Carr, HA orcid.org/0000-0001-6739-0283, Rübel, O and Weber, GH (2022) Distributed Hierarchical Contour Trees. In: 2022 IEEE 12th Symposium on Large Data Analysis and Visualization (LDAV). 2022 IEEE 12th Symposium on Large Data Analysis and Visualization (LDAV), 16 Oct 2022, Oklahoma City, OK, USA. IEEE ISBN 978-1-6654-9156-3
Abstract
Contour trees are a significant tool for data analysis as they capture both local and global variation. However, their utility has been limited by scalability, in particular for distributed computation and storage. We report a distributed data structure for storing the contour tree of a data set distributed on a cluster, based on a fan-in hierarchy, and an algorithm for computing it based on the boundary tree that represents only the superarcs of a contour tree that involve contours that cross boundaries between blocks. This allows us to limit the communication cost for contour tree computation to the complexity of the block boundaries rather than of the entire data set.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This item is protected by copyright. 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. Uploaded in accordance with the publisher's self-archiving policy. |
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 US Department of Energy Not Known |
Depositing User: | Symplectic Publications |
Date Deposited: | 21 Sep 2022 11:27 |
Last Modified: | 08 Jan 2025 15:53 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/LDAV57265.2022.9966394 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:190963 |