On the use of graph search techniques for the analysis of extreme-scale combustion simulation data

Conference · Mon Oct 01 04:00:00 EDT 2012 · IEEE Symposium on Large Data Analysis and Visualization (LDAV); 14-15 Oct. 2012; Seattle, WA, USA

DOI:https://doi.org/10.1109/LDAV.2012.6378976· OSTI ID:1567587

McLendon, W. C.; Bansal, G.; Bremer, P.-T; Chen, J.; Kolla, H.; Bennett, J. C.

With the continuous increase in high performance computing capabilities, simulations are becoming ever larger and more complex, using bigger domains, tracking more variables, and producing more time steps. This increase in the ranges of spatial and temporal simulation scales results in data that presents significant challenges to as well as new opportunities for the visualization and data analysis community. For example, highly-localized, intermittent events (such as the formation of ignition kernels in turbulent combustion) may be caused by interactions between multiple variables across a series of time steps, making both their definition and their extraction difficult, particularly at scale. This paper introduces an intuitive framework to support the identification, characterization, and tracking of such complex, multivariate, temporally evolving events in large-scale simulations. In a pre-processing step, we use topological techniques to create a hierarchical family of feature definitions for each variable of interest. Subsequently, we select a particular set of features for analysis and, using overlap-based metrics, we generate an attributed relational graph (ARG) capturing the relationships between different variables both within one and across multiple time steps. Finally, we leverage subgraph-isomorphism search heuristics to identify patterns in the ARG that characterize interesting events. We demonstrate the power of this approach by analyzing a large-scale turbulent combustion simulation.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
• Sponsoring Organization: USDOE Office of Science; USDOE
• OSTI ID: 1567587
• Conference Information: Journal Name: IEEE Symposium on Large Data Analysis and Visualization (LDAV); 14-15 Oct. 2012; Seattle, WA, USA
• Country of Publication: United States
• Language: English