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

Author: Francesco Pierfederici
Publisher: Birmingham, UK : Packt Publishing, 2016.
Series: Community experience distilled.
Edition/Format:   eBook : Document : EnglishView all editions and formats
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Genre/Form: Electronic books
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Francesco Pierfederici
ISBN: 9781785887048 1785887041 1785889699 9781785889691
OCLC Number: 947111678
Notes: Includes index.
Description: 1 online resource (1 volume) : illustrations.
Series Title: Community experience distilled.
Responsibility: Francesco Pierfederici.

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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.

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