If Dali had a supercomputer: amazing supernova rendering
By Darren Quick
August 2, 2009
Capturing complex visualizations, such as the above Dali-esque rendering of a supernova, don’t just produce pretty pictures ideal for desktop wallpapers. They also allow scientists to see simulations of complex physical, chemical and biological phenomena. Unfortunately generating the quadrillions of data points required for visualizations of everything from supernovas to protein structures is quickly overwhelming current computing capabilities. So scientists at the U.S. Department of Energy’s (DOE) Argonne National Laboratory are exploring ways to speed up the process using a technique called software-based parallel volume rendering.
Volume rendering is a technique that can be used to make sense of the billions of tiny points of data collected from an X-ray, MRI, or a researcher’s simulation. Argonne scientists are trying to find better, quicker ways to form a recognizable image from all of these points of data.
To do this the researchers first divide the data among many processing cores so that they can all work at once, a technique that’s called parallel computing. On Argonne’s Blue Gene/P supercomputer, 160,000 computing cores all work together in parallel. Today’s typical laptop, by comparison, has two cores.
Usually, the supercomputer’s work stops once the data has been gathered, and the data is sent to a set of graphics processors (GPUs), which create the final visualizations. But the driving commercial force behind developing GPUs has been the video game industry, so GPUs aren’t always well suited for scientific tasks. In addition, the sheer amount of data that has to be transferred between computers eats up valuable time and disk space. Just downloading the data to perform a single run of a current model of the explosion of a star on a home computer would take three years.
Argonne researchers wanted to know if they could improve performance by skipping the transfer to the GPUs and instead perform the visualizations right there on the supercomputer. They tested the technique on a set of astrophysics data and found that they could indeed increase the efficiency of the operation.
Because the Blue Gene/P's main processor can visualize data as they are analyzed, Argonne's scientists can investigate physical, chemical, and biological phenomena with much more spatial and temporal detail.
According to Mark Hereld, who leads the visualization and analysis efforts at the Argonne Leadership Computing Facility, this new visualization method could enhance research in a wide variety of disciplines. “In astrophysics, studying how stars burn and explode pulls together all kinds of physics: hydrodynamics, gravitational physics, nuclear chemistry and energy transport,” he said.
“Those kinds of problems often lead to questions that are very complicated to pose mathematically,” Hereld said. “But when you can simply watch a star explode through visualization of the simulation, you can gain insight that’s not available any other way.”
The early success the researchers have had with the software-based parallel volume rendering technique suggests we’ll have plenty of awe-inspiring images to choose from for our desktop wallpaper in no time at all.