推测型Taskloop和OpenMP-for-Loop在硬件事务性内存上线程级推测的性能比较

Juan Salamanca
{"title":"推测型Taskloop和OpenMP-for-Loop在硬件事务性内存上线程级推测的性能比较","authors":"Juan Salamanca","doi":"10.1109/ISPDC55340.2022.00021","DOIUrl":null,"url":null,"abstract":"Speculative Taskloop (STL) is a loop parallelization technique that takes the best of Task-based Parallelism and Thread-Level Speculation to speed up loops with may loop-carried dependencies that were previously difficult for compilers to parallelize. Previous studies show the efficiency of STL when implemented using Hardware Transactional Memory and the advantages it offers compared to a typical DOACROSS technique such as OpenMP ordered. This paper presents a performance comparison between STL and a previously proposed technique that implements Thread-Level Speculation (TLS) in the for worksharing construct (FOR-TLS) over a set of loops from cbench and SPEC2006 benchmarks. The results show interesting insights on how each technique can be more appropriate depending on the characteristics of the evaluated loop. Experimental results reveal that by implementing both techniques on top of HTM, speed-ups of up to 2.41× can be obtained for STL and up to 2× for FOR-TLS.","PeriodicalId":389334,"journal":{"name":"2022 21st International Symposium on Parallel and Distributed Computing (ISPDC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Performance Comparison of Speculative Taskloop and OpenMP-for-Loop Thread-Level Speculation on Hardware Transactional Memory\",\"authors\":\"Juan Salamanca\",\"doi\":\"10.1109/ISPDC55340.2022.00021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Speculative Taskloop (STL) is a loop parallelization technique that takes the best of Task-based Parallelism and Thread-Level Speculation to speed up loops with may loop-carried dependencies that were previously difficult for compilers to parallelize. Previous studies show the efficiency of STL when implemented using Hardware Transactional Memory and the advantages it offers compared to a typical DOACROSS technique such as OpenMP ordered. This paper presents a performance comparison between STL and a previously proposed technique that implements Thread-Level Speculation (TLS) in the for worksharing construct (FOR-TLS) over a set of loops from cbench and SPEC2006 benchmarks. The results show interesting insights on how each technique can be more appropriate depending on the characteristics of the evaluated loop. Experimental results reveal that by implementing both techniques on top of HTM, speed-ups of up to 2.41× can be obtained for STL and up to 2× for FOR-TLS.\",\"PeriodicalId\":389334,\"journal\":{\"name\":\"2022 21st International Symposium on Parallel and Distributed Computing (ISPDC)\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 21st International Symposium on Parallel and Distributed Computing (ISPDC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISPDC55340.2022.00021\",\"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 21st International Symposium on Parallel and Distributed Computing (ISPDC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISPDC55340.2022.00021","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

STL (Speculative Taskloop)是一种循环并行化技术,它充分利用了基于任务的并行性和线程级的推测来加速循环,这些循环携带的依赖关系以前很难被编译器并行化。以前的研究表明,使用硬件事务性内存实现STL的效率,以及与典型的DOACROSS技术(如OpenMP命令)相比,它提供的优势。本文介绍了STL和先前提出的一种技术之间的性能比较,该技术在工作共享结构(for -TLS)中通过一组来自cbench和SPEC2006基准测试的循环实现线程级推测(TLS)。结果显示了一些有趣的见解,说明每种技术如何根据被评估循环的特征更合适。实验结果表明,通过在HTM上实现这两种技术,STL可以获得高达2.41倍的加速,for - tls可以获得高达2倍的加速。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Performance Comparison of Speculative Taskloop and OpenMP-for-Loop Thread-Level Speculation on Hardware Transactional Memory
Speculative Taskloop (STL) is a loop parallelization technique that takes the best of Task-based Parallelism and Thread-Level Speculation to speed up loops with may loop-carried dependencies that were previously difficult for compilers to parallelize. Previous studies show the efficiency of STL when implemented using Hardware Transactional Memory and the advantages it offers compared to a typical DOACROSS technique such as OpenMP ordered. This paper presents a performance comparison between STL and a previously proposed technique that implements Thread-Level Speculation (TLS) in the for worksharing construct (FOR-TLS) over a set of loops from cbench and SPEC2006 benchmarks. The results show interesting insights on how each technique can be more appropriate depending on the characteristics of the evaluated loop. Experimental results reveal that by implementing both techniques on top of HTM, speed-ups of up to 2.41× can be obtained for STL and up to 2× for FOR-TLS.
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
期刊最新文献
Estimating the Impact of Communication Schemes for Distributed Graph Processing Sponsors and Conference Support Performance Comparison of Speculative Taskloop and OpenMP-for-Loop Thread-Level Speculation on Hardware Transactional Memory [Full] Deep Heuristic for Broadcasting in Arbitrary Networks Analysis and Mitigation of Soft-Errors on High Performance Embedded GPUs
×
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