Accelerating Graph Neural Networks on Real Processing-In-Memory Systems

arXiv:2402.16731v2 Announce Type: replace-cross
Abstract: Graph Neural Networks (GNNs) are emerging ML models to analyze graph-structure data. Graph Neural Network (GNN) execution involves both compute-intensive and memory-intensive kernels, the latter dominates the total time, being significantly bottlenecked by data movement between memory and processors. Processing-In-Memory (PIM) systems can alleviate this data movement bottleneck by placing simple processors near or inside to memory arrays. In this work, we introduce PyGim, an efficient ML framework that accelerates GNNs on real PIM systems. …

abstract analyze arxiv compute cs.ar cs.dc cs.lg cs.pf data data movement gnn gnns graph graph neural network graph neural networks in-memory memory ml models network networks neural network neural networks processing processors systems total type