Pounding out atomic nuclei

March 2011
Filed under: Argonne

“This algorithm exploits the mathematical structures of the data-fitting problem and can be run on very general computer simulations,” Wild says.

In the case of DFT, scientists can now use more parameters and optimize their functionals faster and more accurately. “This accelerates the discovery process; what used to take them a year can now be done in two hours,” Wild says.

In part, that comes from the algorithm. “We have to do fewer nuclear simulations because the mathematical algorithm quickly finds better parameter values,” Wild explains. In addition, POUNDERS takes advantage of highly parallel computer architecture. For instance, Wild and his colleagues often run DFT on Argonne’s Fusion cluster, which consists of 2,560 Intel CPU cores and 12 terabytes of memory. By using only about 600 of Fusion’s cores, says Wild, “we can simulate key properties of six dozen nuclei in a minute.”

Getting to that advance, however, took teamwork. As Wild says, “A considerable challenge is bridging the gap between mathematical theory and the actual implementation that works for the physicists.”

As that work continues, Wild asks: “Can you build a single description that predicts properties of nuclei across the entire range of possibilities?” The answer to this question, he says, is “only computationally tractable with the right optimization tools.”

As Wild and his colleagues reported in the August 2010 Physical Review C, they have used POUNDERS to crank out data for 72 nuclei orders of magnitude faster than possible with previous techniques. That work relied on 5,616 cores of a Cray supercomputer called Franklin at the National Energy Research Scientific Computing Center.

Increasing accuracy


Beyond simulating nuclei, this new approach provides measurements of accuracy. “The Argonne team provides us with correlations and error bars on the observables,” Nazarewicz says. As a result, POUNDERS can be used to simulate experiments. “What will happen if I change a particular data point based on the mass? Will this impact the final parameters very much? This will tell us how robust the final fit is.”

The uncertainty in the results also can be used to validate simulations. Nazarewicz explains: “I’d like to use this functional to calculate properties that would be useful to science or, say, for modeling nuclear reactors. I’d like to go to regions of nuclei that have never been, and probably will never be, measured. We’d like to extrapolate.”

In some ways, the extrapolation is underway.

“When we started this project,” Nazarewicz says, “I had limited imagination about what this partnership between computer scientists and applied mathematician can offer.” He quickly adds: “I was very wrong, because it brought us into a completely new world. The resulting progress in science has been transformational.”

Now researchers can simulate more nuclei than ever, faster and more accurately. In addition, the computational power of POUNDERS lets scientists explore nuclear interactions to unveil new aspects of basic science or create new applications.

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About the Author

Mike May has worked as a full-time freelancer since 1998, covering topics ranging from biotech and drug discovery to information technology and optics. Before that, he worked for seven years as an associate editor at American Scientist. He earned an M.S. in biological engineering from the University of Connecticut and a Ph.D. in neurobiology and behavior from Cornell University.

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