Design and Analysis of a 32-bit Embedded High-Performance Cluster Optimized for Energy and Performance

Michael F. Cloutier, Chad Paradis, Vincent M. Weaver
{"title":"Design and Analysis of a 32-bit Embedded High-Performance Cluster Optimized for Energy and Performance","authors":"Michael F. Cloutier, Chad Paradis, Vincent M. Weaver","doi":"10.1109/Co-HPC.2014.7","DOIUrl":null,"url":null,"abstract":"A growing number of supercomputers are being built using processors with low-power embedded ancestry, rather than traditional high-performance cores. In order to evaluate this approach we investigate the energy and performance tradeoffs found with ten different 32-bit ARM development boards while running the HPL Linpack and STREAM benchmarks.Based on these results (and other practical concerns) we chose the Raspberry Pi as a basis for a power-aware embedded cluster computing testbed. Each node of the cluster is instrumented with power measurement circuitry so that detailed cluster-wide power measurements can be obtained, enabling power / performance co-design experiments.While our cluster lags recent x86 machines in performance, the power, visualization, and thermal features make it an excellent low-cost platform for education and experimentation.","PeriodicalId":136638,"journal":{"name":"2014 Hardware-Software Co-Design for High Performance Computing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"37","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 Hardware-Software Co-Design for High Performance Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/Co-HPC.2014.7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 37

Abstract

A growing number of supercomputers are being built using processors with low-power embedded ancestry, rather than traditional high-performance cores. In order to evaluate this approach we investigate the energy and performance tradeoffs found with ten different 32-bit ARM development boards while running the HPL Linpack and STREAM benchmarks.Based on these results (and other practical concerns) we chose the Raspberry Pi as a basis for a power-aware embedded cluster computing testbed. Each node of the cluster is instrumented with power measurement circuitry so that detailed cluster-wide power measurements can be obtained, enabling power / performance co-design experiments.While our cluster lags recent x86 machines in performance, the power, visualization, and thermal features make it an excellent low-cost platform for education and experimentation.
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
能源与性能优化的32位嵌入式高性能集群的设计与分析
越来越多的超级计算机正在使用低功耗嵌入式处理器,而不是传统的高性能内核。为了评估这种方法,我们研究了在运行HPL Linpack和STREAM基准测试时,在十个不同的32位ARM开发板上发现的能量和性能权衡。基于这些结果(以及其他实际问题),我们选择Raspberry Pi作为功耗感知嵌入式集群计算测试平台的基础。集群的每个节点都配备了功率测量电路,以便可以获得详细的集群范围内的功率测量,从而实现功率/性能协同设计实验。虽然我们的集群在性能上落后于最近的x86机器,但其强大的功能、可视化和散热特性使其成为教育和实验的优秀低成本平台。
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
Abstract Machine Models and Proxy Architectures for Exascale Computing Design and Analysis of a 32-bit Embedded High-Performance Cluster Optimized for Energy and Performance mPPM, Viewed as a Co-Design Effort An Implementation of Block Conjugate Gradient Algorithm on CPU-GPU Processors Performance and Energy Evaluation of CoMD on Intel Xeon Phi Co-processors
×
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