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Distributed Computing with Python

Distributed Computing with Python

By : Pierfederici
4.3 (3)
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Distributed Computing with Python

Distributed Computing with Python

4.3 (3)
By: Pierfederici

Overview of this book

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.
Table of Contents (10 chapters)
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9
Index

Common problems – the availability of hardware resources


The hardware resources that our application needs might or might not be available at any given point in time. Moreover, even if some resources were to be available at some point in time, nothing guarantees that they will stay available for much longer. A problem we can face related to this is network glitches, which are quite common in many environments (especially for mobile apps) and which, for most practical purposes, are indistinguishable from machine or application crashes.

Applications using a distributed computing framework or job scheduler can often rely on the framework itself to handle at least some common failure scenarios. Some job schedulers will even resubmit our jobs in case of errors or sudden machine unavailability.

Complex applications, however, might need to implement their own strategies to deal with hardware failures. In some cases, the best strategy is to simply restart the application when the necessary resources...

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