A team led by Thomas Schulthess of the U.S. Department of Energy's Oak Ridge National Laboratory received the prestigious 2008 Association for Computing Machinery (ACM) Gordon Bell Prize Thursday after attaining the fastest performance ever in a scientific supercomputing application.
Schulthess is group leader of ORNL's Computational Materials Science Group and recently accepted a position as director of the Swiss National Supercomputing Center at Manno, an institution of ETH Zurich. He and colleagues Thomas Maier, Michael Summers and Gonzalo Alvarez, all of ORNL, achieved 1.352 quadrillion calculations a second--or 1.352 petaflops--on ORNL's Cray XT Jaguar supercomputer with a simulation of superconductors, or materials that conduct electricity without resistance. By modifying the algorithms and software design of its DCA++ code to maximize speed without sacrificing accuracy, the team was able to boost performance tenfold with the help of John Levesque and Jeff Larkin of Cray Inc.
Jaguar was recently upgraded to a peak performance of 1.64 petaflops, making it the world's first petaflop system dedicated to open research. The team's simulation made efficient use of 150,000 of Jaguar's 180,000-plus processing cores to explore electrical conductance.
To put the achievement into perspective, it would take every man, woman and child on earth more than 500 years to work through as many calculations as DCA++ gets through in a single day--and that's assuming each of us worked day and night solving one calculation a second.
Researchers have known about superconductors for nearly a century and have prized these materials both for their ability to conduct electricity without resistance, or energy loss, and for their especially strong magnetic field. Superconducting materials have obvious potential application in power transmission, and superconducting magnets have found a place in hospital magnetic resonance imaging machines, particle accelerators such as Europe's Large Hadron Collider, and magnetic levitation transportation systems.
The challenge is that superconducting materials must be very, very cold. Even so-called high-temperature superconductors--discovered in the mid-1980s--must be chilled to a "transition temperature" of around ?°F before they exhibit their amazing behavior. In addition, a full scientific explanation is missing of how high-temperature superconductors work.
The team used the DCA++ application within a promising mathematical framework known as the two-dimensional Hubbard model. These simulations were the first in which it had enough computing power to move beyond ideal, perfectly ordered materials. By looking at materials with disorder--or impurities--the team is moving toward the necessarily imperfect materials found in the real world.
"The real materials are very inhomogeneous," noted team member Thomas Maier of ORNL.
Specifically, the team focused on chemical disorder in high-temperature superconductors known as cuprates--layers of copper oxide separated by layers of an insulating material. By advancing our understanding of the interplay between these imperfections and superconductivity, the work promises to help researchers push transition temperatures ever higher, possibly approaching the lofty goal of "room-temperature superconductors," or materials that exhibit this behavior without artificial cooling.
The team studied the local repulsion between electrons on the same atom. Because electrons have a negative electrical charge, they push one another away in what is known as a Coulomb repulsion. For the material to become superconducting, however, the electrons must overcome this repulsion and join into units called Cooper pairs. The team is looking to take advantage of an earlier discovery that indicates the insulating material promotes this process by drawing electrons away from the copper oxide layer.
"If you draw electrons away from the copper oxide layers, they become superconducting," Maier said. "Then the question is, what happens if you replace lanthanum with strontium, for instance. You do have different potentials, but you should also have different Coulomb repulsions on each site."
To achieve the sustained speed demonstrated in the simulation, the team made two fundamental changes to the DCA++ application, allowing it to delay memory-intensive operations and use a less memory-intensive data form. Both of these techniques exploit the fact that DCA++ uses the Monte Carlo approach, which relies on random sampling of a variable to explore systems such as the two-dimensional Hubbard model that do not lend themselves to an exact solution.
Between the two approaches, the team was able to boost the speed of the application by a factor of about 10, according to team member Marcus Eisenbach of ORNL's National Center for Computational Sciences. This increase in speed allows the team to look at a wider variety of materials in increased detail.