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JCR Impact Factor: 0.459
JCR 5-Year IF: 0.442
Issues per year: 4
Current issue: Feb 2017
Next issue: May 2017
Avg review time: 79 days


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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Broken Bar Fault Detection in IM Operating Under No-Load Condition, RELJIC, D., JERKAN, D., MARCETIC, D., OROS, D.
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With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "Big Data - " before the paper title in OpenConf.

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2016-Jun-14
Thomson Reuters published the Journal Citations Report for 2015. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.459, and the JCR 5-Year Impact Factor is 0.442.

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  2/2015 - 5

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

MEI, G. See more information about MEI, G. on SCOPUS See more information about MEI, G. on IEEExplore See more information about MEI, G. on Web of Science, XU, N. See more information about XU, N. on SCOPUS See more information about XU, N. on SCOPUS See more information about XU, N. on Web of Science
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,043 KB) | Citation | Downloads: 244 | Views: 1,386

Author keywords
computational geometry, computer aided engineering, multicore processing, parallel algorithms, parallel programming

References keywords
convex(20), hull(14), hulls(5), graphics(5), algorithm(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2015-05-31
Volume 15, Issue 2, Year 2015, On page(s): 35 - 44
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.02005
Web of Science Accession Number: 000356808900005
SCOPUS ID: 84979726246

Abstract
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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.


References | Cited By  «-- Click to see who has cited this paper

[1] F. P. Preparata, M. I. Shamos, "Computational Geometry: An Introduction", pp. 131-136, New York: Springer-Verlag, 1985.

[2] F. P. Preparata, S. J. Hong, "Convex hulls of finite sets of points in two and three dimensions," Communications of the ACM, vol. 20, no. 2, pp. 87-93, 1977.
[CrossRef] [Web of Science Times Cited 358] [SCOPUS Times Cited 386]


[3] K. L. Clarkson, P. W. Shor, "Algorithms for diametral pairs and convex hulls that are optimal, randomized, and incremental," in Proceedings of the 4th symposium on Computational geometry, 1988, pp. 12-17.
[CrossRef]


[4] T. M. Chan, "Optimal output-sensitive convex hull algorithms in two and three dimensions," Discrete & Computational Geometry, vol. 16, no. 4, pp. 361-368, 1996.
[CrossRef] [Web of Science Times Cited 93]


[5] C. B. Barber, D. P. Dobkin, H. Huhdanpaa, "The quickhull algorithm for convex hulls," ACM Transactions on Mathematical Software (TOMS), vol. 22, no. 4, pp. 469-483, 1996.
[CrossRef] [Web of Science Times Cited 1771]


[6] D. Srikanth, K. Kothapalli, R. Govindarajulu, P. Narayanan, "Parallelizing two dimensional convex hull on NVIDIA GPU and Cell BE," in International conference on high performance computing (HiPC), 2009, pp. 1-5.

[7] S. Srungarapu, D. P. Reddy, K. Kothapalli, P. Narayanan, "Fast two dimensional convex hull on the GPU," in Advanced Information Networking and Applications (WAINA), 2011, pp. 7-12.
[CrossRef] [SCOPUS Times Cited 11]


[8] A. Stein, E. Geva, J. El-Sana, "CudaHull: Fast parallel 3D convex hull on the GPU," Computers & Graphics, vol. 36, no. 4, pp. 265-271, 2012.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 20]


[9] M. Tang, J.-y. Zhao, R.-f. Tong, D. Manocha, "GPU accelerated convex hull computation," Computers & Graphics, vol. 36, no. 5, pp. 498-506, 2012.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 15]


[10] M. Gao, T.-T. Cao, T.-S. Tan, Z. Huang, "gHull: a three-dimensional convex hull algorithm for graphics hardware," in Symposium on Interactive 3D Graphics and Games, San Francisco, California, 2011, pp. 204-204.
[CrossRef] [SCOPUS Times Cited 5]


[11] M. Gao, T.-T. Cao, A. Nanjappa, T.-S. Tan, Z. Huang, "gHull: A GPU algorithm for 3D convex hull," ACM Transactions on Mathematical Software (TOMS), vol. 40, no. 1, pp. 1-19, 2013.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 3]


[12] M. Gao, T.-T. Cao, T.-S. Tan, Z. Huang, "Flip-flop: convex hull construction via star-shaped polyhedron in 3D," in Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, 2013, pp. 45-54.
[CrossRef] [SCOPUS Times Cited 4]


[13] S. Tzeng, J. D. Owens, "Finding convex hulls using Quickhull on the GPU," arXiv:1201.2936, 2012.

[14] J. M. White, K. A. Wortman, "Divide-and-Conquer 3D convex hulls on the GPU," arXiv:1205.1171, 2012.

[15] B. Kim, K.-J. Kim, "Computing the convex hull for a set of spheres on a GPU," in Proceedings of the 11th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry, Singapore, 2012, pp. 345-345.
[CrossRef] [SCOPUS Times Cited 4]


[16] C. Xing, Z. Xiong, Y. Zhang, X. Wu, J. Dan, T. Zhang, "An eficient convex hull algorithm using affine transformation in planar point set," Arabian Journal for Science and Engineering, vol. 39, no. 11, pp. 7785-7793, 2014.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 3]


[17] S. G. Akl, G. T. Toussaint, "A fast convex hull algorithm," Information Processing Letters, vol. 7, no. 5, pp. 219-222, 1978.
[CrossRef] [Web of Science Times Cited 56] [SCOPUS Times Cited 76]


[18] J. Cadenas, G. Megson, "Rapid preconditioning of data for accelerating convex hull computations," Electronics Letters, vol. 50, no. 4, pp. 270-272, 2014.
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 4]


[19] G. Mei, "A straightforward preprocessing approach for accelerating convex hull computations on the GPU," arXiv:1405.3454, 2014.

[20] M. Kallay, "The complexity of incremental convex hull algorithms in Rd," Information Processing Letters, vol. 19, no. 4, pp. 197, 1984.
[CrossRef] [Web of Science Times Cited 21] [SCOPUS Times Cited 5]


[21] B. Grunbaum, "Measure of symmetry for convex sets, "In: Proceedings of the¨7th symposium in pure mathematics of the American mathematical society, 1961. pp. 233-70.



References Weight

Web of Science® Citations for all references: 2,331 TCR
SCOPUS® Citations for all references: 536 TCR

Web of Science® Average Citations per reference: 106 ACR
SCOPUS® Average Citations per reference: 24 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2017-04-18 06:54 in 104 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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