Parallelization of an ultrasound reconstruction algorithm for non destructive testing on multicore CPU and GPU

Antoine Pedron, L. Lacassagne, F. Bimbard, S. Berre
{"title":"Parallelization of an ultrasound reconstruction algorithm for non destructive testing on multicore CPU and GPU","authors":"Antoine Pedron, L. Lacassagne, F. Bimbard, S. Berre","doi":"10.1109/DASIP.2011.6136904","DOIUrl":null,"url":null,"abstract":"The CIVA software platform developed by CEA-LIST offers various simulation and data processing modules dedicated to non-destructive testing (NDT). In particular, ultrasonic imaging and reconstruction tools are proposed, in the purpose of localizing echoes and identifying and sizing the detected defects. Because of the complexity of data processed, computation time is now a limitation for the optimal use of available information. In this article, we present performance results on parallelization of one computationally heavy algorithm on general purpose processors (GPP) and graphic processing units (GPU). GPU implementation makes an intensive use of atomic intrinsics. Compared to initial GPP implementation, optimized GPP implementation runs up to ×116 faster and GPU implementation up to ×631. This shows that, even with irregular workloads, combining software optimization and hardware improvements, GPU give high performance.","PeriodicalId":199500,"journal":{"name":"Proceedings of the 2011 Conference on Design & Architectures for Signal & Image Processing (DASIP)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2011 Conference on Design & Architectures for Signal & Image Processing (DASIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DASIP.2011.6136904","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

The CIVA software platform developed by CEA-LIST offers various simulation and data processing modules dedicated to non-destructive testing (NDT). In particular, ultrasonic imaging and reconstruction tools are proposed, in the purpose of localizing echoes and identifying and sizing the detected defects. Because of the complexity of data processed, computation time is now a limitation for the optimal use of available information. In this article, we present performance results on parallelization of one computationally heavy algorithm on general purpose processors (GPP) and graphic processing units (GPU). GPU implementation makes an intensive use of atomic intrinsics. Compared to initial GPP implementation, optimized GPP implementation runs up to ×116 faster and GPU implementation up to ×631. This shows that, even with irregular workloads, combining software optimization and hardware improvements, GPU give high performance.
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
基于多核CPU和GPU的无损检测超声重构并行化算法
由CEA-LIST开发的CIVA软件平台提供各种专用于无损检测(NDT)的模拟和数据处理模块。特别提出了超声成像和重建工具,目的是定位回波,识别和确定检测到的缺陷。由于处理数据的复杂性,计算时间现在是对可用信息的最佳利用的限制。在本文中,我们展示了在通用处理器(GPP)和图形处理单元(GPU)上并行化一种计算量大的算法的性能结果。GPU的实现大量使用了原子特性。与初始GPP实现相比,优化后的GPP实现速度最快×116, GPU实现速度最快×631。这表明,即使在不规则的工作负载下,结合软件优化和硬件改进,GPU也能提供高性能。
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
Performance evaluation of an automotive distributed architecture based on HPAV communication protocol using a transaction level modeling approach FPGA dynamic reconfiguration using the RVC technology: Inverse quantization case study Design of a processor optimized for syntax parsing in video decoders A framework for the design of reconfigurable fault tolerant architectures Fast and accurate hybrid power estimation methodology for embedded systems
×
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