一句话：提出基于局部敏感哈希的硬件在线缓存行聚类压缩机制，利用相似缓存行簇提升缓存压缩率。
本文针对缓存工作集中此前未被利用的可压缩性来源：彼此相似但不完全相同的缓存行簇。为利用这一机会，作者提出一种硬件级在线缓存行聚类机制，基于局部敏感哈希（LSH）在数据访问流中动态形成簇，并在缓存行消失时淘汰簇；每个簇存储一个“簇代表”（clusteroid）以及重建其余成员所需的小型差异（diff）。评估在 SPEC CPU 2017 上进行，平均达到 2.25 倍压缩，最高 9.9 倍，且在同等硅面积预算下显著优于先前方案。
In this paper, we identify a previously untapped source of compressibility in cache working sets: clusters of cachelines that are similar, but not identical, to one another. To compress the cache, we can then store the "clusteroid" of each cluster together with the (much smaller) "diffs" needed to reconstruct the rest of the cluster. To exploit this opportunity, we propose a hardware-level on-line cacheline clustering mechanism based on locality-sensitive hashing. Our method dynamically forms clusters as they appear in the data access stream and retires them as they disappear from the cache. Our evaluations show that we achieve 2.25× compression on average (and up to 9.9×) on SPEC~CPU~2017 suite and is significantly higher than prior proposals scaled to an iso-silicon budget.
DOI 原文 ·
@inproceedings{paperbot5455,
title = {Thesaurus},
author = {Amin Ghasemazar and Prashant Nair and Mieszko Lis},
booktitle = {ASPLOS},
year = {2020},
doi = {10.1145/3373376.3378518}
}