CudaPre3D: An Alternative Preprocessing Algorithm for Accelerating 3D Convex Hull Computation on the GPU

In the calculating of convex hulls for point sets, a preprocessing procedure that is to filter the input points by discarding non-extreme points is commonly used to improve the computational efficiency. We previously proposed a quite straightforward preprocessing approach for accelerating 2D conv...

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Main Authors: MEI, G., XU, N.
Format: Article
Language:English
Published: Stefan cel Mare University of Suceava 2015-05-01
Series:Advances in Electrical and Computer Engineering
Subjects:
Online Access:http://dx.doi.org/10.4316/AECE.2015.02005
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spelling doaj-1b4ad6c6d2434b3ba94984136d96d5ad2020-11-24T22:55:58ZengStefan cel Mare University of SuceavaAdvances in Electrical and Computer Engineering1582-74451844-76002015-05-01152354410.4316/AECE.2015.02005CudaPre3D: An Alternative Preprocessing Algorithm for Accelerating 3D Convex Hull Computation on the GPUMEI, G.XU, N.In the calculating of convex hulls for point sets, a preprocessing procedure that is to filter the input points by discarding non-extreme points is commonly used to improve the computational efficiency. We previously proposed a quite straightforward preprocessing approach for accelerating 2D convex hull computation on the GPU. In this paper, we extend that algorithm to being used in 3D cases. The basic ideas behind these two preprocessing algorithms are similar: first, several groups of extreme points are found according to the original set of input points and several rotated versions of the input set; then, a convex polyhedron is created using the found extreme points; and finally those interior points locating inside the formed convex polyhedron are discarded. Experimental results show that: when employing the proposed preprocessing algorithm, it achieves the speedups of about 4x on average and 5x to 6x in the best cases over the cases where the proposed approach is not used. In addition, more than 95 percent of the input points can be discarded in most experimental tests.http://dx.doi.org/10.4316/AECE.2015.02005computational geometrycomputer aided engineeringmulticore processingparallel algorithmsparallel programming
collection DOAJ
language English
format Article
sources DOAJ
author MEI, G.
XU, N.
spellingShingle MEI, G.
XU, N.
CudaPre3D: An Alternative Preprocessing Algorithm for Accelerating 3D Convex Hull Computation on the GPU
Advances in Electrical and Computer Engineering
computational geometry
computer aided engineering
multicore processing
parallel algorithms
parallel programming
author_facet MEI, G.
XU, N.
author_sort MEI, G.
title CudaPre3D: An Alternative Preprocessing Algorithm for Accelerating 3D Convex Hull Computation on the GPU
title_short CudaPre3D: An Alternative Preprocessing Algorithm for Accelerating 3D Convex Hull Computation on the GPU
title_full CudaPre3D: An Alternative Preprocessing Algorithm for Accelerating 3D Convex Hull Computation on the GPU
title_fullStr CudaPre3D: An Alternative Preprocessing Algorithm for Accelerating 3D Convex Hull Computation on the GPU
title_full_unstemmed CudaPre3D: An Alternative Preprocessing Algorithm for Accelerating 3D Convex Hull Computation on the GPU
title_sort cudapre3d: an alternative preprocessing algorithm for accelerating 3d convex hull computation on the gpu
publisher Stefan cel Mare University of Suceava
series Advances in Electrical and Computer Engineering
issn 1582-7445
1844-7600
publishDate 2015-05-01
description In the calculating of convex hulls for point sets, a preprocessing procedure that is to filter the input points by discarding non-extreme points is commonly used to improve the computational efficiency. We previously proposed a quite straightforward preprocessing approach for accelerating 2D convex hull computation on the GPU. In this paper, we extend that algorithm to being used in 3D cases. The basic ideas behind these two preprocessing algorithms are similar: first, several groups of extreme points are found according to the original set of input points and several rotated versions of the input set; then, a convex polyhedron is created using the found extreme points; and finally those interior points locating inside the formed convex polyhedron are discarded. Experimental results show that: when employing the proposed preprocessing algorithm, it achieves the speedups of about 4x on average and 5x to 6x in the best cases over the cases where the proposed approach is not used. In addition, more than 95 percent of the input points can be discarded in most experimental tests.
topic computational geometry
computer aided engineering
multicore processing
parallel algorithms
parallel programming
url http://dx.doi.org/10.4316/AECE.2015.02005
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