GPU Accelerated Tandem Traversal of Blocked Bounding Volume Hierarchy Collision Detection for Multibody Dynamics

J. Damkjær, Kenny Erleben
{"title":"GPU Accelerated Tandem Traversal of Blocked Bounding Volume Hierarchy Collision Detection for Multibody Dynamics","authors":"J. Damkjær, Kenny Erleben","doi":"10.2312/PE/vriphys/vriphys09/115-124","DOIUrl":null,"url":null,"abstract":"delivered by EUROGRAPHICS D L IGITAL IBRARY www.eg.org diglib.eg.org Abstract The performance bottleneck of physics based animation is often the collision detection. It is well known by practitioners that the collision detection may consume more than half of the simulation time. In this work, we will introduce a novel approach for collision detection using bounding volume hierarchies. Our approach makes it possible to perform non-convex object versus non-convex object collision on the GPU, using tandem traversals of bounding volume hierarchies. Prior work only supports single traversals on GPUs. We introduce a blocked hierarchy data structure, using imaginary nodes and a simultaneous descend in the tandem traversal. The data structure design and traversal are highly specialized for exploiting the parallel threads in the NVIDIA GPUs. As proof-of-concept we demonstrate a GPU implementation for a multibody dynamics simulation, showing an approximate speedup factor of up to 8 compared to a CPU implementation.","PeriodicalId":446363,"journal":{"name":"Workshop on Virtual Reality Interactions and Physical Simulations","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Workshop on Virtual Reality Interactions and Physical Simulations","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2312/PE/vriphys/vriphys09/115-124","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

delivered by EUROGRAPHICS D L IGITAL IBRARY www.eg.org diglib.eg.org Abstract The performance bottleneck of physics based animation is often the collision detection. It is well known by practitioners that the collision detection may consume more than half of the simulation time. In this work, we will introduce a novel approach for collision detection using bounding volume hierarchies. Our approach makes it possible to perform non-convex object versus non-convex object collision on the GPU, using tandem traversals of bounding volume hierarchies. Prior work only supports single traversals on GPUs. We introduce a blocked hierarchy data structure, using imaginary nodes and a simultaneous descend in the tandem traversal. The data structure design and traversal are highly specialized for exploiting the parallel threads in the NVIDIA GPUs. As proof-of-concept we demonstrate a GPU implementation for a multibody dynamics simulation, showing an approximate speedup factor of up to 8 compared to a CPU implementation.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
GPU加速串联遍历阻塞边界体层次碰撞检测的多体动力学
摘要基于物理的动画的性能瓶颈往往是碰撞检测。从业人员都知道,碰撞检测可能会消耗一半以上的仿真时间。在这项工作中,我们将介绍一种使用边界体层次结构进行碰撞检测的新方法。我们的方法使得在GPU上执行非凸对象与非凸对象的碰撞成为可能,使用边界体层次结构的串联遍历。以前的工作只支持gpu上的单遍历。在串联遍历中,我们引入了一种采用虚节点和同时下降的阻塞分层数据结构。数据结构的设计和遍历是专门为利用NVIDIA gpu中的并行线程而设计的。作为概念验证,我们展示了多体动力学仿真的GPU实现,与CPU实现相比,显示了大约高达8的加速因子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
The Impact of Passive Head-Mounted Virtual Reality Devices on the Quality of EEG Signals Elasticity-based Clustering for Haptic Interaction with Heterogeneous Deformable Objects Implicit Mesh Generation using Volumetric Subdivision A New Force Model for Controllable Breaking Waves Vascular Neurosurgery Simulation with Bimanual Haptic Feedback
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1