Real Time Feature Extraction and Tracking in a Computational Steering Environment

Abstract

Large distributed time-varying simulations are common in many scientific domains to study the evolution of various phenomena. These simulations produce thousands of timesteps which must be analyzed and interpreted. For datasets with evolving features, feature analysis and visualization tools are crucial to help interpret all the information. For example, it is usually important to know how many regions are evolving, what are their lifetimes, do they merge with others, how does the volume/mass change, etc. To be effective these visualization and analysis routines must also be parallelized in order to operate on the data where that data resides. Furthermore, interacting with the routines as the simulations are ongoing can aid in the analysis. In our previous work, we have developed a methodology for analyzing time-varying datasets which tracks 3D amorphous features as they evolve in time. In this paper, we describe the full parallel feature extraction and tracking algorithm within a computational steering environment for parallel and distributed simulations. We demonstrate how one can interact with the code and show various examples within ongoing computations.