Python Parallel Programming Cookbook: Over 70 recipes to solve challenges in multithreading and distributed system with Python 3, 2nd Edition

Python Parallel Programming Cookbook: Over 70 recipes to solve challenges in multithreading and distributed system with Python 3, 2nd EditionReviews
Author: Giancarlo Zaccone
Pub Date: 2019
ISBN: 978-1789533736
Pages: 370
Language: English
Format: EPUB
Size: 10 Mb

Download

Implement effective programming techniques in Python to build scalable software that saves time and memory
Nowadays, it has become extremely important for programmers to understand the link between the software and the parallel nature of their hardware so that their programs run efficiently on computer architectures. Applications based on parallel programming are fast, robust, and easily scalable.
This updated edition features cutting-edge techniques for building effective concurrent applications in Python 3.7. The book introduces parallel programming architectures and covers the fundamental recipes for thread-based and process-based parallelism. You’ll learn about mutex, semaphores, locks, queues exploiting the threading, and multiprocessing modules, all of which are basic tools to build parallel applications. Recipes on MPI programming will help you to synchronize processes using the fundamental message passing techniques with mpi4py. Furthermore, you’ll get to grips with asynchronous programming and how to use the power of the GPU with PyCUDA and PyOpenCL frameworks. Finally, you’ll explore how to design distributed computing systems with Celery and architect Python apps on the cloud using PythonAnywhere, Docker, and serverless applications.
By the end of this book, you will be confident in building concurrent and high-performing applications in Python.
What you will learn

  • Synchronize multiple threads and processes to manage parallel tasks
  • Use message passing techniques to establish communication between processes to build parallel applications
  • Program your own GPU cards to address complex problems
  • Manage computing entities to execute distributed computational task
  • Write efficient programs by adopting the event-driven programming model
  • Explore cloud technology with Django and Google App Engine
  • Apply parallel programming techniques that can lead to performance improvements