Bibliographic Details
| Title: |
Scalable Computation of Topological Abstractions for Scalar Data. |
| Authors: |
Will, M.1 (AUTHOR) mswill@rptu.de, Lukasczyk, J.1 (AUTHOR) jonas.lukasczyk@rptu.de, Tierny, J.2 (AUTHOR) julien.tierny@sorbonne-universite.fr, Gyulassy, A.3 (AUTHOR) jediati@sci.utah.edu, Weber, G. H.4 (AUTHOR) ghweber@lbl.gov, Carr, H.5 (AUTHOR) h.carr@leeds.ac.uk, Garth, C.1 (AUTHOR) garth@rptu.de |
| Source: |
Computer Graphics Forum. Jun2026, p1. 25p. 6 Illustrations. |
| Subject Terms: |
*Parallel programming, *Distributed computing, *Scalability, Data visualization |
| Abstract: |
Topological data analysis has become an important tool for large scale scalar data analysis and visualization, efficiently extracting the inherent structure and features of interest of the data. However, with growing dataset sizes and complexity, it is increasingly becoming infeasible to compute topological abstractions of interest in serial and on single machines. This paper presents the state of the art in the scalable computation of topological abstractions on scalar data, in shared memory parallel on single machines, and in distributed memory parallel on multiple machines. We highlight results for set‐based, graph‐based and complex‐based abstractions and organize the state of the art based on this taxonomy. The paper identifies parallelization and distribution techniques common in topological algorithms and highlights further areas of interest with underdeveloped efforts. [ABSTRACT FROM AUTHOR] |
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| Database: |
Business Source Index |