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

Testing our application

Testing our application is a relatively simple task.

Let's start with compiling the code. All the following commands should be run from inside the following directory:


Inside of this apps folder create a new directory called wordcount_classes; this will be where we store our outputted JAR file. You can use the following mkdir command:

mkdir wordcount_classes

The tool we use for compiling Java code is javac. This handy command-line utility will take a number of inputs and compile our Java code, outputting an application we can run.

The javac command takes several flags and parameters. The first flag is –classpath. This flag points to the Hadoop core JAR file. This is required at the compilation time in order to generate our application. Following this we include the –d flag. The –d flag sets the destination directory for our class file.

Finally we reference the Java file we wish to compile, in this case, as follows:

javac -classpath /home...