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.

翻译：图神经网络（GNNs）已在多种基于图的任务中展现出有效性。然而，其在训练和推理阶段存在的低效问题，为扩展至实际大规模图应用带来了挑战。为应对这些关键挑战，研究者提出了一系列加速GNNs训练与推理的算法，并日益受到学术界的关注。本文系统综述了图神经网络加速算法，根据其目的可归纳为三大主题：训练加速、推理加速与执行加速。具体而言，我们针对每个主题总结并分类现有方法，详细阐述各类别方法的特征。此外，我们回顾了多款与GNN加速算法相关的库，并介绍我们自研的可扩展图学习（SGL）库。最后，本文提出未来研究的潜在方向。完整内容详见我们的GitHub仓库：https://github.com/PKU-DAIR/SGL/blob/main/Awsome-GNN-Acceleration.md。