A Prefetch-Adaptive Intelligent Cache Replacement Policy Based on Machine Learning  

作　　者：杨会静 方娟 蔡旻 才智 Hui-Jing Yang;Juan Fang;Min Cai;Zhi Cai(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China)

机构地区：[1]Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China

出　　处：《Journal of Computer Science & Technology》2023年第2期391-404,共14页计算机科学技术学报（英文版）

基　　金：supported by the Natural Science Foundation of Beijing under Grant No.4192007;the National Natural Science Foundation of China under Grant No.61202076.

摘　　要：Hardware prefetching and replacement policies are two techniques to improve the performance of the memory subsystem.While prefetching hides memory latency and improves performance,interactions take place with the cache replacement policies,thereby introducing performance variability in the application.To improve the accuracy of reuse of cache blocks in the presence of hardware prefetching,we propose Prefetch-Adaptive Intelligent Cache Replacement Policy(PAIC).PAIC is designed with separate predictors for prefetch and demand requests,and uses machine learning to optimize reuse prediction in the presence of prefetching.By distinguishing reuse predictions for prefetch and demand requests,PAIC can better combine the performance benefits from prefetching and replacement policies.We evaluate PAIC on a set of 27 memory-intensive programs from the SPEC 2006 and SPEC 2017.Under single-core configuration,PAIC improves performance over Least Recently Used(LRU)replacement policy by 37.22%,compared with improvements of 32.93%for Signature-based Hit Predictor(SHiP),34.56%for Hawkeye,and 34.43%for Glider.Under the four-core configuration,PAIC improves performance over LRU by 20.99%,versus 13.23%for SHiP,17.89%for Hawkeye and 15.50%for Glider.

关 键 词：hardware prefetching machine learning Prefetch-Adaptive Intelligent Cache Replacement Policy(PAIC) replacement policy 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]