Prefetching Cache Optimization Using Graph Neural Networks: A Modular Framework and Conceptual Analysis

TL;DR

This paper presents a modular framework using Graph Neural Networks to improve cache prefetching by modeling complex data access patterns, offering a new approach beyond traditional heuristics.

Contribution

It introduces a GNN-based framework for cache prefetching, combining structural data modeling with a practical pipeline, advancing beyond conventional heuristic methods.

Findings

GNNs outperform traditional prefetching techniques in modeling access patterns.

The framework effectively captures complex, non-linear dependencies in data access.

Provides a practical, replicable toolchain for future graph-driven system optimization.

Abstract

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.