Fetching the paper…
Reading the bibliography…
Graph Neural Networks (GNNs) are powerful and flexible neural networks that use the naturally sparse connectivity information of the data.
A three-dimensional approach to parallel matrix multiplication
Ramesh C Agarwal, Susanne M Balle, Fred G Gustavson, Mahesh Joshi, and Prasad Palkar · 1995
Earlier work this paper cites.
SUMMA: Scalable universal matrix multiplication algorithm
Robert A Van De Geijn and Jerrell Watts · 1997
Earlier work this paper cites.
Collective communication: theory, practice, and experience
Ernie Chan, Marcel Heimlich, Avi Purkayastha, and Robert Van De Geijn · 2007
Earlier work this paper cites.
On the representation and multiplication of hypersparse matrices
Aydin Buluc and John R Gilbert · 2008
Earlier work this paper cites.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
Earlier work this paper cites.
Minimizing communication in numerical linear algebra
Grey Ballard, James Demmel, Olga Holtz, and Oded Schwartz · 2011
Earlier work this paper cites.
The Combinatorial BLAS: Design, implementation, and applications
Aydın Buluç and John R Gilbert · 2011
Earlier work this paper cites.
Cyclops tensor framework: Reducing communication and eliminating load imbalance in massively parallel contractions
Edgar Solomonik, Devin Matthews, Jeff Hammond, and James Demmel · 2013
Earlier work this paper cites.
Optimizing sparse matrix-multiple vectors multiplication for nuclear configuration interaction calculations
H. Metin Aktulga, Aydın Buluç, Samuel Williams, and Chao Yang · 2014
Earlier work this paper cites.
Exploiting multiple levels of parallelism in sparse matrix-matrix multiplication
Ariful Azad, Grey Ballard, Aydın Buluç, James Demmel, Laura Grigori, Oded Schwartz, Sivan Toledo, and Samuel Williams · 2016
Earlier work this paper cites.
Communication-avoiding parallel sparse-dense matrix-matrix multiplication
Penporn Koanantakool, Ariful Azad, Aydın Buluç, Dmitriy Morozov, Sang-Yun Oh, Leonid Oliker, and Katherine Yelick · 2016
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Earlier work this paper cites.
Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf and Max Welling · 2017
Cited alongside, same era.
HipMCL: a high-performance parallel implementation of the Markov clustering algorithm for large-scale networks
Ariful Azad, Georgios A Pavlopoulos, Christos A Ouzounis, Nikos C Kyrpides, and Aydın Buluç · 2018
Cited alongside, same era.
Integrated model, batch, and domain parallelism in training neural networks
Amir Gholami, Ariful Azad, Peter Jin, Kurt Keutzer, and Aydın Buluç · 2018
Cited alongside, same era.
Mesh-tensorflow: Deep learning for supercomputers
Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Koanantakool, Peter Hawkins, HyoukJoong Lee, Mingsheng Hong, Cliff Young, et al · 2018
Cited alongside, same era.
Design principles for sparse matrix multiplication on the GPU
Carl Yang, Aydın Buluç, and John D Owens · 2018
Cited alongside, same era.
Structure-based function prediction using graph convolutional networks
Vladimir Gligorijevic, P Douglas Renfrew, Tomasz Kosciolek, Julia Koehler Leman, Kyunghyun Cho, Tommi Vatanen, Daniel Berenberg, Bryn C Taylor, Ian M Fisk, Ramnik J Xavier, et al · 2019
Later among the works it cites.
PyTorch-BigGraph: A large-scale graph embedding system
Adam Lerer, Ledell Wu, Jiajun Shen, Timothée Lacroix, Luca Wehrstedt, Abhijit Bose, and Alexander Peysakhovich · 2019
Later among the works it cites.
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
Later among the works it cites.
Summit architecture overview
Tom Papatheodore · 2019
Later among the works it cites.
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
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2018
Cited alongside, same era.
Demystifying parallel and distributed deep learning: An in-depth concurrency analysis
Tal Ben-Nun and Torsten Hoefler · 2019
Cited alongside, same era.
Perlmutter - a 2020 pre-exascale gpu-accelerated system for nersc. architecture and early application performance optimization results
Jack Deslippe · 2019
Cited alongside, same era.
Improving strong-scaling of CNN training by exploiting finer-grained parallelism
Nikoli Dryden, Naoya Maruyama, Tom Benson, Tim Moon, Marc Snir, and Brian Van Essen · 2019
Cited alongside, same era.
Channel and filter parallelism for large-scale CNN training
Nikoli Dryden, Naoya Maruyama, Tim Moon, Tom Benson, Marc Snir, and Brian Van Essen · 2019
Cited alongside, same era.
Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
Cited alongside, same era.
Minjie Wang, Lingfan Yu, Da Zheng, Quan Gan, Yu Gai, Zihao Ye, Mufei Li, Jinjing Zhou, Qi Huang, Chao Ma, Ziyue Huang, Qipeng Guo, Hao Zhang, Haibin Lin, Junbo Zhao, Jinyang Li, Alexander J. Smola, and Zheng Zhang · 2019
Later among the works it cites.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Later among the works it cites.
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
Later among the works it cites.
NCCL: Optimized primitives for collective multi-gpu communication
NVIDIA Corporation · 2020
Closest in time.
Improving the accuracy, scalability, and performance of graph neural networks with ROC
Zhihao Jia, Sina Lin, Mingyu Gao, Matei Zaharia, and Alex Aiken · 2020
Closest in time.
A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
Closest in time.