具有软错误弹性的CPU-GPU混合双对角线缩减

Yulu Jia, P. Luszczek, G. Bosilca, J. Dongarra
{"title":"具有软错误弹性的CPU-GPU混合双对角线缩减","authors":"Yulu Jia, P. Luszczek, G. Bosilca, J. Dongarra","doi":"10.1145/2530268.2530270","DOIUrl":null,"url":null,"abstract":"Soft errors pose a real challenge to applications running on modern hardware as the feature size becomes smaller and the integration density increases for both the modern processors and the memory chips. Soft errors manifest themselves as bit-flips that alter the user value, and numerical software is a category of software that is sensitive to such data changes. In this paper, we present a design of a bidiagonal reduction algorithm that is resilient to soft errors, and we also describe its implementation on hybrid CPU-GPU architectures. Our fault-tolerant algorithm employs Algorithm Based Fault Tolerance, combined with reverse computation, to detect, locate, and correct soft errors. The tests were performed on a Sandy Bridge CPU coupled with an NVIDIA Kepler GPU. The included experiments show that our resilient bidiagonal reduction algorithm adds very little overhead compared to the error-prone code. At matrix size 10110 x 10110, our algorithm only has a performance overhead of 1.085% when one error occurs, and 0.354% when no errors occur.","PeriodicalId":259517,"journal":{"name":"ACM SIGPLAN Symposium on Scala","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"CPU-GPU hybrid bidiagonal reduction with soft error resilience\",\"authors\":\"Yulu Jia, P. Luszczek, G. Bosilca, J. Dongarra\",\"doi\":\"10.1145/2530268.2530270\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Soft errors pose a real challenge to applications running on modern hardware as the feature size becomes smaller and the integration density increases for both the modern processors and the memory chips. Soft errors manifest themselves as bit-flips that alter the user value, and numerical software is a category of software that is sensitive to such data changes. In this paper, we present a design of a bidiagonal reduction algorithm that is resilient to soft errors, and we also describe its implementation on hybrid CPU-GPU architectures. Our fault-tolerant algorithm employs Algorithm Based Fault Tolerance, combined with reverse computation, to detect, locate, and correct soft errors. The tests were performed on a Sandy Bridge CPU coupled with an NVIDIA Kepler GPU. The included experiments show that our resilient bidiagonal reduction algorithm adds very little overhead compared to the error-prone code. At matrix size 10110 x 10110, our algorithm only has a performance overhead of 1.085% when one error occurs, and 0.354% when no errors occur.\",\"PeriodicalId\":259517,\"journal\":{\"name\":\"ACM SIGPLAN Symposium on Scala\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM SIGPLAN Symposium on Scala\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2530268.2530270\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM SIGPLAN Symposium on Scala","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2530268.2530270","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

摘要

随着功能尺寸越来越小,现代处理器和内存芯片的集成密度越来越高,软错误对在现代硬件上运行的应用程序构成了真正的挑战。软错误表现为改变用户值的位翻转,而数字软件是一类对此类数据变化敏感的软件。在本文中,我们提出了一种对软错误具有弹性的双对角约简算法的设计,并描述了其在混合CPU-GPU架构上的实现。我们的容错算法采用基于算法的容错,结合反向计算来检测、定位和纠正软错误。测试是在Sandy Bridge CPU和NVIDIA Kepler GPU上进行的。所包含的实验表明,与容易出错的代码相比,我们的弹性双对角约简算法增加的开销非常小。在矩阵大小为10110 x 10110的情况下,当出现一个错误时,我们的算法的性能开销仅为1.085%,当没有错误时,性能开销为0.354%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
CPU-GPU hybrid bidiagonal reduction with soft error resilience
Soft errors pose a real challenge to applications running on modern hardware as the feature size becomes smaller and the integration density increases for both the modern processors and the memory chips. Soft errors manifest themselves as bit-flips that alter the user value, and numerical software is a category of software that is sensitive to such data changes. In this paper, we present a design of a bidiagonal reduction algorithm that is resilient to soft errors, and we also describe its implementation on hybrid CPU-GPU architectures. Our fault-tolerant algorithm employs Algorithm Based Fault Tolerance, combined with reverse computation, to detect, locate, and correct soft errors. The tests were performed on a Sandy Bridge CPU coupled with an NVIDIA Kepler GPU. The included experiments show that our resilient bidiagonal reduction algorithm adds very little overhead compared to the error-prone code. At matrix size 10110 x 10110, our algorithm only has a performance overhead of 1.085% when one error occurs, and 0.354% when no errors occur.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A scalable randomized least squares solver for dense overdetermined systems A parallel ensemble Kalman filter implementation based on modified Cholesky decomposition Mixed-precision block gram Schmidt orthogonalization Weighted dynamic scheduling with many parallelism grains for offloading of numerical workloads to multiple varied accelerators On efficient Monte Carlo preconditioners and hybrid Monte Carlo methods for linear algebra
×
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