Characterizing and Understanding HGNNs on GPUs
Author:
Affiliation:
1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
2. University of California, Santa Barbara, CA, USA
Funder
CAS Project for Young Scientists in Basic Research
CAS Project for Youth Innovation Promotion Association
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Hardware and Architecture
Link
http://xplorestaging.ieee.org/ielx7/10208/9810321/09855397.pdf?arnumber=9855397
Reference10 articles.
1. Semi-supervised classification with graph convolutional networks;kipf,2016
2. Graphite
3. Leveraging Hardware Caches for Memoization
4. Deep Graph Library: A graph-centric, highly-performant package for graph neural networks;wang,2019
5. Modeling Relational Data with Graph Convolutional Networks
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1. A Comprehensive Survey on Distributed Training of Graph Neural Networks;Proceedings of the IEEE;2023-12
2. Characterizing and Understanding Defense Methods for GNNs on GPUs;IEEE Computer Architecture Letters;2023-07
3. MetaNMP: Leveraging Cartesian-Like Product to Accelerate HGNNs with Near-Memory Processing;Proceedings of the 50th Annual International Symposium on Computer Architecture;2023-06-17
4. GraphMetaP: Efficient MetaPath Generation for Dynamic Heterogeneous Graph Models;2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS);2023-05
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