Parallelized Topological Relaxation Algorithm

Geometric problems of interest to mathematical visualization applications involve changing structures, such as the moves that transform one knot into an equivalent knot. In this paper, we describe mathematical entities (curves and surfaces) as link-node graphs, and make use of energy-driven relaxati...

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Bibliographic Details
Published in2019 IEEE International Conference on Big Data (Big Data) pp. 3406 - 3415
Main Authors Ruan, Guangchen, Zhang, Hui
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2019
Subjects
Force
Instruction sets
mathematical visualization
Parallel algorithms
parallel computing
performance tuning
python
Task analysis
Three-dimensional displays
Time complexity
topological relaxation
Two dimensional displays
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DOI10.1109/BigData47090.2019.9006309

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Summary:Geometric problems of interest to mathematical visualization applications involve changing structures, such as the moves that transform one knot into an equivalent knot. In this paper, we describe mathematical entities (curves and surfaces) as link-node graphs, and make use of energy-driven relaxation algorithms to optimize their geometric shapes by moving knots and surfaces to their simplified equivalence. Furthermore, we design and conFigure parallel functional units in the relaxation algorithms to accelerate the computation these mathematical deformations require. Results show that we can achieve significant performance optimization via the proposed threading model and level of parallelization.
DOI:10.1109/BigData47090.2019.9006309