GPU-Assisted Computation of Centroidal Voronoi Tessellation
Identifieur interne : 002441 ( Main/Exploration ); précédent : 002440; suivant : 002442GPU-Assisted Computation of Centroidal Voronoi Tessellation
Auteurs : Guodong Rong [États-Unis] ; Yang Liu [France] ; Wenping Wang [Hong Kong] ; Xaotian Yin [États-Unis] ; Xianfeng Gu [États-Unis] ; Xiaohu Guo [États-Unis]Source :
- IEEE Transactions on Visualization and Computer Graphics [ 1077-2626 ] ; 2011-03.
Abstract
Centroidal Voronoi tessellations (CVT) are widely used in computational science and engineering. The most commonly used method is Lloyd's method, and recently the L-BFGS method is shown to be faster than Lloyd's method for computing the CVT. However, these methods run on the CPU and are still too slow for many practical applications. We present techniques to implement these methods on the GPU for computing the CVT on 2D planes and on surfaces, and demonstrate significant speedup of these GPU-based methods over their CPU counterparts. For CVT computation on a surface, we use a geometry image stored in the GPU to represent the surface for computing the Voronoi diagram on the surface. In our implementation a new technique is proposed for parallel regional reduction on the GPU for evaluating integrals over Voronoi cells.
Url:
DOI: 10.1109/TVCG.2010.53
Affiliations:
- France, Hong Kong, États-Unis
- Grand Est, Lorraine (région), Texas
- Austin (Texas), Nancy
- Institut national polytechnique de Lorraine, Université Nancy 2, Université de Lorraine, Université du Texas à Austin
Links toward previous steps (curation, corpus...)
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- to stream Hal, to step Curation: 006B24
- to stream Hal, to step Checkpoint: 001F25
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Le document en format XML
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New-York at Stonybrook</orgName><desc><address><country key="FR"></country></address></desc></org></tutelle></tutelles></hal:affiliation><country>États-Unis</country></affiliation></author><author><name sortKey="Guo, Xiaohu" sort="Guo, Xiaohu" uniqKey="Guo X" first="Xiaohu" last="Guo">Xiaohu Guo</name><affiliation wicri:level="1"><hal:affiliation type="laboratory" xml:id="struct-38230" status="VALID"><orgName>Computer Science Department [Austin]</orgName><desc><address><addrLine>University of Texas, Station C0500 Taylor Hall 2.124 Austin, TX 78712-0233</addrLine><country key="US"></country></address><ref type="url">http://www.cs.utexas.edu/</ref></desc><listRelation><relation active="#struct-300756" type="direct"></relation></listRelation><tutelles><tutelle active="#struct-300756" type="direct"><org type="institution" xml:id="struct-300756" status="INCOMING"><orgName>University of Texas at Austin</orgName><desc><address><country key="FR"></country></address></desc></org></tutelle></tutelles></hal:affiliation><country>États-Unis</country><placeName><settlement type="city">Austin (Texas)</settlement><region type="state">Texas</region></placeName><orgName type="university">Université du Texas à Austin</orgName></affiliation></author></analytic><idno type="DOI">10.1109/TVCG.2010.53</idno><series><title level="j">IEEE Transactions on Visualization and Computer Graphics</title><idno type="ISSN">1077-2626</idno><imprint><date type="datePub">2011-03</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Centroidal Voronoi tessellations (CVT) are widely used in computational science and engineering. The most commonly used method is Lloyd's method, and recently the L-BFGS method is shown to be faster than Lloyd's method for computing the CVT. However, these methods run on the CPU and are still too slow for many practical applications. We present techniques to implement these methods on the GPU for computing the CVT on 2D planes and on surfaces, and demonstrate significant speedup of these GPU-based methods over their CPU counterparts. For CVT computation on a surface, we use a geometry image stored in the GPU to represent the surface for computing the Voronoi diagram on the surface. In our implementation a new technique is proposed for parallel regional reduction on the GPU for evaluating integrals over Voronoi cells.</div></front></TEI><affiliations><list><country><li>France</li><li>Hong Kong</li><li>États-Unis</li></country><region><li>Grand Est</li><li>Lorraine (région)</li><li>Texas</li></region><settlement><li>Austin (Texas)</li><li>Nancy</li></settlement><orgName><li>Institut national polytechnique de Lorraine</li><li>Université Nancy 2</li><li>Université de Lorraine</li><li>Université du Texas à Austin</li></orgName></list><tree><country name="États-Unis"><region name="Texas"><name sortKey="Rong, Guodong" sort="Rong, Guodong" uniqKey="Rong G" first="Guodong" last="Rong">Guodong Rong</name></region><name sortKey="Gu, Xianfeng" sort="Gu, Xianfeng" uniqKey="Gu X" first="Xianfeng" last="Gu">Xianfeng Gu</name><name sortKey="Guo, Xiaohu" sort="Guo, Xiaohu" uniqKey="Guo X" first="Xiaohu" last="Guo">Xiaohu Guo</name><name sortKey="Yin, Xaotian" sort="Yin, Xaotian" uniqKey="Yin X" first="Xaotian" last="Yin">Xaotian Yin</name></country><country name="France"><region name="Grand Est"><name sortKey="Liu, Yang" sort="Liu, Yang" uniqKey="Liu Y" first="Yang" last="Liu">Yang Liu</name></region></country><country name="Hong Kong"><noRegion><name sortKey="Wang, Wenping" sort="Wang, Wenping" uniqKey="Wang W" first="Wenping" last="Wang">Wenping Wang</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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