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RapidGNN: Energy and Communication-Efficient Distributed Training on Large-Scale Graph Neural Networks

arXiv 2509.05207 ↗First posted 5 Sep 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

Graph Neural Networks (GNNs) have become popular across a diverse set of tasks in exploring structural relationships between entities. However, due to the highly connected structure of the datasets, distributed training of GNNs on large-scale graphs poses significant challenges. Traditional sampling-based approaches mitigate the computational loads, yet the communication overhead remains a challenge. This paper presents RapidGNN, a distributed GNN training framework with deterministic sampling-based scheduling to enable efficient cache construction and prefetching of remote features. Evaluation on benchmark graph datasets demonstrates RapidGNN's effectiveness across different scales and topologies. RapidGNN improves end-to-end training throughput by 2.46x to 3.00x on average over baseline methods across the benchmark datasets, while cutting remote feature fetches by over 9.70x to 15.39x. RapidGNN further demonstrates near-linear scalability with an increasing number of computing units efficiently. Furthermore, it achieves increased energy efficiency over the baseline methods for both CPU and GPU by 44% and 32%, respectively.

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References · 15

  1. 2017Inductive Representation Learning on Large Graphscited by 6,320
  2. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  3. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 708
  4. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 584
  5. 2019Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networkscited by 541
  6. 2022Graph neural networks for materials science and chemistrycited by 182
  7. 2019Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networkscited by 177
  8. 2020DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphscited by 139
  9. 2021Graph Neural Networks in Network Neurosciencecited by 92
  10. 2022FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generationcited by 67
  11. 2021Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threadscited by 51
  12. 2022Distributed Graph Neural Network Training: A Surveycited by 32
3 more
  1. 2021Clairvoyant Prefetching for Distributed Machine Learning I/Ocited by 14
  2. 2021Communication-Efficient Sampling for Distributed Training of Graph Convolutional Networkscited by 7
  3. 2023Boosting Distributed Full-graph GNN Training with Asynchronous One-bit Communicationcited by 3

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