The feature tree: visualizing feature tracking in distributed AMR datasets

Abstract

We describe a feature extraction and tracking algorithm for AMR (adaptive mesh refinement) datasets that operates within a distributed computing environment. Because features can span multiple refinement levels and multiple processors, tracking must be performed across time, across levels, and across processors. The resulting visualization is represented as a "feature tree". A feature contains multiple parts corresponding to different levels of refinements. The feature tree allows a viewer to determine that a feature splits or merges at the next refinement level, and allows a viewer to extract and isolate a multilevel isosurface and watch how that surface changes over both time and space. The algorithm is implemented within a computational steering environment, which enables the visualization routines to operate on the data in-situ (while the simulation is ongoing).