LazyCAT: Efficient Fine-Grained Cache Partitioning with Two Boundaries
Chuanqi Zhang, Xueqi Li, Ninghui Sun, Yungang Bao, Sa Wang · 2024
Intel CAT is a widely available cache partitioning technique in commercial hardware but falls short in partitioning granularity. We propose LazyCAT, a fine-grained, on-demand, and easy-to-use cache partitioning technique, which not only can ensure the QoS of High-Priority (HP) applications but also yield the under-utilized cache sets to other Best-Effort (BE) applications for better resource efficiency. LazyCAT retains the easy-to-use philosophy of CAT and introduces a new soft LLC partitioning boundary, which is lower than the original CAT partitioning boundary (hard boundary). LazyCAT detects and selects under-utilized cache sets in HP applications during profiling, and specifies them to the soft boundary at runtime dynamically, yielding the cache blocks between these two boundaries for other applications. Meanwhile, LazyCAT provides users with a simple software interface to guide the set-level space allocation according to their needs. Experimental results show that LazyCAT exhibits substantial performance improvements (up to 12.2%) for BE applications with less than 3% performance degradation of HP applications.