanced Python programming so you can write the highest quality code. This new edition has been extensively revised and updated with exercises, four new chapters and updates up to Python 3.10. Revisit important basics, including Pythonic style and syntax and functional programming. Avoid common mistakes made by programmers of all experience levels. Make smart decisions about the best testing and debugging tools to use, optimize your code’s performance across multiple machines and Python versions, and deploy often-forgotten Python features to your advantage. Get fully up to speed with asyncio and stretch the language even further by accessing C functions with simple Python calls. Finally, turn your new-and-improved code into packages and share them with the wider Python community. If you are a Python programmer wanting to improve your code quality and readability, this Python book will make you confident in writing high-quality scripts and taking on bigger challenges
Table of Contents (21 chapters)
19
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20
Index

Hyper-threading versus physical CPU cores

Hyper-threading is a technology that offers extra virtual CPU cores to your physical cores. The idea is that, because these virtual CPU cores have separate caches and other resources, you can more efficiently switch between multiple tasks. If you task-switch between two heavy processes, the CPU won’t have to unload/reload all caches. When it comes to actual CPU instruction processing, however, it will not help you.

When you truly maximize CPU usage, it is generally better to only use the physical processor count. To demonstrate how this affects the performance, we will run a simple test with several process counts. Since my processor has 8 cores (16 if you include hyper-threading), we will run it with 1, 2, 4, 8, 16, and 32 processes to demonstrate how it affects the performance:

import timeit
import multiprocessing

def busy_wait(n):
    while n > 0:
        n -= 1

def benchmark(n, processes, tasks):
    with multiprocessing...