Acceleration Algorithms in GNNs: A Survey
It addresses the problem of scaling GNNs for researchers and practitioners, but it is incremental as it reviews existing methods without introducing new algorithms.
This paper surveys acceleration algorithms for Graph Neural Networks (GNNs) to address inefficiencies in training and inference for scaling to real-world applications, categorizing approaches into training, inference, and execution acceleration and providing a systematic review and future directions.
Graph Neural Networks (GNNs) have demonstrated effectiveness in various graph-based tasks. However, their inefficiency in training and inference presents challenges for scaling up to real-world and large-scale graph applications. To address the critical challenges, a range of algorithms have been proposed to accelerate training and inference of GNNs, attracting increasing attention from the research community. In this paper, we present a systematic review of acceleration algorithms in GNNs, which can be categorized into three main topics based on their purpose: training acceleration, inference acceleration, and execution acceleration. Specifically, we summarize and categorize the existing approaches for each main topic, and provide detailed characterizations of the approaches within each category. Additionally, we review several libraries related to acceleration algorithms in GNNs and discuss our Scalable Graph Learning (SGL) library. Finally, we propose promising directions for future research. A complete summary is presented in our GitHub repository: https://github.com/PKU-DAIR/SGL/blob/main/Awsome-GNN-Acceleration.md.