Minor memory references matter in collaborative caching

Xiaoming Gu
{"title":"Minor memory references matter in collaborative caching","authors":"Xiaoming Gu","doi":"10.1145/1988915.1988927","DOIUrl":null,"url":null,"abstract":"Collaborative caching uses different caching methods, e. g., LRU and MRU, for data with good or poor locality. Poorlocality data are evicted by MRU quickly, leaving most cache space to hold good-locality data by LRU. In our previous study, we selected static memory references with poor locality to use MRU but neglected minor references, which are memory instructions that contribute no more than 0.1% total memory accesses. After removing this restriction, we found that three SPEC CPU benchmarks have on average 6.2 times fewer miss reduction or 9.8% reduction in absolute miss ratio.","PeriodicalId":130040,"journal":{"name":"Workshop on Memory System Performance and Correctness","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Workshop on Memory System Performance and Correctness","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1988915.1988927","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Collaborative caching uses different caching methods, e. g., LRU and MRU, for data with good or poor locality. Poorlocality data are evicted by MRU quickly, leaving most cache space to hold good-locality data by LRU. In our previous study, we selected static memory references with poor locality to use MRU but neglected minor references, which are memory instructions that contribute no more than 0.1% total memory accesses. After removing this restriction, we found that three SPEC CPU benchmarks have on average 6.2 times fewer miss reduction or 9.8% reduction in absolute miss ratio.
次要内存引用在协作缓存中很重要
协作缓存使用不同的缓存方法,例如LRU和MRU,来处理局部性好的数据和局部性差的数据。低局域数据被MRU快速清除,留下大部分缓存空间给LRU保存良好局域数据。在我们之前的研究中,我们选择了局域性差的静态内存引用来使用MRU,但忽略了次要引用,这些引用是内存指令,贡献不超过总内存访问的0.1%。在去掉这个限制之后,我们发现三个SPEC CPU基准测试的脱靶率平均降低了6.2倍,绝对脱靶率降低了9.8%。
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 求助全文
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