Distributed Computing with PythonPackt Publishing Ltd, 12/04/2016 - 170 páginas Harness the power of multiple computers using Python through this fast-paced informative guide About 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. |
Índice
| 1 | |
| 13 | |
Parallelism in Python | 29 |
Distributed Applications with Celery | 47 |
Python in the Cloud | 79 |
Python on an HPC Cluster | 107 |
Testing and Debugging Distributed Applications | 131 |
The Road Ahead | 145 |
| 153 | |
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Palavras e frases frequentes
able algorithm architecture asynchronous Celery chapter cloud cluster command common complex computing configuration connect coroutine create default dependencies directive distributed applications distributed computing environment example exception execution function going happens hardware host HOST3 HTCondor implement import install instance interesting job scheduler keep look machines means memory method module monitoring multiple nodes object Once operations option packages pair parallel performance preceding problem processes processors programming provider Pyro Python queue reason remote requests requirements result screenshot script server shown shows simple single standard start step submit sure task terminal things threads True typically usually virtual worker write