Adrenaline: Pinpointing and reining in tail queries with quick voltage boosting

Chang-Hong Hsu, Yunqi Zhang, M. Laurenzano, David Meisner, T. Wenisch, Jason Mars, Lingjia Tang, R. Dreslinski
{"title":"Adrenaline: Pinpointing and reining in tail queries with quick voltage boosting","authors":"Chang-Hong Hsu, Yunqi Zhang, M. Laurenzano, David Meisner, T. Wenisch, Jason Mars, Lingjia Tang, R. Dreslinski","doi":"10.1109/HPCA.2015.7056039","DOIUrl":null,"url":null,"abstract":"Reducing the long tail of the query latency distribution in modern warehouse scale computers is critical for improving performance and quality of service of workloads such as Web Search and Memcached. Traditional turbo boost increases a processor's voltage and frequency during a coarse-grain sliding window, boosting all queries that are processed during that window. However, the inability of such a technique to pinpoint tail queries for boosting limits its tail reduction benefit. In this work, we propose Adrenaline, an approach to leverage finer granularity, 10's of nanoseconds, voltage boosting to effectively rein in the tail latency with query-level precision. Two key insights underlie this work. First, emerging finer granularity voltage/frequency boosting is an enabling mechanism for intelligent allocation of the power budget to precisely boost only the queries that contribute to the tail latency; and second, per-query characteristics can be used to design indicators for proactively pinpointing these queries, triggering boosting accordingly. Based on these insights, Adrenaline effectively pinpoints and boosts queries that are likely to increase the tail distribution and can reap more benefit from the voltage/frequency boost. By evaluating under various workload configurations, we demonstrate the effectiveness of our methodology. We achieve up to a 2.50x tail latency improvement for Memcached and up to a 3.03x for Web Search over coarse-grained DVFS given a fixed boosting power budget. When optimizing for energy reduction, Adrenaline achieves up to a 1.81x improvement for Memcached and up to a 1.99x for Web Search over coarse-grained DVFS.","PeriodicalId":6593,"journal":{"name":"2015 IEEE 21st International Symposium on High Performance Computer Architecture (HPCA)","volume":"15 1","pages":"271-282"},"PeriodicalIF":0.0000,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"109","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE 21st International Symposium on High Performance Computer Architecture (HPCA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HPCA.2015.7056039","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 109

Abstract

Reducing the long tail of the query latency distribution in modern warehouse scale computers is critical for improving performance and quality of service of workloads such as Web Search and Memcached. Traditional turbo boost increases a processor's voltage and frequency during a coarse-grain sliding window, boosting all queries that are processed during that window. However, the inability of such a technique to pinpoint tail queries for boosting limits its tail reduction benefit. In this work, we propose Adrenaline, an approach to leverage finer granularity, 10's of nanoseconds, voltage boosting to effectively rein in the tail latency with query-level precision. Two key insights underlie this work. First, emerging finer granularity voltage/frequency boosting is an enabling mechanism for intelligent allocation of the power budget to precisely boost only the queries that contribute to the tail latency; and second, per-query characteristics can be used to design indicators for proactively pinpointing these queries, triggering boosting accordingly. Based on these insights, Adrenaline effectively pinpoints and boosts queries that are likely to increase the tail distribution and can reap more benefit from the voltage/frequency boost. By evaluating under various workload configurations, we demonstrate the effectiveness of our methodology. We achieve up to a 2.50x tail latency improvement for Memcached and up to a 3.03x for Web Search over coarse-grained DVFS given a fixed boosting power budget. When optimizing for energy reduction, Adrenaline achieves up to a 1.81x improvement for Memcached and up to a 1.99x for Web Search over coarse-grained DVFS.
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
肾上腺素:精确定位和控制尾部查询与快速电压提升
在现代仓库规模的计算机中,减少查询延迟分布的长尾对于提高Web Search和Memcached等工作负载的性能和服务质量至关重要。传统的turbo boost在粗粒度滑动窗口期间增加处理器的电压和频率,从而提高在该窗口期间处理的所有查询。然而,这种技术无法精确定位尾部查询以进行提升,这限制了它的尾部减少效益。在这项工作中,我们提出了Adrenaline,这是一种利用更细粒度(10纳秒)的电压提升方法,以查询级精度有效地控制尾部延迟。这项工作的基础是两个关键的见解。首先,新兴的更细粒度电压/频率提升是一种智能分配功率预算的启用机制,可以精确地只提升导致尾部延迟的查询;其次,每个查询的特征可以用来设计指示器,以主动定位这些查询,从而触发相应的提升。基于这些见解,Adrenaline能够有效地定位并提升那些可能会增加尾部分布的查询,并能够从电压/频率提升中获得更多收益。通过在各种工作负载配置下进行评估,我们证明了我们的方法的有效性。对于Memcached,我们实现了高达2.50倍的尾部延迟改进,对于粗粒度DVFS,我们实现了高达3.03倍的尾部延迟改进。在优化能量减少时,Adrenaline在Memcached上实现了1.81倍的改进,在粗粒度DVFS上实现了1.99倍的Web搜索改进。
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
Parameter Identification Inverse Problems of Partial Differential Equations Based on the Improved Gene Expression Programming High-Efficiency Realization of SRT Division on Ternary Optical Computers A Fast Training Method for Transductive Support Vector Machine in Semi-supervised Learning Performance Optimization of a DEM Simulation Framework on GPU Using a Stencil Model A Platform for Routine Development of Ternary Optical Computers
×
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