The scientific and engineering applications that use supercomputing are diverse both in the nature of the problems and in the nature of the solutions. Most of these applications have unsatisfied computational needs. They were described in expert briefings to the committee as computing-limited at present and very much in need of 100 to 1,000 times more computing power over the next 5 to 10 years. Increased computing power would be used in a variety of ways:

• To cover larger domains, more space scales, and longer time scales;

• To solve time-critical problems (e.g., national security ones) in shorter times;

• To include more complete physics and/or biogeochemistry;

• To use more sophisticated mathematical algorithms with desirable linear scaling; and

• To add more components to models of complex systems.

Various experts made estimates of the long-range computing power needed for their disciplines in units of petaflops. Most of the applications areas discussed would require a minimum sustained performance of 10 Pflops to begin to solve the most ambitious problems and realize practical benefits. To move toward a full solution of these problems would require capabilities of 100 Pflops and beyond.

The overall computing style in important application areas appears to be evolving toward one in which community models are developed and used by large groups. The individual developers may bring diverse back-grounds and expertise to modeling a complex natural system such as the climate system or to a daunting engineering effort like the development of a fusion power generator. In addition, the applications are moving toward first-principles methods, in which basic physical and biochemical relations are used as much as possible instead of ad hoc parameterizations involving approximations and poorly known constants. Both trends will greatly increase the amount of computing power required in various applications.

A common computational characteristic is the demand for both capacity and capability. Typically, each disciplinary area does many smaller simulations and parameter studies using machine capacity prior to large simulations that require machine capability, followed by analysis studies that use capacity. Many application areas could each use at least one large computing center almost continuously to attack multiple problems in this way.

Another computational characteristic is that each application area has