用于科学探索的轮廓树的分布式增强、超扫描与分支分解
Distributed Augmentation, Hypersweeps, and Branch Decomposition of Contour Trees for Scientific Exploration
AI总结：
本文提出分布式增强、超扫描和分支分解算法，实现轮廓树几何属性的并行计算，并支持将其作为科学探索的查询结构，以高效识别和提取大规模科学数据中的重要轮廓。
摘要：
轮廓树描述了标量场中水平集的拓扑结构，并广泛应用于拓扑数据分析和可视化。利用轮廓树处理大规模科学数据的一个主要挑战是在高性能计算环境下进行大规模计算。为应对这一挑战，近期工作引入了分布式层次轮廓树，用于轮廓树的分布式计算与存储。然而，在分析和可视化中有效使用这些分布式结构，需要后续计算几何属性并进行分支分解，以支持轮廓提取与探索。本文提出了用于增强、超扫描和分支分解的分布式算法，这些算法能够并行计算几何属性，并支持将分布式轮廓树作为科学探索的查询结构使用。我们评估了这些算法的并行性能，并将其应用于识别和提取科学可视化中的重要轮廓。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
