High performance Linux clusters - with OSCAR, Rocks, openMosix, and MPI

Computing speed isn't just a convenience. Faster computers allow us to solve larger problems, and to find solutions more quickly, with greater accuracy, and at a lower cost. All this adds up to a competitive advantage. In the sciences, this may mean the difference between being the first to publish and not publishing. In industry, it may determine who's first to the patent office. Traditional high-performance clusters have proved their worth in a variety of uses—from predicting the weather to industrial design, from molecular dynamics to astronomical modeling. High-performance computing (HPC) has created a new approach to science—modeling is now a viable and respected alternative to the more traditional experiential and theoretical approaches. Clusters are also playing a greater role in business. High performance is a key issue in data mining or in image rendering. Advances in clustering technology have led to high-availability and load-balancing clusters. Clustering is now used for mission-critical applications such as web and FTP servers. For example, Google uses an ever-growing cluster composed of tens of thousands of computers. Because of the expanding role that clusters are playing in distributed computing, it is worth considering this question briefly. There is a great deal of ambiguity, and the terms used to describe clusters and distributed computing are often used inconsistently. This chapter doesn't provide a detailed taxonomy—it doesn't include a discussion of Flynn's taxonomy or of cluster topologies. This has been done quite well a number of times and too much of it would be irrelevant to the purpose of this book. However, this chapter does try to explain the language used. If you need more general information, see the Appendix A for other sources. particularly readable introduction. When computing, there are three basic approaches to improving performance—use a better algorithm, use a faster computer, or divide the calculation among multiple computers. A very common analogy is that of a horse-drawn cart. You can lighten the load, you can get a bigger horse, or you can get a team of horses. (We'll ignore the option of going into therapy and learning to live with what you have.) Let's look briefly at each of these approaches.