减少大规模GPGPU内核的仿真时间

Jen-Cheng Huang, Lifeng Nai, Hyesoon Kim, H. Lee
{"title":"减少大规模GPGPU内核的仿真时间","authors":"Jen-Cheng Huang, Lifeng Nai, Hyesoon Kim, H. Lee","doi":"10.1109/IPDPS.2014.53","DOIUrl":null,"url":null,"abstract":"Architecture simulation for GPGPU kernels can take a significant amount of time, especially for large-scale GPGPU kernels. This paper presents TBPoint, an infrastructure based on profiling-based sampling for GPGPU kernels to reduce the cycle-level simulation time. Compared to existing approaches, TBPoint provides a flexible and architecture-independent way to take samples. For the evaluated 12 kernels, the geometric means of sampling errors of TBPoint, Ideal-Simpoint, and random sampling are 0.47%, 1.74%, and 7.95%, respectively, while the geometric means of the total sample size of TBPoint, Ideal-Simpoint, and random sampling are 2.6%, 5.4%, and 10%, respectively. TBPoint narrows the speed gap between hardware and GPGPU simulators, enabling more and more large-scale GPGPU kernels to be analyzed using detailed timing simulations.","PeriodicalId":309291,"journal":{"name":"2014 IEEE 28th International Parallel and Distributed Processing Symposium","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"TBPoint: Reducing Simulation Time for Large-Scale GPGPU Kernels\",\"authors\":\"Jen-Cheng Huang, Lifeng Nai, Hyesoon Kim, H. Lee\",\"doi\":\"10.1109/IPDPS.2014.53\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Architecture simulation for GPGPU kernels can take a significant amount of time, especially for large-scale GPGPU kernels. This paper presents TBPoint, an infrastructure based on profiling-based sampling for GPGPU kernels to reduce the cycle-level simulation time. Compared to existing approaches, TBPoint provides a flexible and architecture-independent way to take samples. For the evaluated 12 kernels, the geometric means of sampling errors of TBPoint, Ideal-Simpoint, and random sampling are 0.47%, 1.74%, and 7.95%, respectively, while the geometric means of the total sample size of TBPoint, Ideal-Simpoint, and random sampling are 2.6%, 5.4%, and 10%, respectively. TBPoint narrows the speed gap between hardware and GPGPU simulators, enabling more and more large-scale GPGPU kernels to be analyzed using detailed timing simulations.\",\"PeriodicalId\":309291,\"journal\":{\"name\":\"2014 IEEE 28th International Parallel and Distributed Processing Symposium\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-05-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE 28th International Parallel and Distributed Processing Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IPDPS.2014.53\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 28th International Parallel and Distributed Processing Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPDPS.2014.53","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 17

摘要

GPGPU内核的体系结构模拟可能会花费大量的时间,特别是对于大规模的GPGPU内核。TBPoint是一种基于性能分析采样的GPGPU内核架构,可减少周期级仿真时间。与现有的方法相比,TBPoint提供了一种灵活的、与体系结构无关的采样方法。对于评估的12个核,TBPoint、Ideal-Simpoint和随机抽样的抽样误差几何均值分别为0.47%、1.74%和7.95%,TBPoint、Ideal-Simpoint和随机抽样的总样本量几何均值分别为2.6%、5.4%和10%。TBPoint缩小了硬件和GPGPU模拟器之间的速度差距,使越来越多的大规模GPGPU内核能够使用详细的时序模拟进行分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TBPoint: Reducing Simulation Time for Large-Scale GPGPU Kernels
Architecture simulation for GPGPU kernels can take a significant amount of time, especially for large-scale GPGPU kernels. This paper presents TBPoint, an infrastructure based on profiling-based sampling for GPGPU kernels to reduce the cycle-level simulation time. Compared to existing approaches, TBPoint provides a flexible and architecture-independent way to take samples. For the evaluated 12 kernels, the geometric means of sampling errors of TBPoint, Ideal-Simpoint, and random sampling are 0.47%, 1.74%, and 7.95%, respectively, while the geometric means of the total sample size of TBPoint, Ideal-Simpoint, and random sampling are 2.6%, 5.4%, and 10%, respectively. TBPoint narrows the speed gap between hardware and GPGPU simulators, enabling more and more large-scale GPGPU kernels to be analyzed using detailed timing simulations.
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:481959085
Book学术官方微信