Since most of the computers we buy today (2016) are multicore and often even both multiprocessor and multicore, any distributed application that we will write is likely to run on such systems. This brings us to being able to exploit both distributed computing and parallel computing techniques in our code. This mixed distributed-parallel paradigm is the de-facto standard nowadays when writing applications distributed over the network. As usual, reality is rarely binary.
Distributed Computing with Python
Distributed Computing with Python
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.
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Distributed Computing with Python
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Table of Contents (15 chapters)
Distributed Computing with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
An Introduction to Parallel and Distributed Computing
Asynchronous Programming
Parallelism in Python
Distributed Applications – with Celery
Python in the Cloud
Python on an HPC Cluster
Testing and Debugging Distributed Applications
The Road Ahead
Index
Customer Reviews