Carr, HA orcid.org/0000-0001-6739-0283, Rübel, O, Weber, GH et al. (1 more author) (2021) Optimization and Augmentation for Data Parallel Contour Trees. IEEE Transactions on Visualization and Computer Graphics. ISSN 1077-2626
Abstract
Contour trees are used for topological data analysis in scientific visualization. While originally computed with serial algorithms, recent work has introduced a vector-parallel algorithm. However, this algorithm is relatively slow for fully augmented contour trees which are needed for many practical data analysis tasks. We therefore introduce a representation called the hyperstructure that enables efficient searches through the contour tree and use it to construct a fully augmented contour tree in data parallel, with performance on average 6 times faster than the state-of-the-art parallel algorithm in the TTK topological toolkit.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Computational Topology, Contour Tree, Parallel Algorith |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/J013072/1 US Department of Energy Not Known |
Depositing User: | Symplectic Publications |
Date Deposited: | 18 Feb 2021 11:55 |
Last Modified: | 07 Dec 2021 15:29 |
Status: | Published online |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/TVCG.2021.3064385 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:171318 |