ParGNN: A Scalable Graph Neural Network Training Framework on multi-GPUs

Full-batch Graph Neural Network (GNN) training is indispensable for interdisciplinary applications. Although fullbatch training has advantages in convergence accuracy and speed, it still faces challenges such as severe load imbalance and high communication traffic overhead. In order to address these...

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Vydané v:	2025 62nd ACM/IEEE Design Automation Conference (DAC) s. 1 - 7
Hlavní autori:	Gu, Junyu, Li, Shunde, Cao, Rongqiang, Wang, Jue, Wang, Zijian, Liang, Zhiqiang, Liu, Fang, Li, Shigang, Zhou, Chunbao, Wang, Yangang, Chi, Xuebin
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 22.06.2025
Predmet:	Accuracy Computation and communication overlapping Convergence Design automation Faces Full-batch distributed training Graph neural network Graph neural networks Graphics processing units Load balancing Load management Partitioning algorithms Pipelines Training
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