Predicting the future, shrinking to atomic dimensions, traveling back to the birth of the universe: these seemingly impossible feats are all in a day¿s work for a particular breed of scientist called computer modelers. When traditional research is too expensive, dangerous or time-consuming to physically conduct, a computer model can stand in, examining everything from a particular policy¿s effect on the environment to the many possible protein structures resulting from a single DNA sequence. The process of model creation can be excruciatingly slow, however, taking many years.

To address this problem, the U.S. Department of Energy (DOE) has created a new program called Scientific Discovery Through Advanced Computing (SciDAC), which encourages and funds close collaborations between the people who program the computer codes and those who apply and tweak the resulting software. A five-year program, SciDAC already funds 51 projects with a total of $57 million per year.

A Change for the Better

One of the research fields SciDAC has particularly prioritized is climate change. Agriculture, ecosystems and ocean levels are all inextricably linked to the atmosphere--and understanding these processes is obviously critical to studies of climate change and the formation of public policies that are shaped by those studies. Pre-SciDAC, however, the lack of strong bonds among researchers in such disparate areas had made collaboration difficult, hampering and slowing the development and refinement of models. "The primary benefits of SciDAC fall into two categories," explains Dave Bader, acting director. "First, computer simulation will be available as a research tool for a much larger community of researchers than is currently possible. Scientists will no longer be forced to develop their own models to use simulations as a research tool. Second, SciDAC will demonstrate that computer simulation can produce breakthrough basic scientific discoveries."

After two years of preliminary planning and design, SciDAC formally got under way in January 2002. A SciDAC-sponsored conference in Reston, Va., brought applied mathematicians and computational scientists--the code writers and hardware developers--together with "applications" people--the folks who continually use and improve computer models. "The two groups took turns setting up their posters," says participant Dave Randall, professor of atmospheric science at Colorado State University. "While the applications posters were on display, the mathematics and computational scientists people made the rounds looking for ¿matches¿ and vice versa."

After the meeting, 13 interdisciplinary climate groups formed with members from 20 national laboratories and universities. Though each scientist had worked independently for years with some outside partnership, none had ever before collaborated so closely. To further enhance the collaboration, SciDAC has also launched a new program called Science Grid. It links researchers and research groups electronically and allows them to share information, manage gigantic data sets, share computing resources, and communicate with each other more quickly and easily in a secure environment.

Improving Models

Randall heads one research group that's benefiting from SciDAC's funding and encouragement. His team seeks to address a critical problem in modeling: coupling, or bringing together disparate models to create a more complete picture of an environmental system. For example, modeling researchers must bolt together the vital elements of atmosphere and ocean to study processes like the water cycle or heat exchange. Because of the models' divergent structures, however, making them work together is as challenging as plugging an American appliance into a European socket: it can be done, but it requires a lot of effort and forcing--and, in the case of the model, serious computing power and processing time. Randall's team is addressing this problem by creating an ocean model constructed with a grid identical to that of the atmosphere model. "Using atmosphere and ocean grids that have the same shape is in a sense an obvious thing to do, but it is not being done in all models," Randall explains. SciDAC team members are helping to create faster, more efficient codes and hardware for Randall's model, which should speed its completion.

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