IEEE VIS 2024 Content: Distributed Augmentation, Hypersweeps, and Branch Decomposition of Contour Trees for Scientific Exploration

Distributed Augmentation, Hypersweeps, and Branch Decomposition of Contour Trees for Scientific Exploration

Mingzhe Li - University of Utah, Salt Lake City, United States

Hamish Carr - University of Leeds, Leeds, United Kingdom

Oliver Rübel - Lawrence Berkeley National Laboratory, Berkeley, United States

Bei Wang - University of Utah, Salt Lake City, United States

Gunther H Weber - Lawrence Berkeley National Laboratory, Berkeley, United States

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Room: Bayshore I

2024-10-17T14:39:00ZGMT-0600Change your timezone on the schedule page
2024-10-17T14:39:00Z
Exemplar figure, described by caption below
Our method applied to a 3D WarpX laser-driven, plasma-based particle accelerator simulation dataset with a resolution of 6791x371x371. We use the x-component of the electric field. Left: three 2D slices of the volume along different axes with the extracted contours on the slice. Right: Using distributed topological data analysis to extract and visualize 3D isosurfaces corresponding to the top-11 branches of the contour tree.
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Keywords

Contour trees, branch decomposition, parallel algorithms, computational topology, topological data analysis

Abstract

Contour trees describe the topology of level sets in scalar fields and are widely used in topological data analysis and visualization. A main challenge of utilizing contour trees for large-scale scientific data is their computation at scale using high-performance computing. To address this challenge, recent work has introduced distributed hierarchical contour trees for distributed computation and storage of contour trees. However, effective use of these distributed structures in analysis and visualization requires subsequent computation of geometric properties and branch decomposition to support contour extraction and exploration. In this work, we introduce distributed algorithms for augmentation, hypersweeps, and branch decomposition that enable parallel computation of geometric properties, and support the use of distributed contour trees as query structures for scientific exploration. We evaluate the parallel performance of these algorithms and apply them to identify and extract important contours for scientific visualization.