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November 19, 2008
The Search for Stable Storage
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A team led by Thomas Schulthess of Oak Ridge National Laboratory (ORNL) has broken the petaflop barrier with a supercomputing application likely to accelerate the revolution in magnetic storage.
Using ORNL's upgraded Cray XT Jaguar supercomputer, the team was able to achieve a sustained performance of 1.05 quadrillion calculations a second, or 1.05 petaflops, for an application that simulates the behavior of electron systems. Jaguar itself was recently upgraded to a peak performance of 1.64 petaflops, making it the world's first petaflop system dedicated to open scientific research. The team's simulation ran on nearly 150,000 of Jaguar's 180,000-plus processing cores.
Among its benefits, the application promises to advance scientific understanding of magnetic devices such as computer hard drives. In the last couple of decades, hard drive storage capacity has grown at an extraordinary rate. The associated risk, though, is that with increasing storage density, these amazing devices tend to become less stable.
Hard drives hold information by magnetizing tiny regions of a platter, with regions magnetized in one direction counting as ones and in the opposite direction as zeroes. With the exponential growth of storage capacity, these miniscule spots have gotten progressively even smaller; and the smaller the spot, the more likely its magnetic direction is to be incorrectly and unexpectedly reversed. Since disorder at the atomic scale increases with temperature, a hard drive kept as warm as room temperature becomes increasingly susceptible to random changes -- meaning lost data -- as storage density rises.
"A big problem in magnetic recording is that as you make the bits smaller and smaller, thermal excitation will essentially randomize them and you will lose information," explained Markus Eisenbach of ORNL. "If that happens in 500 years you don't care, but if it happens tomorrow you're really unhappy."
The team's current approach differs fundamentally from earlier efforts because it is able to set aside empirical models and their attendant approximations to tackle the system through first-principles calculations. Eisenbach, who serves as the team's developer for the project, noted that this empirical approach was far too computationally intensive for earlier computer systems.
"It's the new Jaguar coming on line that makes it really feasible," he said. "If you have a classical Heisenberg model, an energy calculation takes perhaps milliseconds. For this first-principles calculation, an energy calculation takes tens of seconds. So it's orders of magnitude slower. You really need a computer of that size."
The team simulates the effect of heat on a magnetic material by combining two methods. The first -- known as locally self-consistent multiple scattering, or LSMS -- describes the journeys of scattered electrons by applying density functional theory to solve the Dirac equation, a relativistic wave equation for electron behavior. The code has a robust history, having been the first code to run at a sustained trillion calculations per second and earned its developers the prestigious Gordon Bell Prize in 1998.
The shortcoming of this approach, though, is that it is used primarily to describe a system in its ground state at a temperature of absolute zero, or nearly 460°F. In order to include the energy brought to the system by temperatures outside a laboratory freezer, the team's simulations incorporate a Monte Carlo method known as Wang-Landau, which guides the LSMS application to explore electron behavior at a variety of temperatures.
According to Eisenbach, the two methods are ideally suited to massively parallel computing systems. They scale linearly, meaning the need for computing resources grows at the same rate as the size of the system being simulated, and LSMS can be scaled to very large materials systems by assigning one atom to each processing core.
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