CHROME: Concurrency-Aware Holistic Cache Management Framework with Online Reinforcement Learning
Authors: X. Lu, H. Najafi, J. Liu, X.-H. Sun
Date: March, 2024
Venue: The 30th IEEE International Symposium on High-Performance Computer Architecture (HPCA 2024), Edinburgh, Scotland
Type: Conference
Abstract
Cache management is a critical aspect of computer architecture, encompassing techniques such as cache replace- ment, bypassing, and prefetching. Existing research has often fo- cused on individual techniques, overlooking the potential benefits of joint optimization. Moreover, many of these approaches rely on static and intuition-driven policies, limiting their performance under complex and dynamic workloads. To address these chal- lenges, this paper introduceesCHROME,a novelty aware cache management framework. CHROME takes a holistic approach by seamlessly integrating intelligent cache replacement and bypassing with pattern-based prefetching. By leveraging online reinforcement learning, CHROME dynamically adapts cache decisions based on multiple program features and applies a reward for each decision that considers the accuracy of the action and the system-level feedback information. Our performance evaluation demonstrates that CHROME outperforms current state-of-the-art schemes, exhibiting significant improvements in cache management. Notably, CHROME achieves a remarkable performance boost of up to 13.7% over the traditional LRU method in multi-core systems with only modest overhead.