Reducing Communication in Graph Neural Network Training

Graph Neural Networks (GNNs) are powerful and flexible neural networks that use the naturally sparse connectivity information of the data. GNNs represent this connectivity as sparse matrices, which have lower arithmetic intensity and thus higher communication costs compared to dense matrices, making...

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Veröffentlicht in:	International Conference for High Performance Computing, Networking, Storage and Analysis (Online) Jg. 2020; S. 1 - 14
Hauptverfasser:	Tripathy, Alok, Yelick, Katherine, Buluc, Aydin
Format:	Tagungsbericht Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.11.2020
Schlagworte:	Clustering algorithms communication-avoiding algorithms distributed training Graph neural networks MATHEMATICS AND COMPUTING Proteins Sparse matrices Three-dimensional displays Training Two dimensional displays
ISSN:	2167-4329
Online-Zugang:	Volltext
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