Distributed Computing with Python

Book Description

Key Features

• You’ll learn to write data processing programs in Python that are highly available, reliable, and fault tolerant
• Make use of Amazon Web Services along with Python to establish a powerful remote computation system
• Train Python to handle data-intensive and resource hungry applications

Book Description

CPU-intensive data processing tasks have become crucial considering the complexity of the various big data applications that are used today. Reducing the CPU utilization per process is very important to improve the overall speed of applications.

This book will teach you how to perform parallel execution of computations by distributing them across multiple processors in a single machine, thus improving the overall performance of a big data processing task. We will cover synchronous and asynchronous models, shared memory and file systems, communication between various processes, synchronization, and more.

What You Will Learn

• Get an introduction to parallel and distributed computing
• See synchronous and asynchronous programming
• Explore parallelism in Python
• Distributed application with Celery
• Python in the Cloud
• Python on an HPC cluster
• Test and debug distributed applications

About the Author

Francesco Pierfederici is a software engineer who loves Python. He has been working in the fields of astronomy, biology, and numerical weather forecasting for the last 20 years.

He has built large distributed systems that make use of tens of thousands of cores at a time and run on some of the fastest supercomputers in the world. He has also written a lot of applications of dubious usefulness but that are great fun. Mostly, he just likes to build things.

Table of Contents

Chapter 1. An Introduction to Parallel and Distributed Computing
Chapter 2. Asynchronous Programming
Chapter 3. Parallelism in Python
Chapter 4. Distributed Applications – with Celery
Chapter 5. Python in the Cloud
Chapter 6. Python on an HPC Cluster
Chapter 7. Testing and Debugging Distributed Applications
Chapter 8. The Road Ahead

中文：

书名：使用Python的分布式计算

主要特点

• 您将学习用高可用性、可靠性和容错性的Python语言编写数据处理程序
• 利用Amazon Web Services和Python建立功能强大的远程计算系统
• 培训Python以处理数据密集型和资源密集型应用程序

图书描述

考虑到当今使用的各种大数据应用程序的复杂性，CPU密集型数据处理任务变得至关重要。降低每个进程的CPU使用率对于提高应用程序的整体速度非常重要。

这本书将教你如何通过将计算分布在一台机器的多个处理器上来执行并行计算，从而提高大数据处理任务的整体性能。我们将讨论同步和异步模型、共享内存和文件系统、各种进程之间的通信、同步等。

What You Will Learn

• 了解并行和分布式计算
• See synchronous and asynchronous programming
• 探索Python中的并行性
• 基于芹菜的分布式应用
• 云端的巨蟒
• HPC群集上的Python
• Test and debug distributed applications

关于作者

弗朗西斯科·皮尔弗里德里奇 是一位热爱Python的软件工程师。在过去的20年里，他一直在天文学、生物学和数值天气预报领域工作。

他构建了大型分布式系统，一次使用数万个核心，并在世界上一些速度最快的超级计算机上运行。他还写了很多用处不明的应用程序，但这些应用程序非常有趣。大多数情况下，他只是喜欢建造东西。

目录表

第1章并行和分布式计算简介
第2章.异步编程
第3章.Python中的并行性
Chapter 4. Distributed Applications – with Celery
第5章：云中的Python
第6章.HPC群集上的Python
第7章.测试和调试分布式应用程序
第八章前面的路