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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.05207 |
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| _version_ | 1866908521386213376 |
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| author | Niam, Arefin Kosar, Tevfik Nine, M S Q Zulkar |
| author_facet | Niam, Arefin Kosar, Tevfik Nine, M S Q Zulkar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_05207 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RapidGNN: Energy and Communication-Efficient Distributed Training on Large-Scale Graph Neural Networks Niam, Arefin Kosar, Tevfik Nine, M S Q Zulkar Machine Learning Artificial Intelligence 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. |
| title | RapidGNN: Energy and Communication-Efficient Distributed Training on Large-Scale Graph Neural Networks |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.05207 |