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

TL;DR

This paper presents distributed algorithms for contour tree augmentation, hypersweeps, and branch decomposition, enabling scalable geometric analysis and visualization of large scientific datasets using high-performance computing.

Contribution

It introduces novel distributed algorithms that facilitate parallel geometric analysis and branch decomposition of contour trees for large-scale scientific data exploration.

Findings

Algorithms demonstrate efficient parallel performance.

Effective identification and extraction of important scientific contours.

Supports scalable topological data analysis in high-performance environments.

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.