用深度学习预测远端内存访问，提升性能3.6倍
Learning Semantics, Not Addresses: Runtime Neural Prefetching for Far Memory
- 将程序语义与内存布局分离，用轻量映射实现高效预测
- 在4个数据密集型任务中最高提升3.6倍性能
- 首个基于Linux的远端内存深度学习预取系统
内存预取长期用于提升CPU缓存性能，对远端内存系统尤为重要，此类系统将大量内存移至成本更低的远程层级。有效预取依赖于对未来访问的准确预测，但以往机器学习方法受限于仿真环境或小规模硬件。本文提出FarSight，首个基于Linux的远端内存系统，通过解耦应用语义与运行时内存布局，使离线训练的模型可基于紧凑的序数词汇表预测访问模式，并在运行时通过轻量映射解析。在四个数据密集型工作负载上，FarSight性能最高达当前最优方案的3.6倍。
原文摘要 · Abstract (English)
Memory prefetching has long boosted CPU caches and is increasingly vital for far-memory systems, where large portions of memory are offloaded to cheaper, remote tiers. While effective prefetching requires accurate prediction of future accesses, prior ML approaches have been limited to simulation or small-scale hardware. We introduce FarSight, the first Linux-based far-memory system to leverage deep learning by decoupling application semantics from runtime memory layout. This separation enables offline-trained models to predict access patterns over a compact ordinal vocabulary, which are resolved at runtime through lightweight mappings. Across four data-intensive workloads, FarSight delivers up to 3.6x higher performance than the state-of-the-art.
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