{"ID":2874301,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.05207","arxiv_id":"2509.05207","title":"RapidGNN: Energy and Communication-Efficient Distributed Training on Large-Scale Graph Neural Networks","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.","short_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 mi...","url_abs":"https://arxiv.org/abs/2509.05207","url_pdf":"https://arxiv.org/pdf/2509.05207v1","authors":"[\"Arefin Niam\",\"Tevfik Kosar\",\"M S Q Zulkar Nine\"]","published":"2025-09-05T16:10:20Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[\"Graph Neural Network\"]","has_code":false}