一种评估紧密集成和分解加速体系结构的方法

Taylor L. Groves, C. Daley, Rahulkumar Gayatri, H. Nam, Nan Ding, Lenny Oliker, N. Wright, Samuel Williams
{"title":"一种评估紧密集成和分解加速体系结构的方法","authors":"Taylor L. Groves, C. Daley, Rahulkumar Gayatri, H. Nam, Nan Ding, Lenny Oliker, N. Wright, Samuel Williams","doi":"10.1109/PMBS56514.2022.00012","DOIUrl":null,"url":null,"abstract":"Tighter integration of computational resources can foster superior application performance by mitigating communication bottlenecks. Unfortunately, not every application can use every compute or accelerator all the time. As a result, co-locating resources often leads to under-utilization of resources. To mitigate this challenge, architects have proposed disaggregation and ad hoc pooling of computational resources. In the next five years, HPC system architects will be presented with a spectrum of accelerated solutions ranging from tightly coupled, single package APUs to a sea of disaggregated GPUs interconnected by a global network. In this paper, we detail NEthing, our methodology and tool for evaluating the potential performance implications of such diverse architectural paradigms. We demonstrate our methodology on today’s and projected 2026 technologies for three distinct workloads: a compute-intensive kernel, a tightly-coupled HPC simulation, and an ensemble of loosely-coupled HPC simulations. Our results leverage NEthing to quantify the increased utilization disaggregated systems must achieve in order to match superior performance of APUs and on-board GPUs.","PeriodicalId":321991,"journal":{"name":"2022 IEEE/ACM International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Methodology for Evaluating Tightly-integrated and Disaggregated Accelerated Architectures\",\"authors\":\"Taylor L. Groves, C. Daley, Rahulkumar Gayatri, H. Nam, Nan Ding, Lenny Oliker, N. Wright, Samuel Williams\",\"doi\":\"10.1109/PMBS56514.2022.00012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Tighter integration of computational resources can foster superior application performance by mitigating communication bottlenecks. Unfortunately, not every application can use every compute or accelerator all the time. As a result, co-locating resources often leads to under-utilization of resources. To mitigate this challenge, architects have proposed disaggregation and ad hoc pooling of computational resources. In the next five years, HPC system architects will be presented with a spectrum of accelerated solutions ranging from tightly coupled, single package APUs to a sea of disaggregated GPUs interconnected by a global network. In this paper, we detail NEthing, our methodology and tool for evaluating the potential performance implications of such diverse architectural paradigms. We demonstrate our methodology on today’s and projected 2026 technologies for three distinct workloads: a compute-intensive kernel, a tightly-coupled HPC simulation, and an ensemble of loosely-coupled HPC simulations. Our results leverage NEthing to quantify the increased utilization disaggregated systems must achieve in order to match superior performance of APUs and on-board GPUs.\",\"PeriodicalId\":321991,\"journal\":{\"name\":\"2022 IEEE/ACM International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS)\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE/ACM International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PMBS56514.2022.00012\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE/ACM International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PMBS56514.2022.00012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

计算资源的紧密集成可以通过减轻通信瓶颈来提高应用程序的性能。不幸的是,并不是每个应用程序都可以一直使用每个计算或加速器。因此,共同分配资源往往导致资源利用不足。为了减轻这一挑战,架构师提出了分解和特别的计算资源池。在未来五年内,HPC系统架构师将面临一系列加速解决方案,从紧密耦合的单封装apu到由全球网络互联的大量分解gpu。在本文中,我们详细介绍了NEthing,我们用于评估这些不同架构范例的潜在性能影响的方法和工具。我们针对三种不同的工作负载演示了我们的方法:计算密集型内核、紧密耦合的HPC模拟和松散耦合的HPC模拟。我们的结果利用NEthing来量化分解系统必须达到的利用率,以匹配apu和板载gpu的卓越性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Methodology for Evaluating Tightly-integrated and Disaggregated Accelerated Architectures
Tighter integration of computational resources can foster superior application performance by mitigating communication bottlenecks. Unfortunately, not every application can use every compute or accelerator all the time. As a result, co-locating resources often leads to under-utilization of resources. To mitigate this challenge, architects have proposed disaggregation and ad hoc pooling of computational resources. In the next five years, HPC system architects will be presented with a spectrum of accelerated solutions ranging from tightly coupled, single package APUs to a sea of disaggregated GPUs interconnected by a global network. In this paper, we detail NEthing, our methodology and tool for evaluating the potential performance implications of such diverse architectural paradigms. We demonstrate our methodology on today’s and projected 2026 technologies for three distinct workloads: a compute-intensive kernel, a tightly-coupled HPC simulation, and an ensemble of loosely-coupled HPC simulations. Our results leverage NEthing to quantify the increased utilization disaggregated systems must achieve in order to match superior performance of APUs and on-board GPUs.
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信