Adaptive Message Quantization and Parallelization for Distributed Full-graph GNN Training

descriptionPublicationkeyboard_double_arrow_right Article , Conference object , Preprint 01 Jan 2023Embargo end date: 01 Jan 2023Publisher:ZenodoJournal:CoRR, volume abs/2306.01381

Authors: Borui Wan; Juntao Zhao 0002; Chuan Wu 0001;

doi: 10.5281/zenodo.7781827 , 10.48550/arxiv.2306.01381 , 10.5281/zenodo.7783787 , 10.5281/zenodo.7781826

arXiv: 2306.01381

Adaptive Message Quantization and Parallelization for Distributed Full-graph GNN Training

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Abstract

Artifacts required for the evaluation of our MLSys 2023 paper.

Related Organizations

University of Hong Kong
China (People's Republic of)

Keywords

FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Artificial Intelligence, Bi-objective Optimization, Stochastic Integer Quantization, Distributed, Parallel, and Cluster Computing (cs.DC), Graph Neural Networks, Distributed Training, Machine Learning (cs.LG)

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