High Performance Computing, Computational Grid, and Numerical Libraries

Abstract

In the last 50 years, the field of scientific computing has seen rapid, sweeping changes—in vendors, in architectures, in technologies, and in the users and uses of high-performance computer systems. The evolution of performance over the same 50-year period, however, seems to have been a very steady and continuous process. Moore’s law is often cited in this context, and, in fact, a plot of the peak performance of the various computers that could be considered the “supercomputers” of their times clearly shows that this law has held for almost the entire lifespan of modern computing. On average, performance has increased every decade by about two orders of magnitude.

Two statements have been consistently true in the realm of computer science: (1) the need for computational power is always greater than what is available at any given point, and (2) to access our resources, we always want the simplest, yet the most complete and easy to use interface possible. With these conditions in mind, researchers have directed considerable attention in recent years to the area of grid computing. The ultimate goal is the ability to plug any and all of our resources into a computational grid and draw from these resources. This is analogous to the electrical power grid, much as we plug our appliances into electrical sockets today.

Advances in networking technologies will soon make it possible to use the global information infrastructure in a qualitatively different way—as a computational as well as an information resource. As described in the recent book “The Grid: Blueprint for a New Computing Infrastructure,” this “Grid” will connect the nation’s computers, databases, instruments, and people in a seamless web of computing and distributed intelligence, that can be used in an on-demand fashion as a problem-solving resource in many fields of human endeavor—and, in particular, for science and engineering.

The availability of Grid resources will give rise to dramatically new classes of applications, in which computing resources are no longer localized, but distributed, heterogeneous, and dynamic; computation is increasingly sophisticated and multidisciplinary; and computation is integrated into our daily lives, and hence subject to stricter time constraints than at present. The impact of these new applications will be pervasive, ranging from new systems for scientific inquiry, through computing support for crisis management, to the use of ambient computing to enhance personal mobile computing environments.

The goal of the Grid Application Development Software (GrADS) project is to simplify distributed heterogeneous computing in the same way that the World Wide Web simplified information sharing over the Internet. The GrADS project is exploring the scientific and technical problems that must be solved to make Grid applications development and performance tuning for real applications an everyday practice. This requires research in four key areas; each validated in a prototype infrastructure that will make programming on grids a routine task:

• 1. Grid software architectures that facilitate information flow and resource negotiation among applications, libraries, compilers, linkers, and runtime systems;

• 2. Base software technologies, such as scheduling, resource discovery, and communication, to support development and execution of performance-efficient Grid applications;

• 3. Languages, compilers, environments, and tools to support creation of applications for the Grid and solution of problems on the Grid; and

• 4. Mathematical and data structure libraries for Grid applications, including numerical methods for control of accuracy and latency tolerance.

In this talk we will explore the issues of developing a prototype system designed specifically for the use of numerical libraries in the grid setting.