Abstract
Contemporary scientific data sets require fast and scalable topological analysis to enable visualization, simplification and interaction. Within this field, parallel merge tree construction has seen abundant recent contributions, with a trend of decentralized, task-parallel or SMP-oriented algorithms dominating in terms of total runtime. However, none of these recent approaches computed complete merge trees on distributed systems, leaving this field to traditional divide & conquer approaches. This article introduces a scalable, parallel and distributed algorithm for merge tree construction outperforming the previously fastest distributed solution by a factor of around three. This is achieved by a task-parallel identification of individual merge tree arcs by growing regions around critical points in the data, without any need for ordered progression or global data structures, based on a novel insight introducing a sufficient local boundary for region growth.
Highlights
W ITH increasing size and complexity of scientific data arising from simulations and measurements, methods for efficient visualization and interactive exploration are becoming more and more important, forming a crucial component of modern, computational science
Each local minimum is a vertex with no smaller-valued neighbors and creates an isolated connected component of the sub-level set at its value, and it appears as a leaf in the join tree
We have described a task-parallel algorithm for augmented merge tree construction, that is efficient in shared-memory, scalable in distributed settings, adaptable to a hybrid GPU solution and can compute the entire, un-altered merge tree with or without augmentation
Summary
W ITH increasing size and complexity of scientific data arising from simulations and measurements, methods for efficient visualization and interactive exploration are becoming more and more important, forming a crucial component of modern, computational science. Extend upon the monotone path-based core idea of methods, that do not rely on locally ordered progression [15], [18], [22], by deriving a novel insight of a ”local boundary” This avoids global data structures, resulting in the identification of locally restricted, independent tasks, that can span beyond shared-memory and run on distributed computation nodes concurrently. The contour tree is a powerful topological abstraction, building a kind of skeleton of geometry with respect to a scalar field Within this setting, each local minimum is a vertex with no smaller-valued neighbors and creates an isolated connected component of the sub-level set at its value, and it appears as a leaf in the join tree. This is the aspect in which most contemporary solutions differ
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