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Accelerating Backward Aggregation in GCN Training with Execution Path Preparing on GPUs. (arXiv:2204.02662v2 [cs.LG] UPDATED)
Oct. 11, 2022, 1:14 a.m. | Shaoxian Xu, Zhiyuan Shao, Ci Yang, Xiaofei Liao, Hai Jin
cs.LG updates on arXiv.org arxiv.org
The emerging Graph Convolutional Network (GCN) has now been widely used in
many domains, and it is challenging to improve the efficiencies of applications
by accelerating the GCN trainings. For the sparsity nature and exploding scales
of input real-world graphs, state-of-the-art GCN training systems (e.g.,
GNNAdvisor) employ graph processing techniques to accelerate the message
exchanging (i.e. aggregations) among the graph vertices. Nevertheless, these
systems treat both the aggregation stages of forward and backward propagation
phases as all-active graph processing procedures …
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