mlsanityData to 7 Oct 2026

Characterizing and Understanding Distributed GNN Training on GPUs

arXiv 2204.08150 ↗First posted 18 Apr 2022cs.DC cs.LGcited by 7

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Abstract (arXiv)

Graph neural network (GNN) has been demonstrated to be a powerful model in many domains for its effectiveness in learning over graphs. To scale GNN training for large graphs, a widely adopted approach is distributed training which accelerates training using multiple computing nodes. Maximizing the performance is essential, but the execution of distributed GNN training remains preliminarily understood. In this work, we provide an in-depth analysis of distributed GNN training on GPUs, revealing several significant observations and providing useful guidelines for both software optimization and hardware optimization.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 9 of its 13 references that are in the map. First posted on arXiv 18 Apr 2022.

Builds on

9 of 13 references.

References · 9

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  5. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  6. 2020DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphscited by 139
  7. 2020HyGCN: A GCN Accelerator with Hybrid Architecturecited by 73
  8. 2021DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networkscited by 59
  9. 2020Characterizing and Understanding GCNs on GPUcited by 22

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Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 4.

7 papers.

  1. 2022A Comprehensive Survey on Distributed Training of Graph Neural Networkscited by 15
  2. 2022Characterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying Glasscited by 3
  3. 2023Learning Graph Neural Networks using Exact Compressioncited by 3
  4. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 2
  5. 2024D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networkscited by 2
  6. 2024Characterizing and Understanding HGNN Training on GPUscited by 1
  7. 2024SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Accelerationcited by 0