Book Image

Distributed Computing with Python

Book Image

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.
Table of Contents (15 chapters)
Distributed Computing with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

The first two chapters


The initial chapters of this book provided you with some of the basic theories of parallel and distributed computing. They introduced a number of important concepts such as shared memory and distributed memory architectures and their differences.

They also looked at the basic arithmetic of code speedup by parallelization in terms of Amdahl's law. The main lesson of that discussion was that after a while, the efforts put into parallelizing an existing algorithm start to outweigh the performance gains. Also as mentioned, one way to side step Amdahl's law is to increase the problem size and have the parallel parts of our code do more work with respect to the serial parts (Gustafson's law).

The other lesson from Amdahl's law is to try and keep interprocess communication within our applications as small as possible. Ideally, our processes should be completely independent from each other. Little or no interprocess crosstalk reduces code complexity as well as general overhead...