Phases, Modalities, Temporal and Spatial Locality: Domain Specific ML Prefetcher for Accelerating Graph Analytics
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Graph processing applications are severely bottlenecked by memory system performance due to low data reuse and irregular memory accesses. While state-of-the-art prefetchers using Machine Learning (ML) have made great progress, they do not perform well on graph analytics applications due to phase transitions in the execution and irregular data access that is hard to predict. We propose MPGraph: a novel ML-based Prefetcher for Graph analytics. MPGraph makes three novel optimizations based on domain knowledge of graph analytics. It detects the transition of graph processing phases during execution using a novel soft detection technique, predicts memory accesses and pages using phase-specific multi-modality predictors, and prefetches using a novel chain spatio-temporal prefetching strategy. We evaluate our approach using three widely-used graph processing frameworks and a variety of graph datasets. Our approach achieves 34.17 than the KSWIN and decision tree baselines. Our predictors achieve 6.80 higher F1-score for access prediction and 11.68 for page prediction compared with the baselines LSTM-based and vanilla attention-based models. Simulations show that MPGraph achieves on the average 87.16 12.52 BO by 7.58 Voyager by 3.27 improvement.
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