 |
Center for Nonlinear Studies |
Scalar functions characterize many physical phenomena, e.g., the density distribution of particles highlights the structure of the universe in cosmological simulations, the fuel consumption rate indicates burning/extinct regions in combustion simulations, and the distance from a material boundary characterizes the structure of porous materials. Since data sizes in all applications grow beyond sizes that can be visualized directly, an increasingly important approach is to detect salient features in the data and use these features for subsequent analysis and visualization. Topological analysis has proven to be a powerful tool to detect such features for a wide range of applications since it supports feature definitions based on the general concept of “connectedness†and provides means to characterize the prominence and stability of features. However, the global nature of topological methods makes their parallelization challenging. In this talk, I will present applications of topological data analysis and a new approach to topological analysis on parallel machines: A new distributed representation of topological descriptors reduces the amount of communication necessary during their parallel computation and subsequent data analysis.
Host: Curt Canada