The world’s largest supercomputer, dubbed Roadrunner, has gone online, and is starting with a terribly simple little task – modelling the composition of the universe.
The model is one of the largest simulations of the distribution of matter in the universe, and aims to look at galaxy-scale mass concentrations above and beyond quantities seen in state-of-the-art sky surveys.
Understanding dark energy is the number one issue in explaining the universe, according to Salman Habib of Los Alamos Laboratory.
“Because the universe is expanding and at the same time accelerating, either there is a huge gap in our understanding of physics, or there is a strange new form of matter that dominates the universe – ‘dark energy’ – making up about 70 percent of it,” said Habib. “In addition, there is five times more of an unknown ‘dark matter’ than there is ordinary matter in the universe, and we know it’s there from many different observations; most spectacularly, we’ve seen it bend light in pictures from the Hubble Space Telescope, but its origin is also not understood.”
Even though it’s looking at only a small segment of the “accessible” universe, Habib’s “Roadrunner Universe” model requires a petascale computer because, like the universe, it’s mind-bendingly large. The model’s basic unit is a particle with a mass of approximately one billion suns (in order to sample galaxies with masses of about a trillion suns), and it includes 64 billion and more of those particles.
The Roadrunner Universe model relies on a hierarchical grid/particle algorithm that best matches the physical aspects of the simulation to the hybrid architecture of Roadrunner. Habib and his team wrote an entirely new computer code to exploit Roadrunner’s hybrid architecture and make full use of the PowerXCell 8i computational accelerators. They also created a dedicated analysis and visualization software framework to handle the huge simulation database.
Other jobs lined up for the machine include creating an HIV family tree that could lead researchers to new vaccine focus areas, and modelling nanowires over long timescales.
