One-Way Wave Equation Migration at Scale on GPUs Using Directive Based Programming

Kshitij Mehta, M. Hugues, Oscar R. Hernandez, D. Bernholdt, H. Calandra
{"title":"One-Way Wave Equation Migration at Scale on GPUs Using Directive Based Programming","authors":"Kshitij Mehta, M. Hugues, Oscar R. Hernandez, D. Bernholdt, H. Calandra","doi":"10.1109/IPDPS.2017.82","DOIUrl":null,"url":null,"abstract":"One-Way Wave Equation Migration (OWEM) is a depth migration algorithm used for seismic imaging. A parallel version of this algorithm is widely implemented using MPI. Heterogenous architectures that use GPUs have become popular in the Top 500 because of their performance/power ratio. In this paper, we discuss the methodology and code transformations used to port OWEM to GPUs using OpenACC, along with the code changes needed for scaling the application up to 18,400 GPUs (more than 98%) of the Titan leadership class supercomputer at Oak Ridget National Laboratory. For the individual OpenACC kernels, we achieved an average of 3X speedup on a test dataset using one GPU as compared with an 8-core Intel Sandy Bridge CPU. The application was then run at large scale on the Titan supercomputer achieving a peak of 1.2 petaflops using an average of 5.5 megawatts. After porting the application to GPUs, we discuss how we dealt with other challenges of running at scale such as the application becoming more I/O bound and prone to silent errors. We believe this work will serve as valuable proof that directive-based programming models are a viable option for scaling HPC applications to heterogenous architectures.","PeriodicalId":209524,"journal":{"name":"2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPDPS.2017.82","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

One-Way Wave Equation Migration (OWEM) is a depth migration algorithm used for seismic imaging. A parallel version of this algorithm is widely implemented using MPI. Heterogenous architectures that use GPUs have become popular in the Top 500 because of their performance/power ratio. In this paper, we discuss the methodology and code transformations used to port OWEM to GPUs using OpenACC, along with the code changes needed for scaling the application up to 18,400 GPUs (more than 98%) of the Titan leadership class supercomputer at Oak Ridget National Laboratory. For the individual OpenACC kernels, we achieved an average of 3X speedup on a test dataset using one GPU as compared with an 8-core Intel Sandy Bridge CPU. The application was then run at large scale on the Titan supercomputer achieving a peak of 1.2 petaflops using an average of 5.5 megawatts. After porting the application to GPUs, we discuss how we dealt with other challenges of running at scale such as the application becoming more I/O bound and prone to silent errors. We believe this work will serve as valuable proof that directive-based programming models are a viable option for scaling HPC applications to heterogenous architectures.
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
基于指令编程的gpu单向波动方程大规模迁移
单向波动方程偏移(OWEM)是一种用于地震成像的深度偏移算法。该算法的并行版本被广泛使用MPI实现。使用gpu的异构架构由于其性能/功耗比而在500强中变得流行。在本文中,我们讨论了使用OpenACC将OWEM移植到gpu的方法和代码转换,以及将应用程序扩展到橡树岭国家实验室的泰坦领导级超级计算机的18,400个gpu(超过98%)所需的代码更改。对于单个OpenACC内核,我们在使用一个GPU的测试数据集上实现了与8核英特尔Sandy Bridge CPU相比平均3倍的加速。然后,该应用程序在泰坦超级计算机上大规模运行,达到每秒1.2千万亿次的峰值,平均使用5.5兆瓦的功率。在将应用程序移植到gpu之后,我们将讨论如何处理大规模运行的其他挑战,例如应用程序变得越来越受I/O限制,并且容易出现无声错误。我们相信这项工作将作为有价值的证据,证明基于指令的编程模型是将HPC应用程序扩展到异构架构的可行选择。
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
Capability Models for Manycore Memory Systems: A Case-Study with Xeon Phi KNL Toucan — A Translator for Communication Tolerant MPI Applications Production Hardware Overprovisioning: Real-World Performance Optimization Using an Extensible Power-Aware Resource Management Framework Approximation Proofs of a Fast and Efficient List Scheduling Algorithm for Task-Based Runtime Systems on Multicores and GPUs Dynamic Memory-Aware Task-Tree Scheduling
×
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