Phases, Modalities, Spatial and Temporal Locality: Domain Specific ML Prefetcher for Accelerating Graph Analytics

Abstract

Memory performance is a key bottleneck in accelerating graph analytics. Existing Machine Learning (ML) prefetchers encounter challenges with phase transitions and irregular memory accesses in graph processing. We propose MPGraph, an ML-based Prefetcher for Graph analytics using domain specific models. MPGraph introduces three novel optimizations: soft detection of phase transitions, phase-specific multi-modality models for access delta and page predictions, and chain spatio-temporal prefetching (CSTP) for prefetch control. Our transition detector achieves 34.17-82.15% higher precision compared with Kolmogorov-Smirnov Windowing and decision tree. Our predictors achieve 6.80-16.02% higher F1-score for delta and 11.68-15.41% higher accuracy-at-10 for page prediction compared with LSTM and vanilla attention models. Using CSTP, MP-Graph achieves 12.52-21.23% IPC improvement, outperforming state-of-the-art non-ML prefetcher BO by 7.58-12.03% and ML-based prefetchers Voyager and TransFetch by 3.27-4.58%. For practical implementation, we compress the prediction models to reduce the storage and latency overhead. MPGraph with the compressed models still shows significantly superior accuracy and coverage compared to BO, with 3.58% IPC improvement.