Book Image

Python High Performance, Second Edition - Second Edition

By : Dr. Gabriele Lanaro
Book Image

Python High Performance, Second Edition - Second Edition

By: Dr. Gabriele Lanaro

Overview of this book

Python is a versatile language that has found applications in many industries. The clean syntax, rich standard library, and vast selection of third-party libraries make Python a wildly popular language. Python High Performance is a practical guide that shows how to leverage the power of both native and third-party Python libraries to build robust applications. The book explains how to use various profilers to find performance bottlenecks and apply the correct algorithm to fix them. The reader will learn how to effectively use NumPy and Cython to speed up numerical code. The book explains concepts of concurrent programming and how to implement robust and responsive applications using Reactive programming. Readers will learn 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. By the end of the book, readers will have learned to achieve performance and scale from their Python applications.
Table of Contents (10 chapters)

Summary

Cython is a tool that bridges the convenience of Python with the speed of C. Compared to C bindings, Cython programs are much easier to maintain and debug, thanks to the tight integration and compatibility with Python and the availability of excellent tools.

In this chapter, we introduced the basics of the Cython language and how to make our programs faster by adding static types to our variables and functions. We also learned how to work with C arrays, NumPy arrays, and memoryviews.

We optimized our particle simulator by rewriting the critical evolve function, obtaining a tremendous speed gain. Finally, we learned how to use the annotated view to spot hard-to-find interpreter related calls and how to enable cProfile support in Cython. Also, we learned how to take advantage of the Jupyter notebook for integrated profiling and analysis of Cython codes.

In the next chapter, we will explore other tools that...