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DTSTAMP:20210402T160551Z
LOCATION:Track 4
DTSTART;TZID=America/New_York:20201118T150000
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UID:submissions.supercomputing.org_SC20_sess177@linklings.com
SUMMARY:Graph Neural Networks
DESCRIPTION:Paper\n\nFeatGraph: A Flexible and Efficient Backend for Graph
  Neural Network Systems\n\nHu, Ye, Wang, Yu, Zheng...\n\nGraph neural netw
 orks (GNNs) are gaining increasing popularity as a promising approach to m
 achine learning on graphs. Unlike traditional graph workloads where each v
 ertex/edge is associated with a scalar, GNNs attach a feature tensor to ea
 ch vertex/edge. This additional feature dimension, along with...\n\n------
 ---------------\nGE-SpMM: General-Purpose Sparse Matrix-Matrix Multiplicat
 ion on GPUs for Graph Neural Networks\n\nHuang, Dai, Wang, Yang\n\nThe acc
 eleration of Graph Neural Networks (GNNs) requires efficient and framework
 -compatible Sparse-Dense Matrix-Matrix Multiplication (SpMM). From the com
 patibility perspective, the sophisticated sparse matrix representations in
  state-of-the-art SpMM designs cause heavy preprocessing overhead for t...
 \n\n---------------------\nReducing Communication in Graph Neural Network 
 Training\n\nTripathy, Yelick, Buluc\n\nGraph Neural Networks (GNNs) are po
 werful and flexible neural networks that use the naturally sparse connecti
 vity information of the data. GNNs represent this connectivity as sparse m
 atrices, which have lower arithmetic intensity and thus higher communicati
 on costs compared to dense matrices, making...\n\n\nTag: Accelerators, FPG
 A, and GPUs, Linear Algebra, Machine Learning, Deep Learning and Artificia
 l Intelligence\n\nRegistration Category: Tech Program Reg Pass
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