that can be usefully applied to a problem. As the committee shows in Chapter 4, many disciplines have a good understanding of how they would exploit supercomputers that are many orders of magnitude more powerful than the ones they currently use; they have a good understanding of how science and engineering will benefit from improvements in supercomputing performance in the years and decades to come.

One of the principal ways to increase the amount of computing achievable in a given period of time is to use parallelism—doing multiple coordinated computations at the same time. Some problems, such as searches for patterns in data, can distribute the computational workload easily. The problem can be broken down into subproblems that can be solved independently on a diverse collection of processors that are intermittently available and that are connected by a low-speed network such as the Internet.4 Some problems necessarily distribute the work over a high-speed computational grid5 in order to access unique resources such as very large data repositories or real-time observational facilities. However, many important problems, such as the modeling of fluid flows, cannot be so easily decomposed or widely distributed. While the solution of such problems can be accelerated through the use of parallelism, dependencies among the parallel subproblems necessitate frequent exchanges of data and partial results, thus requiring significantly better communication (both higher bandwidth and lower latency) between processors and data storage than can be provided by a computational grid. Both computational grids and supercomputers hosted in one machine room are components of a cyberinfrastructure, defined in a recent NSF report as “the infrastructure based upon distributed computer, information and communication technology.”6 This report focuses mostly on systems hosted in one machine room (such systems often require a large, dedicated room). To maintain focus, it does not address networking except to note its importance. Also, the report does not address special-purpose hardware accelerators. Special-purpose hardware has always played an important but

4  

A good example is SETI@home: The Search for Extraterrestrial Intelligence, <http://setiathome.ssl.berkeley.edu>.

5  

A computational grid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities (I. Foster and C. Kesselman, 2003, The Grid 2: Blueprint for a New Computing Infrastructure, 2nd ed., San Francisco, Calif.: Morgan Kaufman).

6  

NSF. 2003. Revolutionizing Science and Engineering Through Cyberinfrastructure. NSF Blue-Ribbon Advisory Panel on Cyberinfrastructure.



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