The Feature Tree: Visualizing Feature Tracking in Distributed AMR Datasets

  • Authors:
  • J. Chen;D. Silver;L. Jiang

  • Affiliations:
  • Rutgers University;Rutgers University;Rutgers University

  • Venue:
  • PVG '03 Proceedings of the 2003 IEEE Symposium on Parallel and Large-Data Visualization and Graphics
  • Year:
  • 2003

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Abstract

In this paper, 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 will now contain 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 multi-level 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).