Efficient Scaling of Dynamic Graph Neural Networks

We present distributed algorithms for training dynamic Graph Neural Networks (GNN) on large scale graphs spanning multi-node, multi-GPU systems. To the best of our knowledge, this is the first scaling study on dynamic GNN. We devise mechanisms for reducing the GPU memory usage and identify two execu...

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Bibliographic Details

Published in:	SC21: International Conference for High Performance Computing, Networking, Storage and Analysis pp. 1 - 13
Main Authors:	Chakaravarthy, Venkatesan T., Pandian, Shivmaran S., Raje, Saurabh, Sabharwal, Yogish, Suzumura, Toyotaro, Ubaru, Shashanka
Format:	Conference Proceeding
Language:	English
Published:	ACM 14.11.2021
Subjects:	Data transfer Distributed algorithms dynamic graphs Graph neural networks Graphics processing units Heuristic algorithms High performance computing learning Training
ISSN:	2167-4337
Online Access:	Get full text
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