二维高度关联的二级缓存设计

Chuanjun Zhang, Bing Xue
{"title":"二维高度关联的二级缓存设计","authors":"Chuanjun Zhang, Bing Xue","doi":"10.1109/ICCD.2008.4751934","DOIUrl":null,"url":null,"abstract":"High associativity is important for level-two cache designs [9]. Implementing CAM-based highly associative caches (CAM-HAC), however, is both costly in hardware and exhibits poor scalability. We propose to implement the CAM-HAC in macro-blocks to improve scalability. Each macro-block contains 128-row and 8-column of cache blocks. We name it Two dimensional Cache, or T-Cache. Each macro-block has an associativity equivalent to 128times8=1024-way. Twelve bits of the T-Cachepsilas tag are implemented by using CAM, while the remaining tag uses SRAM; Furthermore, random replacement is used in rows to balance cache sets usage while LRU is used in columns to select the victim from a row. The hardware complexity for replacement is reduced greatly compared to a traditional CAM-HAC using LRU solely. Experimental results show that the T-Cache achieves a 16% miss rate reduction over a traditional 8-way unified L2 cache. This translates into an average IPC improvement of 5% and as high as 18%. The T-Cache exhibits a 4% total memory access-related energy savings due to the reduction to applicationspsila execution time.","PeriodicalId":345501,"journal":{"name":"2008 IEEE International Conference on Computer Design","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Two dimensional highly associative level-two cache design\",\"authors\":\"Chuanjun Zhang, Bing Xue\",\"doi\":\"10.1109/ICCD.2008.4751934\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"High associativity is important for level-two cache designs [9]. Implementing CAM-based highly associative caches (CAM-HAC), however, is both costly in hardware and exhibits poor scalability. We propose to implement the CAM-HAC in macro-blocks to improve scalability. Each macro-block contains 128-row and 8-column of cache blocks. We name it Two dimensional Cache, or T-Cache. Each macro-block has an associativity equivalent to 128times8=1024-way. Twelve bits of the T-Cachepsilas tag are implemented by using CAM, while the remaining tag uses SRAM; Furthermore, random replacement is used in rows to balance cache sets usage while LRU is used in columns to select the victim from a row. The hardware complexity for replacement is reduced greatly compared to a traditional CAM-HAC using LRU solely. Experimental results show that the T-Cache achieves a 16% miss rate reduction over a traditional 8-way unified L2 cache. This translates into an average IPC improvement of 5% and as high as 18%. The T-Cache exhibits a 4% total memory access-related energy savings due to the reduction to applicationspsila execution time.\",\"PeriodicalId\":345501,\"journal\":{\"name\":\"2008 IEEE International Conference on Computer Design\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE International Conference on Computer Design\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCD.2008.4751934\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE International Conference on Computer Design","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCD.2008.4751934","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

高关联性对于二级缓存设计非常重要[9]。然而,实现基于cam的高关联缓存(CAM-HAC)在硬件上成本很高,而且可扩展性很差。我们建议在宏块中实现CAM-HAC以提高可扩展性。每个宏块包含128行和8列的缓存块。我们称之为二维缓存,或者t -缓存。每个宏块的结合性相当于128times8=1024-way。T-Cachepsilas标签的12位使用CAM实现,其余标签使用SRAM;此外,在行中使用随机替换来平衡缓存集的使用,而在列中使用LRU来从一行中选择受害者。与仅使用LRU的传统CAM-HAC相比,大大降低了更换硬件的复杂性。实验结果表明,与传统的8路统一L2缓存相比,T-Cache的丢失率降低了16%。这意味着IPC平均提高了5%,最高可达18%。由于减少了应用程序的执行时间,T-Cache显示了4%的内存访问相关的总能源节约。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Two dimensional highly associative level-two cache design
High associativity is important for level-two cache designs [9]. Implementing CAM-based highly associative caches (CAM-HAC), however, is both costly in hardware and exhibits poor scalability. We propose to implement the CAM-HAC in macro-blocks to improve scalability. Each macro-block contains 128-row and 8-column of cache blocks. We name it Two dimensional Cache, or T-Cache. Each macro-block has an associativity equivalent to 128times8=1024-way. Twelve bits of the T-Cachepsilas tag are implemented by using CAM, while the remaining tag uses SRAM; Furthermore, random replacement is used in rows to balance cache sets usage while LRU is used in columns to select the victim from a row. The hardware complexity for replacement is reduced greatly compared to a traditional CAM-HAC using LRU solely. Experimental results show that the T-Cache achieves a 16% miss rate reduction over a traditional 8-way unified L2 cache. This translates into an average IPC improvement of 5% and as high as 18%. The T-Cache exhibits a 4% total memory access-related energy savings due to the reduction to applicationspsila execution time.
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