MSz: An Efficient Parallel Algorithm for Correcting Morse-Smale Segmentations in Error-Bounded Lossy Compressors

This research explores a novel paradigm for preserving topological segmentations in existing error-bounded lossy compressors. Today's lossy compressors rarely consider preserving topologies such as Morse-Smale complexes, and the discrepancies in topology between original and decompressed datase...

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Vydané v:IEEE transactions on visualization and computer graphics Ročník 31; číslo 1; s. 130 - 140
Hlavní autori: Li, Yuxiao, Liang, Xin, Wang, Bei, Qiu, Yongfeng, Yan, Lin, Guo, Hanqi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.01.2025
Predmet:
Compressors
feature-preserving compression
Lossy compression
Morse-Smale segmentations
Prediction algorithms
Reviews
Scalability
shared-memory parallelism
Topology
Vectors
Watersheds
ISSN:1077-2626, 1941-0506, 1941-0506
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Shrnutí:This research explores a novel paradigm for preserving topological segmentations in existing error-bounded lossy compressors. Today's lossy compressors rarely consider preserving topologies such as Morse-Smale complexes, and the discrepancies in topology between original and decompressed datasets could potentially result in erroneous interpretations or even incorrect scientific conclusions. In this paper, we focus on preserving Morse-Smale segmentations in 2D/3D piecewise linear scalar fields, targeting the precise reconstruction of minimum/maximum labels induced by the integral line of each vertex. The key is to derive a series of edits during compression time. These edits are applied to the decompressed data, leading to an accurate reconstruction of segmentations while keeping the error within the prescribed error bound. To this end, we develop a workflow to fi x ex trema an d in tegral lines alternatively until convergence within finite iterations. We accelerate each workflow component with shared-memory/GPU parallelism to make the performance practical for coupling with compressors. We demonstrate use cases with fluid dynamics, ocean, and cosmology application datasets with a significant acceleration with an NVIDIA A100 GPU.
Bibliografia:ObjectType-Article-1
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ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2024.3456337