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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 – software environments


Another common challenge is making sure that the software installed on all the various machines we are ever going to use is consistent and consistently upgraded.

Unfortunately, it is frustratingly common to spend hours debugging a distributed application only to discover that for some unknown and seemingly impossible reason, some computers had an old version of the code and/or its dependencies. Sometimes, we might even find the code to have disappeared completely.

The reasons for these discrepancies can be many: from a mount point that failed, to a bug in our deployment procedures, to a simple human mistake.

A common approach, especially in the HPC world, is to always create a self-contained environment for our code before launching the application itself. Some projects go as far as preferring static linking of all dependencies to avoid having the runtime pick up the wrong version of a dynamic library.

This approach works well if the application runtime...

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