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Acceleration Algorithms in GNNs: A Survey

arXiv 2405.04114 ↗First posted 7 May 2024cs.LG cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 75 of its 96 references that are in the map. First posted on arXiv 7 May 2024.

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References · 75

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38 more
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  5. 2023GNNHLS: Evaluating Graph Neural Network Inference via High-Level Synthesis
  6. 2024Sparse Decomposition of Graph Neural Networks
  7. 2025DCI: A Coordinated Allocation and Filling Workload-Aware Dual-Cache Allocation GNN Inference Acceleration System
  8. 2022Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification