IEEE VIS 2024 Content: Distributed Path Compression for Piecewise Linear Morse-Smale Segmentations and Connected Components

Distributed Path Compression for Piecewise Linear Morse-Smale Segmentations and Connected Components

Michael Will - RPTU Kaiserslautern-Landau, Kaiserslautern, Germany

Jonas Lukasczyk - RPTU Kaiserslautern-Landau, Kaiserslautern, Germany

Julien Tierny - CNRS, Paris, France. Sorbonne Université, Paris, France

Christoph Garth - RPTU Kaiserslautern-Landau, Kaiserslautern, Germany

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Room: Bayshore II

2024-10-13T16:00:00ZGMT-0600Change your timezone on the schedule page
2024-10-13T16:00:00Z
Exemplar figure, described by caption below
Left: an illustration of using path compression to quickly compute the ascending / descending segmentations. Right: Illustrating the use of Connected Component extraction for data segmentation. Running these computations on multiple nodes allows us to use much larger datasets by using the distributed memory of all the nodes.
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Abstract

This paper describes the adaptation of a well-scaling parallel algorithm for computing Morse-Smale segmentations based on path compression to a distributed computational setting. Additionally, we extend the algorithm to efficiently compute connected components in distributed structured and unstructured grids, based either on the connectivity of the underlying mesh or a feature mask. Our implementation is seamlessly integrated with the distributed extension of the Topology ToolKit (TTK), ensuring robust performance and scalability. To demonstrate the practicality and efficiency of our algorithms, we conducted a series of scaling experiments on large-scale datasets, with sizes of up to 4096^3 vertices on up to 64 nodes and 768 cores.