HolisticGNN: Geometric Deep Learning Engines for Computational SSDs

Abstract

Graph neural networks (GNNs) process large-scale graphs consisting of a hundred billion edges, which exhibit much higher accuracy in a variety of prediction tasks. However, as GNNs are engaged with a large set of graphs and embedding data on storage, they suffer from heavy I/O accesses and irregular computation. We propose a novel deep learning framework on large graphs, HolisticGNN, that provides an easy-to-use, near-storage inference infrastructure for fast, energy-efficient GNN processing. To achieve the best end-to-end latency and high energy efficiency, HolisticGNN allows users to implement various GNN algorithms and directly executes them where the data exist in a holistic manner. We fabricate HolisticGNN's hardware RTL and implement its software on an FPGA-based computational SSD (CSSD). Our empirical evaluations show that the inference time of HolisticGNN outperforms GNN inference services using high-performance GPU by 7.1x while reducing energy consumption by 33.2x, on average.