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Python High Performance, Second Edition

By : Dr. Gabriele Lanaro

4 (2)

Buy this Book

Python High Performance, Second Edition

4 (2)

By: Dr. Gabriele Lanaro

Buy this Book

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.

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

Benchmarking and Profiling

Designing your application

Writing tests and benchmarks

Better tests and benchmarks with pytest-benchmark

Finding bottlenecks with cProfile

Profile line by line with line_profiler

Optimizing our code

The dis module

Profiling memory usage with memory_profiler

Summary

Pure Python Optimizations

Useful algorithms and data structures

Caching and memoization

Comprehensions and generators

Summary

Fast Array Operations with NumPy and Pandas

Getting started with NumPy

Rewriting the particle simulator in NumPy

Reaching optimal performance with numexpr

Pandas

Summary

C Performance with Cython

Compiling Cython extensions

Adding static types

Sharing declarations

Working with arrays

Particle simulator in Cython

Profiling Cython

Using Cython with Jupyter

Summary

Exploring Compilers

Numba

The PyPy project

Other interesting projects

Summary

Implementing Concurrency

Asynchronous programming

The asyncio framework

Reactive programming

Summary

Parallel Processing

Introduction to parallel programming

Using multiple processes

Parallel Cython with OpenMP

Automatic parallelism

Summary

Distributed Processing

Introduction to distributed computing

Dask

Using PySpark

Scientific computing with mpi4py

Summary

Designing for High Performance

Choosing a suitable strategy

Organizing your source code

Isolation, virtual environments, and containers

Continuous integration

Summary

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Parallel Processing

With parallel processing by using multiple cores, you can increase the amount of calculations your program can do in a given time frame without needing a faster processor. The main idea is to divide a problem into independent subunits and use multiple cores to solve those subunits in parallel.

Parallel processing is necessary to tackle large-scale problems. Companies produce massive quantities of data every day that need to be stored in multiple computers and analyzed. Scientists and engineers run parallel code on supercomputers to simulate massive systems.

Parallel processing allows you to take advantage of multicore CPUs as well as GPUs that work extremely well with highly parallel problems. In this chapter, we will cover the following topics:

A brief introduction to the fundamentals of parallel processing
Illustrating how to parallelize simple problems with the multiprocessing Python library...