AdaGL: Adaptive Learning for Agile Distributed Training of Gigantic GNNs

Distributed GNN training on contemporary massive and densely connected graphs requires information aggregation from all neighboring nodes, which leads to an explosion of inter-server communications. This paper proposes AdaGL, a highly scalable end-to-end framework for rapid distributed GNN training....

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Published in2023 60th ACM/IEEE Design Automation Conference (DAC) pp. 1 - 6
Main Authors Zhang, Ruisi, Javaheripi, Mojan, Ghodsi, Zahra, Bleiweiss, Amit, Koushanfar, Farinaz
Format Conference Proceeding
LanguageEnglish
Published IEEE 09.07.2023
Subjects
Adaptive learning
Design automation
Explosions
Linear programming
Servers
Training
Online AccessGet full text
DOI10.1109/DAC56929.2023.10248003

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Abstract Distributed GNN training on contemporary massive and densely connected graphs requires information aggregation from all neighboring nodes, which leads to an explosion of inter-server communications. This paper proposes AdaGL, a highly scalable end-to-end framework for rapid distributed GNN training. AdaGL novelty lies upon our adaptive-learning based graph-allocation engine as well as utilizing multi-resolution coarse representation of dense graphs. As a result, AdaGL achieves an unprecedented level of balanced server computation while minimizing the communication overhead. Extensive proof-of-concept evaluations on billion-scale graphs show AdaGL attains ∼30−40% faster convergence compared with prior arts.
AbstractList Distributed GNN training on contemporary massive and densely connected graphs requires information aggregation from all neighboring nodes, which leads to an explosion of inter-server communications. This paper proposes AdaGL, a highly scalable end-to-end framework for rapid distributed GNN training. AdaGL novelty lies upon our adaptive-learning based graph-allocation engine as well as utilizing multi-resolution coarse representation of dense graphs. As a result, AdaGL achieves an unprecedented level of balanced server computation while minimizing the communication overhead. Extensive proof-of-concept evaluations on billion-scale graphs show AdaGL attains ∼30−40% faster convergence compared with prior arts.
Author Koushanfar, Farinaz
Zhang, Ruisi
Ghodsi, Zahra
Bleiweiss, Amit
Javaheripi, Mojan
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SubjectTerms Adaptive learning
Design automation
Explosions
Linear programming
Servers
Training
Title AdaGL: Adaptive Learning for Agile Distributed Training of Gigantic GNNs
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