Data partitioning on chip multiprocessors

J. Cieslewicz, K. A. Ross
{"title":"Data partitioning on chip multiprocessors","authors":"J. Cieslewicz, K. A. Ross","doi":"10.1145/1457150.1457156","DOIUrl":null,"url":null,"abstract":"Partitioning is a key database task. In this paper we explore partitioning performance on a chip multiprocessor (CMP) that provides a relatively high degree of on-chip thread-level parallelism. It is therefore important to implement the partitioning algorithm to take advantage of the CMP's parallel execution resources. We identify the coordination of writing partition output as the main challenge in a parallel partitioning implementation and evaluate four techniques for enabling parallel partitioning. We confirm previous work in single threaded partitioning that finds L2 cache misses and translation lookaside buffer misses to be important performance issues, but we now add the management of concurrent threads to this analysis.","PeriodicalId":298901,"journal":{"name":"International Workshop on Data Management on New Hardware","volume":"83 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"42","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Workshop on Data Management on New Hardware","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1457150.1457156","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 42

Abstract

Partitioning is a key database task. In this paper we explore partitioning performance on a chip multiprocessor (CMP) that provides a relatively high degree of on-chip thread-level parallelism. It is therefore important to implement the partitioning algorithm to take advantage of the CMP's parallel execution resources. We identify the coordination of writing partition output as the main challenge in a parallel partitioning implementation and evaluate four techniques for enabling parallel partitioning. We confirm previous work in single threaded partitioning that finds L2 cache misses and translation lookaside buffer misses to be important performance issues, but we now add the management of concurrent threads to this analysis.
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
芯片多处理器上的数据分区
分区是一项关键的数据库任务。在本文中,我们探讨了芯片多处理器(CMP)上的分区性能,它提供了相对较高的片上线程级并行性。因此,实现分区算法以利用CMP的并行执行资源是很重要的。我们将写入分区输出的协调确定为并行分区实现中的主要挑战,并评估了实现并行分区的四种技术。我们确认了之前在单线程分区中发现L2缓存缺失和转换暂置缓冲区缺失是重要的性能问题的工作,但是我们现在将并发线程的管理添加到这个分析中。
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
On testing persistent-memory-based software SIMD-accelerated regular expression matching FPGA-accelerated group-by aggregation using synchronizing caches Customized OS support for data-processing Larger-than-memory data management on modern storage hardware for in-memory OLTP database 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