G-MAP: A Graph Neural Network-Based Framework for Memory Access Prediction

Abhiram Rao Gorle, Pengmiao Zhang, Rajgopal Kannan, Viktor K. Prasanna

Abstract: Memory access prediction is a crucial problem in data prefetchers, as it helps us improve memory performance and reduce latency in computing systems. Existing works model the problem as a sequence prediction problem. This can be limited in its ability to capture complex patterns and dependencies in memory access behavior. In recent years, Graph Neural Networks (GNNs) have emerged as a promising technique for modeling and predicting complex relationships in graph-structured data. In this paper, we introduce G-MAP, a novel Graph Neural Network-based framework for Memory Access Prediction. First, we propose Me $m2Graph$ , a novel approach mapping a memory access sequence to a graph representation, capturing both the spatial and temporal locality in the sequence. Second, we implement various GNNs for G-MAP, including Graph Convolutional Network (GCN), Gated Graph Sequence Neural Network (GG-NN), and Graph Attention Network (GAT). Those models take the graph generated from Mem2Graph as input and predict future memory address jumps (deltas). We evaluate the effectiveness of G-MAP using the SPEC 2006 benchmark. G-MAP using GG-NN shows the highest performance among all models, achieving 0.7526 F1-Score on the average, which is 10.77% higher than the Multi-Layer Perceptron baseline.