一句话：提出 HybridTier，通过长短期热度追踪与概率化元数据实现自适应轻量级 CXL 内存分层。
现代工作负载对内存容量需求日增，基于 Compute Express Link（CXL）的内存分层利用 DRAM 与慢速 CXL 内存，是重要解决方案。现有系统难以兼顾动态偏斜的数据热度分布与分层带来的内存/缓存开销。HybridTier 同时追踪长期访问频率和短期访问动量以捕捉热度变化，并通过概率化访问追踪减少元数据内存开销，用面向数据局部性的轻量结构降低缓存开销。评估显示其性能最高提升 91%（几何平均 19%），内存开销降低 2.0-7.8 倍，缓存缺失降低 1.7-3.5 倍。
Modern workloads are demanding increasingly larger memory capacity. Compute Express Link (CXL)-based memory tiering has emerged as a promising solution for addressing this problem by utilizing traditional DRAM alongside slow-tier CXL memory devices. We analyze prior tiering systems and observe two challenges for high-performance memory tiering: adapting to skewed but dynamically varying data hotness distributions while minimizing memory and cache overhead due to tiering. To address these challenges, we propose HybridTier, an adaptive and lightweight tiering system for CXL memory. HybridTier tracks both long-term data access frequency and short-term access momentum simultaneously to accurately capture and adapt to shifting hotness distributions. HybridTier reduces the metadata memory overhead by tracking data accesses probabilistically, obtaining higher memory efficiency by trading off a small amount of tracking inaccuracy that has a negligible impact on application performance. To reduce cache overhead, HybridTier uses lightweight data structures that optimize for data locality to track data hotness. Our evaluations show that HybridTier outperforms prior systems by up to 91% (19% geomean), incurring 2.0-7.8x less memory overhead and 1.7-3.5x less cache misses.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{SongY0Z0P25,
title = {HybridTier: an Adaptive and Lightweight CXL-Memory Tiering System},
author = {Kevin Song and Jiacheng Yang and Zixuan Wang and Jishen Zhao and Sihang Liu and Gennady Pekhimenko},
booktitle = {ASPLOS},
year = {2025},
doi = {10.1145/3676642.3736119}
}