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Graph Convolutional Networks (GCNs) is the state-of-the-art method for learning graph-structured data, and training large-scale GCNs requires distributed training across multiple accelerators such that each accelerator is able to hold a partitioned subgraph.
A fast and high quality multilevel scheme for partitioning irregular graphs
George Karypis and Vipin Kumar · 1998
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Hogwild!: A lock-free approach to parallelizing stochastic gradient descent
Feng Niu, Benjamin Recht, Christopher Ré, and Stephen J Wright · 2011
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More effective distributed ml via a stale synchronous parallel parameter server
Qirong Ho, James Cipar, Henggang Cui, Jin Kyu Kim, Seunghak Lee, Phillip B Gibbons, Garth A Gibson, Gregory R Ganger, and Eric P Xing · 2013
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Communication efficient distributed machine learning with the parameter server
Mu Li, David G Andersen, Alexander J Smola, and Kai Yu · 2014
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1-bit stochastic gradient descent and its application to data-parallel distributed training of speech dnns
Frank Seide, Hao Fu, Jasha Droppo, Gang Li, and Dong Yu · 2014
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Qsgd: Communication-efficient sgd via gradient quantization and encoding
Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Terngrad: Ternary gradients to reduce communication in distributed deep learning
Wei Wen, Cong Xu, Feng Yan, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2017
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Stochastic training of graph convolutional networks with variance reduction
Jianfei Chen, Jun Zhu, and Le Song · 2018
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Pipedream: Fast and efficient pipeline parallel dnn training
Aaron Harlap, Deepak Narayanan, Amar Phanishayee, Vivek Seshadri, Nikhil Devanur, Greg Ganger, and Phil Gibbons · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Graph convolutional neural networks for web-scale recommender systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec · 2018
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Gradiveq: Vector quantization for bandwidth-efficient gradient aggregation in distributed cnn training
Mingchao Yu, Zhifeng Lin, Krishna Giri Narra, Songze Li, Youjie Li, Nam Sung Kim, Alexander Schwing, Murali Annavaram, and Salman Avestimehr · 2018
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Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen · 2018
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Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh · 2019
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Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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Improving the accuracy, scalability, and performance of graph neural networks with roc
Zhihao Jia, Sina Lin, Mingyu Gao, Matei Zaharia, and Alex Aiken · 2020
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A pac-bayesian approach to generalization bounds for graph neural networks
Renjie Liao, Raquel Urtasun, and Richard Zemel · 2020
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Reducing communication in graph neural network training
Alok Tripathy, Katherine Yelick, and Aydin Buluc · 2020
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Neugraph: parallel deep neural network computation on large graphs
Lingxiao Ma, Zhi Yang, Youshan Miao, Jilong Xue, Ming Wu, Lidong Zhou, and Yafei Dai · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Deep graph library: A graph-centric, highly-performant package for graph neural networks
Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, and Zheng Zhang · 2019
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Aligraph: A comprehensive graph neural network platform
Rong Zhu, Kun Zhao, Hongxia Yang, Wei Lin, Chang Zhou, Baole Ai, Yong Li, and Jingren Zhou · 2019
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Minimal variance sampling with provable guarantees for fast training of graph neural networks
Weilin Cong, Rana Forsati, Mahmut Kandemir, and Mehrdad Mahdavi · 2020
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Generalization and representational limits of graph neural networks
Vikas Garg, Stefanie Jegelka, and Tommi Jaakkola · 2020
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A network-centric hardware/algorithm co-design to accelerate distributed training of deep neural networks
Youjie Li, Jongse Park, Mohammad Alian, Yifan Yuan, Zheng Qu, Peitian Pan, Ren Wang, Alexander Gerhard Schwing, Hadi Esmaeilzadeh, and Nam Sung Kim
Cited in the paper.
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna · 2020
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On the importance of sampling in learning graph convolutional networks
Weilin Cong, Morteza Ramezani, and Mehrdad Mahdavi · 2021
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P3: Distributed deep graph learning at scale
Swapnil Gandhi and Anand Padmanabha Iyer · 2021
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Dorylus: affordable, scalable, and accurate gnn training with distributed cpu servers and serverless threads
John Thorpe, Yifan Qiao, Jonathan Eyolfson, Shen Teng, Guanzhou Hu, Zhihao Jia, Jinliang Wei, Keval Vora, Ravi Netravali, Miryung Kim, et al · 2021
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Pipemare: Asynchronous pipeline parallel dnn training
Bowen Yang, Jian Zhang, Jonathan Li, Christopher Ré, Christopher Aberger, and Christopher De Sa · 2021
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EXACT: Scalable graph neural networks training via extreme activation compression
Zirui Liu, Kaixiong Zhou, Fan Yang, Li Li, Rui Chen, and Xia Hu · 2022
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BNS-GCN: Efficient full-graph training of graph convolutional networks with partition-parallelism and random boundary node sampling
Cheng Wan, Youjie Li, Ang Li, Nam Sung Kim, and Yingyan Lin · 2022
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