Enhancing a manycore-oriented compressed cache for GPGPU

Keitarou Oka, Satoshi Kawakami, Teruo Tanimoto, Takatsugu Ono, Koji Inoue
{"title":"Enhancing a manycore-oriented compressed cache for GPGPU","authors":"Keitarou Oka, Satoshi Kawakami, Teruo Tanimoto, Takatsugu Ono, Koji Inoue","doi":"10.1145/3368474.3368491","DOIUrl":null,"url":null,"abstract":"GPUs can achieve high performance by exploiting massive-thread parallelism. However, some factors limit performance on GPUs, one of which is the negative effects of L1 cache misses. In some applications, GPUs are likely to suffer from L1 cache conflicts because a large number of cores share a small L1 cache capacity. A cache architecture that is based on data compression is a strong candidate for solving this problem as it can reduce the number of cache misses. Unlike previous studies, our data compression scheme attempts to exploit the value locality existing within not only intra cache lines but also inter cache lines. We enhance the structure of a last-level compression cache proposed for general purpose manycore processors to optimize against shared L1 caches on GPUs. The experimental results reveal that our proposal outperforms the other compression cache for GPUs by 11 points on average.","PeriodicalId":314778,"journal":{"name":"Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region","volume":"415 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3368474.3368491","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

GPUs can achieve high performance by exploiting massive-thread parallelism. However, some factors limit performance on GPUs, one of which is the negative effects of L1 cache misses. In some applications, GPUs are likely to suffer from L1 cache conflicts because a large number of cores share a small L1 cache capacity. A cache architecture that is based on data compression is a strong candidate for solving this problem as it can reduce the number of cache misses. Unlike previous studies, our data compression scheme attempts to exploit the value locality existing within not only intra cache lines but also inter cache lines. We enhance the structure of a last-level compression cache proposed for general purpose manycore processors to optimize against shared L1 caches on GPUs. The experimental results reveal that our proposal outperforms the other compression cache for GPUs by 11 points on average.
增强面向GPGPU的多核压缩缓存
gpu可以通过利用大规模线程并行性来实现高性能。然而,一些因素限制了gpu上的性能,其中之一是L1缓存丢失的负面影响。在某些应用程序中,由于大量内核共享较小的L1缓存容量,gpu可能会出现L1缓存冲突。基于数据压缩的缓存架构是解决此问题的有力候选,因为它可以减少缓存丢失的次数。与以往的研究不同,我们的数据压缩方案试图利用存在于缓存线内和缓存线之间的值局部性。我们增强了为通用多核处理器提出的最后一级压缩缓存的结构,以优化gpu上的共享L1缓存。实验结果表明,我们的方案比其他gpu压缩缓存的性能平均高出11分。
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:604180095
Book学术官方微信