Parallel distributed, GPU-accelerated, advanced lighting calculations for large-scale volume visualization

Min Shih, S. Rizzi, J. Insley, T. Uram, V. Vishwanath, M. Hereld, M. Papka, K. Ma
{"title":"Parallel distributed, GPU-accelerated, advanced lighting calculations for large-scale volume visualization","authors":"Min Shih, S. Rizzi, J. Insley, T. Uram, V. Vishwanath, M. Hereld, M. Papka, K. Ma","doi":"10.1109/LDAV.2016.7874309","DOIUrl":null,"url":null,"abstract":"The benefits of applying advanced illumination models to volume visualization have been demonstrated by many researchers. For a parallel distributed, GPU computing environment, however, there is no efficient algorithm for scalable global illumination calculations. This paper presents a parallel, data-distributed and GPU-accelerated algorithm for volume rendering with advanced lighting. Our approach features tunable soft shadows for enhancing perception of complex spatial structures and relationships. For lighting calculations, our design effectively avoids data exchange among GPUs. Performance evaluation on a GPU cluster using up to 128 GPUs shows scalable rendering performance, with both the number of GPUs and volume data size.","PeriodicalId":148570,"journal":{"name":"2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/LDAV.2016.7874309","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12

Abstract

The benefits of applying advanced illumination models to volume visualization have been demonstrated by many researchers. For a parallel distributed, GPU computing environment, however, there is no efficient algorithm for scalable global illumination calculations. This paper presents a parallel, data-distributed and GPU-accelerated algorithm for volume rendering with advanced lighting. Our approach features tunable soft shadows for enhancing perception of complex spatial structures and relationships. For lighting calculations, our design effectively avoids data exchange among GPUs. Performance evaluation on a GPU cluster using up to 128 GPUs shows scalable rendering performance, with both the number of GPUs and volume data size.
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
并行分布式,gpu加速,先进的照明计算大规模体积可视化
许多研究人员已经证明了将先进的照明模型应用于体可视化的好处。然而,对于并行分布式GPU计算环境,没有有效的可扩展全局照明计算算法。提出了一种并行、数据分布式、gpu加速的高级光照体绘制算法。我们的方法具有可调的软阴影,以增强对复杂空间结构和关系的感知。对于光照计算,我们的设计有效地避免了gpu之间的数据交换。在使用多达128个GPU的GPU集群上进行性能评估,显示GPU数量和卷数据大小均可扩展的渲染性能。
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
Statistical projections for multi-dimensional visual data exploration Contour forests: Fast multi-threaded augmented contour trees Parallel peak pruning for scalable SMP contour tree computation Formal evaluation strategies for feature tracking In situ generated probability distribution functions for interactive post hoc visualization and analysis
×
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