Book Image

Advanced Python Programming

By : Dr. Gabriele Lanaro, Quan Nguyen, Sakis Kasampalis
Book Image

Advanced Python Programming

By: Dr. Gabriele Lanaro, Quan Nguyen, Sakis Kasampalis

Overview of this book

This Learning Path shows you how to leverage the power of both native and third-party Python libraries for building robust and responsive applications. You will learn about profilers and reactive programming, concurrency and parallelism, as well as tools for making your apps quick and efficient. You will discover how to write code for parallel architectures using TensorFlow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. With the knowledge of how Python design patterns work, you will be able to clone objects, secure interfaces, dynamically choose algorithms, and accomplish much more in high performance computing. By the end of this Learning Path, you will have the skills and confidence to build engaging models that quickly offer efficient solutions to your problems. This Learning Path includes content from the following Packt products: • Python High Performance - Second Edition by Gabriele Lanaro • Mastering Concurrency in Python by Quan Nguyen • Mastering Python Design Patterns by Sakis Kasampalis
Table of Contents (41 chapters)
Title Page
Copyright
About Packt
Contributors
Preface
Index

Not everything should be made concurrent


Not all programs are created equal: some can be made parallel or concurrent relatively easily, while others are inherently sequential, and thus cannot be executed concurrently, or in parallel. An extreme example of the former is embarrassingly parallel programs, which can be divided into different parallel tasks, between which there is little or no dependency or need for communication.

Embarrassingly parallel

A common example of an embarrassingly parallel program is the 3D video rendering handled by a graphics processing unit, where each frame or pixel can be processed with no interdependency. Password cracking is another embarrassingly parallel task that can easily be distributed on CPU cores. In a later chapter, we will tackle a number of similar problems, including image processing and web scraping, which can be made concurrent/parallel intuitively, resulting in significantly improved execution times.

Inherently sequential

In opposition to embarrassingly...