For discovering significant supernova phenomena and simulation flaws, several pairs of eyes beat pages of numbers, Anthony Mezzacappa says.
Data visualization has been a key tool as he and his fellow astrophysicists model the standing accretion shock instability (SASI) in core-collapse supernovae, says Mezzacappa, director of the Joint Institute for Computational Science at Oak Ridge National Laboratory and the University of Tennessee, Knoxville (UT).
His collaborator, North Carolina State University astrophysics professor John Blondin, says visualization was vital in the mid-2000s, when the researchers moved from two-dimensional to three-dimensional models on Phoenix, Oak Ridge’s Cray X1E.
“We were running models and we literally had no ways of visualizing them,” Blondin says. “That really was poking around in the dark, trying to see what was happening.”
The team eventually found commercial visualization software and later tapped into EVEREST, a then-new Oak Ridge facility where researchers watch simulation renderings on wall-size displays.
Visualization is even more important – and more difficult – today, as the research team uses Titan, Oak Ridge’s Cray XK7, to run detailed 3-D supernova models. “It continues to be enormous in understanding these simulations” and is still the best way to spot bugs, Mezzacappa says.
The latest simulation code, Chimera, is named for a three-headed beast from Greek mythology because it includes three major components: neutrino dynamics, hydrodynamics and nuclear element synthesis. To tame its data output, visualization specialists led by Oak Ridge and UT computational physicist Bronson Messer developed Bellerophon, named for the mythical hero who slayed the chimera.
Bellerophon lets researchers see how their simulations are evolving on the fly so they can identify and correct problems, Mezzacappa says. It also lets researchers capture still images and movies for analysis and presentation at conferences.
“That’s been just a godsend to us,” he adds.
A fourth-year fellow probes cloud droplets to boost climate predictions. Read More
A UC Davis fellow combines computing, the corn genome and growing data to help farmers… Read More
A Computational Science Graduate Fellowship recipient monitors threats to satellites in Earth’s ionosphere by modeling… Read More
A computational sciences fellow models COVID-19 virus variants and examines how people weigh complex decisions. Read More
A Colorado State fellow employs machine learning for climate modeling, putting provenance behind predictions. Read More
A LANL statistician helps cosmologists and epidemiologists grasp their data and answer vital questions. Read More