Data-Intensive Scalable Computing

Web search engines have become fixtures in our society, but few people realize that they are actually publicly accessible supercomputing systems, where a single query can unleash the power of several hundred processors operating on a data set of over 200 terabytes. With Internet search, computing has risen to entirely new levels of scale, especially in terms of the sizes of the data sets involved. Google and its competitors have created a new class of large-scale computer systems, which we label "Data- Intensive Scalable Computer" (DISC) systems. DISC systems differ from conventional supercomputers in their focus is on data: they acquire and maintain continually changing data sets, in addition to performing large-scale computations over the data.
DISC points the way to new ways of organizing large-scale computing systems to be more robust, scalable, and cost effective than are current high-performance computing systems.

Dr. Randal E. Bryant is Dean of the Carnegie Mellon University School of Computer Science. He received his B.S. in Applied Mathematics from the University of Michigan in 1973, and his PhD from MIT in 1981. He was on the faculty at Caltech from 1981 to 1984, and at Carnegie Mellon since 1984. His research has focused on methods for formally verifying digital hardware and some forms of software. Dr. Bryant is a fellow of the IEEE and the ACM, and a member of the National Academy of Engineering and the American Academy of Arts and Sciences. His awards include the 2007 IEEE Piore Award, the 1997 ACM Kanellakis Theory and Practice Award, and the 1989 IEEE W.R.G. Baker Prize for the best paper appearing in any IEEE publication during the preceding year.