Citation and metadata
Recommended citation
Quentin Avril, Valérie Gouranton, and Bruno Arnaldi, Collision Detection: Broad Phase Adaptation from Multi-Core to Multi-GPU Architecture. Journal of Virtual Reality and Broadcasting, 11(2014), no. 6. (urn:nbn:de:0009-6-39893)
Download Citation
Endnote
%0 Journal Article %T Collision Detection: Broad Phase Adaptation from Multi-Core to Multi-GPU Architecture %A Avril, Quentin %A Gouranton, Valérie %A Arnaldi, Bruno %J Journal of Virtual Reality and Broadcasting %D 2014 %V 11(2014) %N 6 %@ 1860-2037 %F avril2014 %X We present in this paper several contributions on the collision detection optimization centered on hardware performance. We focus on the broad phase which is the first step of the collision detection process and propose three new ways of parallelization of the well-known Sweep and Prune algorithm. We first developed a multi-core model takes into account the number of available cores. Multi-core architecture enables us to distribute geometric computations with use of multi-threading. Critical writing section and threads idling have been minimized by introducing new data structures for each thread. Programming with directives, like OpenMP, appears to be a good compromise for code portability. We then proposed a new GPU-based algorithm also based on the "Sweep and Prune" that has been adapted to multi-GPU architectures. Our technique is based on a spatial subdivision method used to distribute computations among GPUs. Results show that significant speed-up can be obtained by passing from 1 to 4 GPUs in a large-scale environment. %L 004 %K Collision Detection %K GPGPU %K High Performance Computing %K Multi-CPU %R 10.20385/1860-2037/11.2014.6 %U http://nbn-resolving.de/urn:nbn:de:0009-6-39893 %U http://dx.doi.org/10.20385/1860-2037/11.2014.6Download
Bibtex
@Article{avril2014, author = "Avril, Quentin and Gouranton, Val{\'e}rie and Arnaldi, Bruno", title = "Collision Detection: Broad Phase Adaptation from Multi-Core to Multi-GPU Architecture", journal = "Journal of Virtual Reality and Broadcasting", year = "2014", volume = "11(2014)", number = "6", keywords = "Collision Detection; GPGPU; High Performance Computing; Multi-CPU", abstract = "We present in this paper several contributions on the collision detection optimization centered on hardware performance. We focus on the broad phase which is the first step of the collision detection process and propose three new ways of parallelization of the well-known Sweep and Prune algorithm. We first developed a multi-core model takes into account the number of available cores. Multi-core architecture enables us to distribute geometric computations with use of multi-threading. Critical writing section and threads idling have been minimized by introducing new data structures for each thread. Programming with directives, like OpenMP, appears to be a good compromise for code portability. We then proposed a new GPU-based algorithm also based on the ``Sweep and Prune'' that has been adapted to multi-GPU architectures. Our technique is based on a spatial subdivision method used to distribute computations among GPUs. Results show that significant speed-up can be obtained by passing from 1 to 4 GPUs in a large-scale environment.", issn = "1860-2037", doi = "10.20385/1860-2037/11.2014.6", url = "http://nbn-resolving.de/urn:nbn:de:0009-6-39893" }Download
RIS
TY - JOUR AU - Avril, Quentin AU - Gouranton, Valérie AU - Arnaldi, Bruno PY - 2014 DA - 2014// TI - Collision Detection: Broad Phase Adaptation from Multi-Core to Multi-GPU Architecture JO - Journal of Virtual Reality and Broadcasting VL - 11(2014) IS - 6 KW - Collision Detection KW - GPGPU KW - High Performance Computing KW - Multi-CPU AB - We present in this paper several contributions on the collision detection optimization centered on hardware performance. We focus on the broad phase which is the first step of the collision detection process and propose three new ways of parallelization of the well-known Sweep and Prune algorithm. We first developed a multi-core model takes into account the number of available cores. Multi-core architecture enables us to distribute geometric computations with use of multi-threading. Critical writing section and threads idling have been minimized by introducing new data structures for each thread. Programming with directives, like OpenMP, appears to be a good compromise for code portability. We then proposed a new GPU-based algorithm also based on the "Sweep and Prune" that has been adapted to multi-GPU architectures. Our technique is based on a spatial subdivision method used to distribute computations among GPUs. Results show that significant speed-up can be obtained by passing from 1 to 4 GPUs in a large-scale environment. SN - 1860-2037 UR - http://nbn-resolving.de/urn:nbn:de:0009-6-39893 DO - 10.20385/1860-2037/11.2014.6 ID - avril2014 ER -Download
Wordbib
<?xml version="1.0" encoding="UTF-8"?> <b:Sources SelectedStyle="" xmlns:b="http://schemas.openxmlformats.org/officeDocument/2006/bibliography" xmlns="http://schemas.openxmlformats.org/officeDocument/2006/bibliography" > <b:Source> <b:Tag>avril2014</b:Tag> <b:SourceType>ArticleInAPeriodical</b:SourceType> <b:Year>2014</b:Year> <b:PeriodicalTitle>Journal of Virtual Reality and Broadcasting</b:PeriodicalTitle> <b:Volume>11(2014)</b:Volume> <b:Issue>6</b:Issue> <b:Url>http://nbn-resolving.de/urn:nbn:de:0009-6-39893</b:Url> <b:Url>http://dx.doi.org/10.20385/1860-2037/11.2014.6</b:Url> <b:Author> <b:Author><b:NameList> <b:Person><b:Last>Avril</b:Last><b:First>Quentin</b:First></b:Person> <b:Person><b:Last>Gouranton</b:Last><b:First>Valérie</b:First></b:Person> <b:Person><b:Last>Arnaldi</b:Last><b:First>Bruno</b:First></b:Person> </b:NameList></b:Author> </b:Author> <b:Title>Collision Detection: Broad Phase Adaptation from Multi-Core to Multi-GPU Architecture</b:Title> <b:Comments>We present in this paper several contributions on the collision detection optimization centered on hardware performance. We focus on the broad phase which is the first step of the collision detection process and propose three new ways of parallelization of the well-known Sweep and Prune algorithm. We first developed a multi-core model takes into account the number of available cores. Multi-core architecture enables us to distribute geometric computations with use of multi-threading. Critical writing section and threads idling have been minimized by introducing new data structures for each thread. Programming with directives, like OpenMP, appears to be a good compromise for code portability. We then proposed a new GPU-based algorithm also based on the "Sweep and Prune" that has been adapted to multi-GPU architectures. Our technique is based on a spatial subdivision method used to distribute computations among GPUs. Results show that significant speed-up can be obtained by passing from 1 to 4 GPUs in a large-scale environment.</b:Comments> </b:Source> </b:Sources>Download
ISI
PT Journal AU Avril, Q Gouranton, V Arnaldi, B TI Collision Detection: Broad Phase Adaptation from Multi-Core to Multi-GPU Architecture SO Journal of Virtual Reality and Broadcasting PY 2014 VL 11(2014) IS 6 DI 10.20385/1860-2037/11.2014.6 DE Collision Detection; GPGPU; High Performance Computing; Multi-CPU AB We present in this paper several contributions on the collision detection optimization centered on hardware performance. We focus on the broad phase which is the first step of the collision detection process and propose three new ways of parallelization of the well-known Sweep and Prune algorithm. We first developed a multi-core model takes into account the number of available cores. Multi-core architecture enables us to distribute geometric computations with use of multi-threading. Critical writing section and threads idling have been minimized by introducing new data structures for each thread. Programming with directives, like OpenMP, appears to be a good compromise for code portability. We then proposed a new GPU-based algorithm also based on the "Sweep and Prune" that has been adapted to multi-GPU architectures. Our technique is based on a spatial subdivision method used to distribute computations among GPUs. Results show that significant speed-up can be obtained by passing from 1 to 4 GPUs in a large-scale environment. ERDownload
Mods
<mods> <titleInfo> <title>Collision Detection: Broad Phase Adaptation from Multi-Core to Multi-GPU Architecture</title> </titleInfo> <name type="personal"> <namePart type="family">Avril</namePart> <namePart type="given">Quentin</namePart> </name> <name type="personal"> <namePart type="family">Gouranton</namePart> <namePart type="given">Valérie</namePart> </name> <name type="personal"> <namePart type="family">Arnaldi</namePart> <namePart type="given">Bruno</namePart> </name> <abstract>We present in this paper several contributions on the collision detection optimization centered on hardware performance. We focus on the broad phase which is the first step of the collision detection process and propose three new ways of parallelization of the well-known Sweep and Prune algorithm. We first developed a multi-core model takes into account the number of available cores. Multi-core architecture enables us to distribute geometric computations with use of multi-threading. Critical writing section and threads idling have been minimized by introducing new data structures for each thread. Programming with directives, like OpenMP, appears to be a good compromise for code portability. We then proposed a new GPU-based algorithm also based on the "Sweep and Prune" that has been adapted to multi-GPU architectures. Our technique is based on a spatial subdivision method used to distribute computations among GPUs. Results show that significant speed-up can be obtained by passing from 1 to 4 GPUs in a large-scale environment.</abstract> <subject> <topic>Collision Detection</topic> <topic>GPGPU</topic> <topic>High Performance Computing</topic> <topic>Multi-CPU</topic> </subject> <classification authority="ddc">004</classification> <relatedItem type="host"> <genre authority="marcgt">periodical</genre> <genre>academic journal</genre> <titleInfo> <title>Journal of Virtual Reality and Broadcasting</title> </titleInfo> <part> <detail type="volume"> <number>11(2014)</number> </detail> <detail type="issue"> <number>6</number> </detail> <date>2014</date> </part> </relatedItem> <identifier type="issn">1860-2037</identifier> <identifier type="urn">urn:nbn:de:0009-6-39893</identifier> <identifier type="doi">10.20385/1860-2037/11.2014.6</identifier> <identifier type="uri">http://nbn-resolving.de/urn:nbn:de:0009-6-39893</identifier> <identifier type="citekey">avril2014</identifier> </mods>Download
Full Metadata
Bibliographic Citation | JVRB, 11(2014), no. 6. |
---|---|
Title |
Collision Detection: Broad Phase Adaptation from Multi-Core to Multi-GPU Architecture (eng) |
Author | Quentin Avril, Valérie Gouranton, Bruno Arnaldi |
Language | eng |
Abstract | We present in this paper several contributions on the collision detection optimization centered on hardware performance. We focus on the broad phase which is the first step of the collision detection process and propose three new ways of parallelization of the well-known Sweep and Prune algorithm. We first developed a multi-core model takes into account the number of available cores. Multi-core architecture enables us to distribute geometric computations with use of multi-threading. Critical writing section and threads idling have been minimized by introducing new data structures for each thread. Programming with directives, like OpenMP, appears to be a good compromise for code portability. We then proposed a new GPU-based algorithm also based on the "Sweep and Prune" that has been adapted to multi-GPU architectures. Our technique is based on a spatial subdivision method used to distribute computations among GPUs. Results show that significant speed-up can be obtained by passing from 1 to 4 GPUs in a large-scale environment. |
Subject | Collision Detection, GPGPU, High Performance Computing, Multi-CPU |
Classified Subjects |
|
DDC | 004 |
Rights | DPPL |
URN: | urn:nbn:de:0009-6-39893 |
DOI | https://doi.org/10.20385/1860-2037/11.2014.6 |