DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks

Full-batch training on Graph Neural Networks (GNN) to learn the structure of large graphs is a critical problem that needs to scale to hundreds of compute nodes to be feasible. It is challenging due to large memory capacity and bandwidth requirements on a single compute node and high communication v...

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Veröffentlicht in:	SC21: International Conference for High Performance Computing, Networking, Storage and Analysis S. 1 - 14
Hauptverfasser:	Md, Vasimuddin, Misra, Sanchit, Ma, Guixiang, Mohanty, Ramanarayan, Georganas, Evangelos, Heinecke, Alexander, Kalamkar, Dhiraj, Ahmed, Nesreen K., Avancha, Sasikanth
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	ACM 14.11.2021
Schlagworte:	Clustering algorithms Deep Graph Library Deep Learning Distributed Algorithm Graph Neural Networks Graph Partition High performance computing Memory management Proteins Social networking (online) Sockets Training
ISSN:	2167-4337
Online-Zugang:	Volltext
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