GPU-based efficient computation of power diagram

[Display omitted] •We introduced the GPU-based JFA for parallelly rendering the power diagram.•We proposed a method to extract the geometrical of the power diagram.•Our constructing method is coupled with the existing method as a hybrid algorithm.•We put the JFA and GPU-based optimization into our p...

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Veröffentlicht in:Computers & graphics 2019-05, Vol.80, p.29-36
Hauptverfasser: Zheng, Liping, Gui, Zhiqiang, Cai, Ruiwen, Fei, Yue, Zhang, Gaofeng, Xu, Benzhu
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container_end_page 36
container_issue
container_start_page 29
container_title Computers & graphics
container_volume 80
creator Zheng, Liping
Gui, Zhiqiang
Cai, Ruiwen
Fei, Yue
Zhang, Gaofeng
Xu, Benzhu
description [Display omitted] •We introduced the GPU-based JFA for parallelly rendering the power diagram.•We proposed a method to extract the geometrical of the power diagram.•Our constructing method is coupled with the existing method as a hybrid algorithm.•We put the JFA and GPU-based optimization into our pure GPU algorithm. Power diagrams are widely used in graphics and engineering. One of the most complex operations defined on the centroidal capacity-constrained power diagrams is the geometrical construction, which takes more than 50% of the total computing time. In order to overcome this performance bottleneck, we propose a novel GPU-based power diagram construction algorithm. To this end, we first introduce the jump flooding algorithm for parallel rendering of the power diagram, and present an approach for extracting the geometrical vertices and edges. Next, we introduce two novel GPU-based algorithms to improve the computational performance. The first algorithm allows a hybrid GPU-CPU implementation by coupling the existing CPU-based algorithm while the second algorithm is a pure GPU implementation for the platforms where GPU hardware is capable of significant speedups. In our implementation, we utilize the discrete Lloyd’s algorithm for centroidal constraints and a GPU-based analytical algorithm for weights and capacities. Experiment results show that our approach improves the effciency of the power diagram construction up to several orders of magnitude.
doi_str_mv 10.1016/j.cag.2019.03.011
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subjects Algorithms
Apexes
Capacity
Centroidal
Computational efficiency
Computing time
Constraints
Flooding
GPU accelerate
Graph theory
Graphics processing units
Power diagram
title GPU-based efficient computation of power diagram
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