Kill the Program Counter

Author:

Kim Jinchun1,Teran Elvira1,Gratz Paul V.1,Jiménez Daniel A.1,Pugsley Seth H.2,Wilkerson Chris2

Affiliation:

1. Texas A&M University, College Station, TX, USA

2. Intel Labs, Hillsboro, OR, USA

Abstract

Data prefetching and cache replacement algorithms have been intensively studied in the design of high performance microprocessors. Typically, the data prefetcher operates in the private caches and does not interact with the replacement policy in the shared Last-Level Cache (LLC). Similarly, most replacement policies do not consider demand and prefetch requests as different types of requests. In particular, program counter (PC)-based replacement policies cannot learn from prefetch requests since the data prefetcher does not generate a PC value. PC-based policies can also be negatively affected by compiler optimizations. In this paper, we propose a holistic cache management technique called Kill-the-PC (KPC) that overcomes the weaknesses of traditional prefetching and replacement policy algorithms. KPC cache management has three novel contributions. First, a prefetcher which approximates the future use distance of prefetch requests based on its prediction confidence. Second, a simple replacement policy provides similar or better performance than current state-of-the-art PC-based prediction using global hysteresis. Third, KPC integrates prefetching and replacement policy into a whole system which is greater than the sum of its parts. Information from the prefetcher is used to improve the performance of the replacement policy and vice-versa. Finally, KPC removes the need to propagate the PC through entire on-chip cache hierarchy while providing a holistic cache management approach with better performance than state-of-the-art PC-, and non-PC-based schemes. Our evaluation shows that KPC provides 8% better performance than the best combination of existing prefetcher and replacement policy for multi-core workloads.

Funder

National Science Foundation

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design,Software

Cited by 11 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. RL-CoPref: a reinforcement learning-based coordinated prefetching controller for multiple prefetchers;The Journal of Supercomputing;2024-02-27

2. A Fairness-Aware Prefetching Mechanism based on Reinforcement Learning for Multi-Core Systems;2023 IEEE International Conference on High Performance Computing & Communications, Data Science & Systems, Smart City & Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys);2023-12-17

3. A Prefetch-Adaptive Intelligent Cache Replacement Policy Based on Machine Learning;Journal of Computer Science and Technology;2023-03-30

4. RL-Based Cache Replacement: A Modern Interpretation of Belady’s Algorithm With Bypass Mechanism and Access Type Analysis;IEEE Access;2023

5. Puppeteer: A Random Forest Based Manager for Hardware Prefetchers Across the Memory Hierarchy;ACM Transactions on Architecture and Code Optimization;2022-12-16

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