GraNNDis: Fast Distributed Graph Neural Network Training Framework for Multi-Server Clusters

Graph neural networks (GNNs) are one of the rapidly growing fields within deep learning. While many distributed GNN training frameworks have been proposed to increase the training throughput, they face three limitations when applied to multi-server clusters. 1) They suffer from an inter-server commu...

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Vydané v:	2024 33rd International Conference on Parallel Architectures and Compilation Techniques (PACT) s. 91 - 107
Hlavní autori:	Song, Jaeyong, Jang, Hongsun, Lim, Hunseong, Jung, Jaewon, Kim, Youngsok, Lee, Jinho
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	ACM 13.10.2024
Predmet:	Distributed Graph Neural Network Training Distributed Systems Graph Neural Network Training Graph neural networks Knowledge engineering Parallel architectures Redundancy Sampling methods Scalability Servers Systems for Deep Learning Throughput Training
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