Distributed Computing with Python

Packt Publishing Limited
  • 1. Auflage
  • |
  • erschienen am 12. April 2016
  • |
  • 170 Seiten
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
978-1-78588-704-8 (ISBN)
Harness the power of multiple computers using Python through this fast-paced informative guideAbout This BookYou'll learn to write data processing programs in Python that are highly available, reliable, and fault tolerantMake use of Amazon Web Services along with Python to establish a powerful remote computation systemTrain 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 LearnGet an introduction to parallel and distributed computingSee synchronous and asynchronous programmingExplore parallelism in PythonDistributed application with CeleryPython in the CloudPython on an HPC clusterTest 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.
  • Englisch
  • Birmingham
  • |
  • Großbritannien
978-1-78588-704-8 (9781785887048)
1785887041 (1785887041)
weitere Ausgaben werden ermittelt
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.
  • 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 Celery
  • Testing 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 Applications
  • The 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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