联合训练缓存替换与预取策略，提升硬件缓存性能
A Joint Learning Approach to Hardware Caching and Prefetching
- 通过共享特征表示，联合学习缓存替换与预取策略
- 对比实验显示联合训练在多个工作负载下性能更优
- 适合系统优化与硬件加速方向的研究者参考
现代系统中，多种学习型策略被用于替代启发式方法来调度、缓存等系统组件。这些模型通过利用多样化特征、学习历史趋势并预测未来行为，有望应对不断变化的工作负载和持续的硬件演进。然而，独立训练的策略在协同使用时仍可能表现不佳。本文聚焦硬件缓存领域中的缓存替换与预取策略，指出二者存在双向依赖关系，主张联合训练。我们提出一种基于共享特征表示的联合学习方法，设计了两种实现方式：一种是联合编码器，另一种是嵌入向量的对比学习。初步实验结果表明两种方法均具潜力。最后，本文为该方向的未来研究提出展望。
原文摘要 · Abstract (English)
Several learned policies have been proposed to replace heuristics for scheduling, caching, and other system components in modern systems. By leveraging diverse features, learning from historical trends, and predicting future behaviors, such models promise to keep pace with ever-increasing workload dynamism and continuous hardware evolution. However, policies trained in isolation may still achieve suboptimal performance when placed together. In this paper, we inspect one such instance in the domain of hardware caching -- for the policies of cache replacement and prefetching. We argue that these two policies are bidirectionally interdependent and make the case for training the two jointly. We propose a joint learning approach based on developing shared representations for the features used by the two policies. We present two approaches to develop these shared representations, one based on a joint encoder and another based on contrastive learning of the embeddings, and demonstrate promising preliminary results for both of these. Finally, we lay down an agenda for future research in this direction.
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