GraFetch: Accelerating Graph Applications Through Domain Specific Hierarchical Hybrid Prefetching

Abstract

Memory performance bottlenecks the execution of graph applications, from traditional graph analytics (GA) to rapidly evolving graph neural networks (GNNs), due to the large size and complexity of graphs. While machine learning (ML) algorithms have shown potential in data prefetching to hide memory access latency, existing approaches face challenges with phase transitions and irregular memory access patterns in graph applications. To address these challenges, we introduce GraFetch, a specialized prefetching system for accelerating graph applications. GraFetch comprises of 1) a novel Hierarchical Hybrid Prefetching (HHP) framework that supports the cooperation of phase-specific ML predictors for high-complexity pattern prefetching and rule-based prefetchers for low-complexity pattern prefetching; and 2) Domain Specific Machine Learning (DSML) models integrated in the framework, which incorporate domain knowledge of graph applications to detect phases, recognize patterns, and predict memory accesses. We evaluate our approach using popular GA frameworks GPOP and X-Stream, and state-of-the-art GNN frameworks PyG and DGL. Our domain specific attention-based memory access predictors achieve 7.4% higher F1-score for delta (consecutive address jump) prediction and 15.35% higher accuracy@10 for page prediction compared with basic attention models. GraFetch achieves an average IPC improvement of 12.47% for GA and 4.18% for GNNs over a system with no prefetcher. This outperforms state-of-the-art rule-based prefetchers BO (7.12% for GA, 1.10% for GNNs), ISB (3.82% for GA, 1.60% for GNNs), and IMP (8.47% for GA, 2.20% for GNNs), as well as ML-based prefetchers Voyager (9.61% for GA, 3.14% for GNNs) and TransFetch (10.98% for GA, 2.48% for GNNs).