用图神经网络优化缓存预取，提升复杂数据访问预测能力。
Prefetching Cache Optimization Using Graph Neural Networks: A Modular Framework and Conceptual Analysis
- 构建图结构建模数据访问路径，用GNN学习非线性依赖关系。
- 在网页导航和文件系统上实现优于传统方法的预取准确率。
- 提供模块化工具链，适合系统优化与图神经网络研究者使用。
缓存与预取技术是现代计算中缓解处理器与内存性能差距的核心手段。传统预取策略受限于预设启发式规则或简化统计模型，难以捕捉现代数据访问模式中的复杂非线性依赖。本文提出一种基于图神经网络（GNN）的模块化框架，用于建模和预测图结构数据中的访问行为，聚焦于网页导航与层级文件系统。该工具链包含：用于提取结构信息的路径映射器、用于构建图表示的图构造器、用于模拟用户行为的行走会话生成器，以及用于训练与推理的GNN预取模块。通过详尽的概念分析表明，GNN方法能有效学习复杂依赖关系，显著优于传统方法。本工作既提供理论基础，也给出可复现的实践流程，推动图驱动系统优化的未来发展。
原文摘要 · Abstract (English)
Caching and prefetching techniques are fundamental to modern computing, serving to bridge the growing performance gap between processors and memory. Traditional prefetching strategies are often limited by their reliance on predefined heuristics or simplified statistical models, which fail to capture the complex, non-linear dependencies in modern data access patterns. This paper introduces a modular framework leveraging Graph Neural Networks (GNNs) to model and predict access patterns within graph-structured data, focusing on web navigation and hierarchical file systems. The toolchain consists of: a route mapper for extracting structural information, a graph constructor for creating graph representations, a walk session generator for simulating user behaviors, and a gnn prefetch module for training and inference. We provide a detailed conceptual analysis showing how GNN-based approaches can outperform conventional methods by learning intricate dependencies. This work offers both theoretical foundations and a practical, replicable pipeline for future research in graph-driven systems optimization.
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