Architectural Implications of GNN Aggregation Programming Abstractions

Graph neural networks (GNNs) have gained significant popularity due to the powerful capability to extract useful representations from graph data. As the need for efficient GNN computation intensifies, a variety of programming abstractions designed for optimizing GNN Aggregation have emerged to facil...

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Veröffentlicht in:	IEEE computer architecture letters Jg. 23; H. 1; S. 125 - 128
Hauptverfasser:	Qi, Yingjie, Yang, Jianlei, Zhou, Ao, Qiao, Tong, Hu, Chunming
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	characterization execution patterns Graph neural networks Graph neural networks (GNNs) Graphical representations Graphics processing units Indexes Kernel Organizations Programming programming abstractions Taxonomy
ISSN:	1556-6056, 1556-6064
Online-Zugang:	Volltext
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