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Purpose

I and my team strive to enable scalable HPC data analytics of scientific data. In other words, we study the use of high-performance supercomputers for the analysis and visualization of scientific data, in addition to supercomputers' traditional role for simulation and modeling. The data we analyze originates in scientific instruments and computer simulations, and the supercomputers and science facilities are some of the largest in the world.

Strategies

We study the problem from three perspectives: scalable algorithms, software infrastructure, and application engagement.

Team

The team, which we have affectionately dubbed PEDAL (Parallel Extreme-Scale Data Analytics) consists of the following members:

Tom's picture

Tom Peterka
computer scientist

Youssef's picture

Youssef Nashed
assistant computer scientist

Hanqi's picture

Hanqi Guo
assistant computer scientist

Orcun's picture

Orcun Yildiz
postdoctoral scholar

Mukund's picture

Mukund Raj
postdoctoral scholar

Bio

Tom Peterka is a computer scientist at Argonne National Laboratory, scientist at the University of Chicago Consortium for Advanced Science and Engineering (CASE), adjunct assistant professor at the University of Illinois at Chicago, and fellow of the Northwestern Argonne Institute for Science and Engineering (NAISE). His research interests are in large-scale parallel in situ analysis of scientific data. Recipient of the 2017 DOE early career award and three best paper awards, Peterka has published in ACM SIGGRAPH, IEEE VR, IEEE TVCG, and ACM/IEEE SC, among other top conferences and journals. Peterka received his Ph.D. in computer science from the University of Illinois at Chicago in 2007, and he currently works actively in several DOE- and NSF-funded projects.