A microbenchmark to study GPU performance models

V. Volkov
{"title":"A microbenchmark to study GPU performance models","authors":"V. Volkov","doi":"10.1145/3178487.3178536","DOIUrl":null,"url":null,"abstract":"Basic microarchitectural features of NVIDIA GPUs have been stable for a decade, and many analytic solutions were proposed to model their performance. We present a way to review, systematize, and evaluate these approaches by using a microbenchmark. In this manner, we produce a brief algebraic summary of key elements of selected performance models, identify patterns in their design, and highlight their previously unknown limitations. Also, we identify a potentially superior method for estimating performance based on classical work.","PeriodicalId":193776,"journal":{"name":"Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3178487.3178536","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

Abstract

Basic microarchitectural features of NVIDIA GPUs have been stable for a decade, and many analytic solutions were proposed to model their performance. We present a way to review, systematize, and evaluate these approaches by using a microbenchmark. In this manner, we produce a brief algebraic summary of key elements of selected performance models, identify patterns in their design, and highlight their previously unknown limitations. Also, we identify a potentially superior method for estimating performance based on classical work.
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
一个研究GPU性能模型的微基准
NVIDIA gpu的基本微架构特征已经稳定了十年,并且提出了许多分析解决方案来模拟其性能。我们提出了一种通过使用微基准来审查、系统化和评估这些方法的方法。通过这种方式,我们对所选性能模型的关键元素进行了简要的代数总结,确定了其设计中的模式,并强调了它们以前未知的局限性。此外,我们还确定了一种潜在的基于经典工作的性能评估方法。
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
Graph partitioning applied to DAG scheduling to reduce NUMA effects Juggler: a dependence-aware task-based execution framework for GPUs Performance modeling for GPUs using abstract kernel emulation Automated code acceleration targeting heterogeneous openCL devices Layrub: layer-centric GPU memory reuse and data migration in extreme-scale deep learning systems
×
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