HybridTier：一种自适应且轻量级的CXL内存分层系统
HybridTier: an Adaptive and Lightweight CXL-Memory Tiering System
AI总结：
HybridTier提出一种自适应轻量级CXL内存分层系统，通过同时跟踪长期访问频率与短期动量并采用概率跟踪和局部性优化数据结构，在提升性能的同时显著降低内存和缓存开销。
摘要：
现代工作负载对内存容量的需求日益增长。基于Compute Express Link（CXL）的内存分层技术已成为解决该问题的一种有前景的方案，它同时利用传统DRAM和慢速层CXL内存设备。我们分析了此前的分层系统，并观察到实现高性能内存分层面临两个挑战：既要适应倾斜但动态变化的数据热度分布，又要尽量减少分层带来的内存和缓存开销。为解决这些挑战，我们提出了HybridTier，一个面向CXL内存的自适应且轻量级的分层系统。HybridTier同时跟踪长期数据访问频率和短期访问动量，以准确捕捉并适应不断变化的热度分布。HybridTier通过概率性地跟踪数据访问来降低元数据内存开销，以少量跟踪不准确性换取更高的内存效率，而这种不准确性对应用性能的影响可以忽略不计。为减少缓存开销，HybridTier使用针对数据局部性优化的轻量级数据结构来跟踪数据热度。我们的评估表明，HybridTier的性能比先前系统最多高出91%（几何平均为19%），同时内存开销减少2.0至7.8倍，缓存未命中减少1.7至3.5倍。
英文摘要：
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 \emph{simultaneously} to accurately capture and adapt to shifting hotness distributions. HybridTier reduces the metadata memory overhead by tracking data accesses \emph{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 $2.0-7.8\times$ less memory overhead and $1.7-3.5\times$ less cache misses.
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