1.5 My Biases

The material covered in this book reflects three of my biases, of which you should be aware. I have tried to write a book to help people get started with clusters. As such, I have focused primarily on mainstream, high-performance computing, using open source software. Let me explain why.

First, there are many approaches and applications for clusters. I do not believe that it is feasible for any book to address them all, even if a less-than-exhaustive approach is used. In selecting material for this book, I have tried to use the approaches and software that are the most useful for the largest number of people. I feel that it is better to cover a limited number of approaches than to try to say too much and risk losing focus. However, I have tried to justify my decisions and point out options along the way so that if your needs don't match my assumptions, you'll at least have an idea where to start looking.

Second, in keeping with my goal of addressing mainstream applications of clusters, the book primarily focuses on high-performance computing. This is the application from which clusters grew and remains one of their dominant uses. Since high availability and load balancing tend to be used with mission-critical applications, they are beyond the scope of a book focusing on getting started with clusters. You really should have some basic experience with generic clusters before moving on to such mission-critical applications. And, of course, improved performance lies at the core of all the other uses for clusters.

Finally, I have focused on open source software. There are a number of proprietary solutions available, some of which are excellent. But given the choice between comparable open source software and proprietary software, my preference is for open source. For clustering, I believe that high-quality, robust open source software is readily available and that there is little justification for considering proprietary software for most applications.

While I'll cover the basics of clusters here, you would do well to study the specifics of clusters that closely match your applications as well. There are a number of well-known clusters that have been described in detail. A prime example is Google, with literally tens of thousands of computers. Others include clusters at Fermilab, Argonne National Laboratory (Chiba City cluster), and Oak Ridge National Laboratory. Studying the architecture of clusters similar to what you want to build should provide additional insight. Hopefully, this book will leave you well prepared to do just that.

One last comment-if you keep reading, I promise not to mention horses again.