Raspberry Pi and parallel computing

Having reviewed some of the key terms of High Performance Computing, it is now time to turn our attention to the Raspberry Pi and how and why we intend to implement many of the ideas explained so far.

This article assumes that you are familiar with the basics of the Raspberry Pi and how it works, and have a basic understanding of programming. Throughout this article when using the term Raspberry Pi, it will be in reference to the Model B version.

Other topics covered in this article, such as Apache Hadoop, will also be accompanied with links to information that provides a more in-depth guide to the topic at hand. Due to the Raspberry Pi's small size and low cost, it makes a good alternative to building a cluster in the cloud on Amazon, or similar providers which can be expensive or using desktop PC's.

The Raspberry Pi comes with a built-in Ethernet port, which allows you to connect it to a switch, router, or similar device. Multiple Raspberry Pi devices connected to a switch can then be formed into a cluster; this model will form the basis of our hardware configuration in the article. Unlike your laptop or PC, which may contain more than one CPU, the Raspberry Pi contains just a single ARM processor; however, multiple Raspberry Pi's combined give us more CPU's to work with.

One benefit of the Raspberry Pi is that it also uses SD cards as secondary storage, which can easily be copied, allowing you to create an image of the Raspberry Pi's operating system and then clone it for re-use on multiple machines. When starting out with the Raspberry Pi this is a useful feature.

The Model B contains two USB ports allowing us to expand the device's storage capacity (and the speed of accessing the data) by using a USB hard drive instead of the SD card.

From the perspective of writing software, the Raspberry Pi can run various versions of the Linux operating system as well as other operating systems, such as FreeBSD and the software and tools associated with development on it. This allows us to implement the types of technology found in Beowulf clusters and other parallel systems.