2025/08/11 by Mingzhe Li, Li, Mingzhe, Hamish Carr +7
Computer Science · #Computational Geometry (cs.CG) #Computer Graphics and Visualization Techniques #Data Structures and Algorithms (cs.DS) #Data Visualization and Analytics #Distributed #FOS: Computer and information sciences #Parallel #Topological and Geometric Data Analysis #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2508.08433
openalex publication_date 2025/08/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Contour trees offer an abstract representation of the level set topology in scalar fields and are widely used in topological data analysis and visualization. However, applying contour trees to large-scale scientific datasets remains challenging due to scalability limitations. Recent developments in distributed hierarchical contour trees have addressed these challenges by enabling scalable computation across distributed systems. Building on these structures, advanced analytical tasks -- such as volumetric branch decomposition and contour extraction -- have been introduced to facilitate large-scale scientific analysis. Despite these advancements, such analytical tasks substantially increase memory usage, which hampers scalability. In this paper, we propose a pre-simplification strategy to significantly reduce the memory overhead associated with analytical tasks on distributed hierarchical contour trees. We demonstrate enhanced scalability through strong scaling experiments, constructing the largest known contour tree -- comprising over half a trillion nodes with complex topology -- in under 15 minutes on a dataset containing 550 billion elements.