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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)

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%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/10.20385/1860-2037/11.2014.6
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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"
}
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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  -
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Wordbib

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<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 &quot;Sweep and Prune&quot; 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>
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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.
ER
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Mods

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    <title>Collision Detection: Broad Phase Adaptation from Multi-Core to Multi-GPU Architecture</title>
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  <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>
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  <identifier type="uri">http://nbn-resolving.de/urn:nbn:de:0009-6-39893</identifier>
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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
swd
  • 4800907-6, Kollisionserkennung
  • 4582114-8, Graphikprozessor
  • 4532701-4, Hochleistungsrechnen
DDC 004
Rights DPPL
URN: urn:nbn:de:0009-6-39893
DOI https://doi.org/10.20385/1860-2037/11.2014.6