The size and detail of their results will be useful to other researchers involved in spatial mapping and simulated sky surveys, shedding light on the underlying physical processes at work. But to keep the model faithful to reality, the researchers need to represent the extreme variability of matter as it coalesces under gravity, becoming many orders of magnitude more dense in local areas.

"We need to zoom in on these dense regions to capture the key physical processes - including gravitation, flows of normal and 'dark' matter, and shock heating and radiative cooling of the gas", stated Michael Norman. "This requires ENZO's 'adaptive mesh refinement' capability."

Adaptive mesh refinement (AMR) codes begin with a coarse grid spacing, and then spawn more detailed (and more computationally demanding) subgrids as needed to track key processes in higher density regions.

"We achieved unprecedented detail by reaching seven levels of subgrids throughout the survey volume - something never done before - producing more than 400,000 subgrids, which we could only do thanks to the two large-memory TeraGrid systems", stated San Diego Supercomputer Center's computational scientist Robert Harkness, who carried out the runs with astrophysicist Brian O'Shea of Los Alamos National Laboratory.

Running the code for a total of about 500,000 processor hours, the researchers used 2 TB of memory (around 2000 times the memory of a typical laptop) on the IBM DataStar at SDSC and 1.5 TB of shared memory on the SGI Altix Cobalt system at NCSA. To achieve these computations, an ASTA collaboration between Robert Harkness and Michael Norman's group made major improvements in scaling and efficiency of the code.