Extremely Scalable Distributed Computation of Contour Trees via Pre-Simplification

TL;DR

This paper introduces a pre-simplification method that reduces memory usage in distributed contour tree computations, enabling the processing of extremely large datasets efficiently and scaling to over half a trillion nodes.

Contribution

It presents a novel pre-simplification strategy that significantly improves scalability and memory efficiency in distributed contour tree analysis for large scientific datasets.

Findings

Constructed the largest known contour tree with over 500 billion nodes.

Achieved contour tree computation on 550-billion-element dataset in under 15 minutes.

Demonstrated improved scalability with strong scaling experiments.

Abstract

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.