sponsored byIEEEACMThe International Conference for High Performance 
Computing, Networking, Storage and Analysis
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SCHEDULE: NOV 16-21, 2014

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In-Situ Feature Extraction of Large Scale Combustion Simulations Using Segmented Merge Trees

SESSION: Large-Scale Visualization


TIME: 4:30PM - 5:00PM


AUTHOR(S):Aaditya G. Landge, Valerio Pascucci, Attila Gyulassy, Janine C. Bennett, Hemanth Kolla, Jacqueline Chen, Peer-Timo Bremer



The increasing amount of data generated by scientific simulations coupled with system I/O constraints are fueling a need for in-situ analysis techniques. Of particular interest are approaches that produce reduced data representations while maintaining the ability to redefine, extract, and study features in a post-process to obtain scientific insights.
We present two variants of in-situ feature extraction techniques using segmented merge trees, which encode a wide range of threshold based features. The first approach is a fast, low communication cost technique that generates an exact solution but has limited scalability. The second is a scalable, local approximation that guarantees to correctly extract all features up to a predefined size. We demonstrate both variants using some of the largest combustion simulations available on leadership class supercomputers. Our approach allows state-of-the-art, feature-based analysis to be performed in-situ at significantly higher frequency than currently possible and with negligible impact on the simulation runtime.

Chair/Author Details:

Brent Welch (Chair) - Google

Aaditya G. Landge - University of Utah

Valerio Pascucci - University of Utah

Attila Gyulassy - University of Utah

Janine C. Bennett - Sandia National Laboratories

Hemanth Kolla - Sandia National Laboratories

Jacqueline Chen - Sandia National Laboratories

Peer-Timo Bremer - Lawrence Livermore National Laboratory

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