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

• URL: http://arxiv.org/abs/2509.05207v1
• Date: Fri, 05 Sep 2025 16:10:20 GMT
• Title: RapidGNN: Energy and Communication-Efficient Distributed Training on Large-Scale Graph Neural Networks
• Authors: Arefin Niam, Tevfik Kosar, M S Q Zulkar Nine,
• Abstract summary: Graph Neural Networks (GNNs) have become popular across a diverse set of tasks in exploring structural relationships between entities.<n>Traditional sampling-based approaches mitigate the computational loads, yet the communication overhead remains a challenge.<n>This paper presents RapidGNN, a distributed GNN training framework with deterministic sampling-based scheduling.
• Score: 1.5675763601034223
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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