Distributed computing with Python : harness the power of multiple computers using Python through this fast-paced informative guide / Francesco Pierfederici.

Description based on online resource; title from cover (Safari, viewed April 21, 2016).

Includes index.

Annotation Harness the power of multiple computers using Python through this fast-paced informative guideAbout This Book 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 applicationsWho This Book Is ForThis book is for Python developers who have developed Python programs for data processing and now want to learn how to write fast, efficient programs that perform CPU-intensive data processing tasks.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 applicationsIn DetailCPU-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.Style and ApproachThis example based, step-by-step guide will show you how to make the best of your hardware configuration using Python for distributing applications.

Cover; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: An Introduction to Parallel and Distributed Computing; Parallel computing; Distributed computing; Shared memory versus distributed memory; Amdahl's law; The mixed paradigm; Summary; Chapter 2: Asynchronous Programming; Coroutines; An asynchronous example; Summary; Chapter 3: Parallelism in Python; Multiple threads; Multiple processes; Multiprocess queues; Closing thoughts; Summary; Chapter 4: Distributed Applications -- with Celery; Establishing a multimachine environment

Installing CeleryTesting the installation; A tour of Celery; More complex Celery applications; Celery in production; Celery alternatives -- Python-RQ; Celery alternatives -- Pyro; Summary; Chapter 5: Python in the Cloud; Cloud computing and AWS; Creating an AWS account; Creating an EC2 instance; Storing data in Amazon S3; Amazon elastic beanstalk; Creating a private cloud; Summary; Chapter 6: Python on an HPC Cluster; Your typical HPC cluster; Job schedulers; Running a Python job using HTCondor; Running a Python job using PBS; Debugging; Summary

Chapter 7: Testing and Debugging Distributed ApplicationsThe big picture; Common problems -- clocks and time; Common problems -- software environments; Common problems -- permissions and environments; Common problems -- the availability of hardware resources; Challenges -- the development environment; A useful strategy -- logging everything; A useful strategy -- simulating components; Summary; Chapter 8: The Road Ahead; The first two chapters; The tools; The cloud and the HPC world; Debugging and monitoring; Where to go next; Index

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