Book Image

Raspberry Pi Super Cluster

By : Andrew K. Dennis
Book Image

Raspberry Pi Super Cluster

By: Andrew K. Dennis

Overview of this book

A cluster is a type of parallel/distributed processing system which consists of a collection of interconnected stand-alone computers cooperatively working together. Using Raspberry Pi computers, you can build a two-node parallel computing cluster which enhances performance and availability. This practical, example-oriented guide will teach you how to set up the hardware and operating systems of multiple Raspberry Pi computers to create your own cluster. It will then navigate you through how to install the necessary software to write your own programs such as Hadoop and MPICH before moving on to cover topics such as MapReduce. Throughout this book, you will explore the technology with the help of practical examples and tutorials to help you learn quickly and efficiently. Starting from a pile of hardware, with this book, you will be guided through exciting tutorials that will help you turn your hardware into your own super-computing cluster. You'll start out by learning how to set up your Raspberry Pi cluster's hardware. Following this, you will be taken through how to install the operating system, and you will also be given a taste of what parallel computing is about. With your Raspberry Pi cluster successfully set up, you will then install software such as MPI and Hadoop. Having reviewed some examples and written some programs that explore these two technologies, you will then wrap up with some fun ancillary projects. Finally, you will be provided with useful links to help take your projects to the next step.
Table of Contents (15 chapters)
Raspberry Pi Super Cluster
About the Author
About the Reviewers


MapReduce is a programming approach that allows systems to process large datasets in parallel.

The key concept is that of using two functions, Map and Reduce, that are combined to produce a desired result.

Its genesis can be found in functional programming and has been available in languages such as LISP for several decades. Google has been a driver for bringing it out of the functional programming paradigm into the OOP (Object Orientated Programming) world. Its contributions include publishing a seminal paper on the subject in 2004, and being granted a patent on the technology.

So how does MapReduce work? The Map function takes a data set and then operates on the data, returning another data set as an output. This output is then fed to the Reduce function, which subsequently operates on the data set once again and returns a smaller data set as an output.

So let's look at an example of how the Map function operates. The pseudo code function CtoF in the following code takes a list...