2022/01/23 by Miryeong Kwon, Kwon, Miryeong, Donghyun Gouk +5
Computer Science · Materials Science · #Advanced Graph Neural Networks #FOS: Computer and information sciences #Graph Theory and Algorithms #Hardware Architecture (cs.AR) #Machine Learning (cs.LG) #Machine Learning in Materials Science
paper · pdf · doi:10.48550/arxiv.2201.09189
openalex publication_date 2022/01/23 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
Graph neural networks (GNNs) process large-scale graphs consisting of a hundred billion edges. In contrast to traditional deep learning, unique behaviors of the emerging GNNs are engaged with a large set of graphs and embedding data on storage, which exhibits complex and irregular preprocessing. 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 actual data exist in a holistic manner. It also enables RPC over PCIe such that the users can simply program GNNs through a graph semantic library without any knowledge of the underlying hardware or storage configurations. 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 modern GPUs by 7.1x while reducing energy consumption by 33.2x, on average.