A parallel implementation of RFT on GPU

Zhe-ran Shang, Xiansi Tan, Zhiguo Qu, Hong Wang
{"title":"A parallel implementation of RFT on GPU","authors":"Zhe-ran Shang, Xiansi Tan, Zhiguo Qu, Hong Wang","doi":"10.1109/RADAR.2016.8059450","DOIUrl":null,"url":null,"abstract":"Radon Fourier Transform (RFT) is a kind of generalized MTD, which can integrate along the track of targets. However, it is not easy for RFT to be engineered due to the calculating burden. Aiming at this problem, a kind of RFT parallelization strategy is put forward based on GPU and CUDA. Through experimental verification, the execution time of RFT on GPU platform proved a great speedup compared with that of RFT and fast RFT on CPU. In addition, it suggests in the results that the execution time can be as fast as MTD when RFT results are saved in global memory.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 CIE International Conference on Radar (RADAR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RADAR.2016.8059450","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Radon Fourier Transform (RFT) is a kind of generalized MTD, which can integrate along the track of targets. However, it is not easy for RFT to be engineered due to the calculating burden. Aiming at this problem, a kind of RFT parallelization strategy is put forward based on GPU and CUDA. Through experimental verification, the execution time of RFT on GPU platform proved a great speedup compared with that of RFT and fast RFT on CPU. In addition, it suggests in the results that the execution time can be as fast as MTD when RFT results are saved in global memory.
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
RFT在GPU上的并行实现
Radon傅里叶变换(RFT)是一种广义MTD,可以沿目标轨迹进行积分。然而,由于计算量大,RFT的工程化并不容易。针对这一问题,提出了一种基于GPU和CUDA的RFT并行化策略。通过实验验证,RFT在GPU平台上的执行速度比在CPU平台上的RFT和快速RFT有很大的提高。此外,结果表明,当RFT结果保存在全局内存中时,执行时间可以与MTD一样快。
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
Refined SAR image segmentation algorithm based on K-means clustering Extended PGA processing of high resolution airborne SAR imagery reconstructed via backprojection algorithm Design of Ka-band practical waveguide duplexer Adaptive structured detector and performance assessment in training-limited cases Vivaldi antenna for railway cutting monitoring
×
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