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Despite the recent success of Graph Neural Networks, it remains challenging to train a GNN on large graphs with millions of nodes and billions of edges, which are prevalent in many graph-based applications.
A fast and high quality multilevel scheme for partitioning irregular graphs
G. Karypis and V. Kumar · 1998
Earlier work this paper cites.
Revisiting distributed synchronous sgd
J. Chen, X. Pan, R. Monga, S. Bengio, and R. Jozefowicz · 2016
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Discriminative embeddings of latent variable models for structured data
H. Dai, B. Dai, and L. Song · 2016
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2016
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Flash storage disaggregation
A. Klimovic, C. Kozyrakis, E. Thereska, B. John, and S. Kumar · 2016
Earlier work this paper cites.
Freebase-triples: A methodology for processing the freebase data dumps
N. Chah · 2017
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Neural message passing for quantum chemistry
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
W. Hamilton, Z. Ying, and J. Leskovec · 2017
Earlier work this paper cites.
Decibel: Isolation and sharing in disaggregated Rack-Scale storage
M. Nanavati, J. Wires, and A. Warfield · 2017
Earlier work this paper cites.
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio · 2017
Earlier work this paper cites.
Asynchronous stochastic gradient descent with delay compensation
S. Zheng, Q. Meng, T. Wang, W. Chen, N. Yu, Z.-M. Ma, and T.-Y. Liu · 2017
Earlier work this paper cites.
Stochastic training of graph convolutional networks with variance reduction
J. Chen, J. Zhu, and L. Song · 2018
Earlier work this paper cites.
Pixie: A system for recommending 3+ billion items to 200+ million users in real-time
C. Eksombatchai, P. Jindal, J. Z. Liu, Y. Liu, R. Sharma, C. Sugnet, M. Ulrich, and J. Leskovec · 2018
Earlier work this paper cites.
Graph convolutional neural networks for web-scale recommender systems
R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec · 2018
Earlier work this paper cites.
Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
W.-L. Chiang, X. Liu, S. Si, Y. Li, S. Bengio, and C.-J. Hsieh · 2019
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Gcn-gan: A non-linear temporal link prediction model for weighted dynamic networks
K. Lei, M. Qin, B. Bai, G. Zhang, and M. Yang · 2019
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{ \{ NeuGraph } \} : Parallel deep neural network computation on large graphs
L. Ma, Z. Yang, Y. Miao, J. Xue, M. Wu, L. Zhou, and Y. Dai · 2019
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Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al · 2019
Cited alongside, same era.
Deep graph library: A graph-centric, highly-performant package for graph neural networks
M. Wang, D. Zheng, Z. Ye, Q. Gan, M. Li, X. Song, J. Zhou, C. Ma, L. Yu, Y. Gai, et al · 2019
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Improving the accuracy, scalability, and performance of graph neural networks with roc
Z. Jia, S. Lin, M. Gao, M. Zaharia, and A. Aiken · 2020
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Federated optimization in heterogeneous networks
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith · 2020
Later among the works it cites.
Capuchin: Tensor-based gpu memory management for deep learning
X. Peng, X. Shi, H. Dai, H. Jin, W. Ma, Q. Xiong, F. Yang, and X. Qian · 2020
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Reducing communication in graph neural network training
A. Tripathy, K. Yelick, and A. Buluç · 2020
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Distdgl: distributed graph neural network training for billion-scale graphs
D. Zheng, C. Ma, M. Wang, J. Zhou, Q. Su, X. Song, Q. Gan, Z. Zhang, and G. Karypis · 2020
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Fedat: a high-performance and communication-efficient federated learning system with asynchronous tiers
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Graphsaint: Graph sampling based inductive learning method
H. Zeng, H. Zhou, A. Srivastava, R. Kannan, and V. Prasanna · 2019
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Aligraph: a comprehensive graph neural network platform
R. Zhu, K. Zhao, H. Yang, W. Lin, C. Zhou, B. Ai, Y. Li, and J. Zhou · 2019
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Can far memory improve job throughput?
E. Amaro, C. Branner-Augmon, Z. Luo, A. Ousterhout, M. K. Aguilera, A. Panda, S. Ratnasamy, and S. Shenker · 2020
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Distributed training of graph convolutional networks using subgraph approximation
A. Angerd, K. Balasubramanian, and M. Annavaram · 2020
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Simple and deep graph convolutional networks
M. Chen, Z. Wei, Z. Huang, B. Ding, and Y. Li · 2020
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Asynchronous online federated learning for edge devices with non-iid data
Y. Chen, Y. Ning, M. Slawski, and H. Rangwala · 2020
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Principal neighbourhood aggregation for graph nets
G. Corso, L. Cavalleri, D. Beaini, P. Liò, and P. Veličković · 2020
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Z. Chai, Y. Chen, A. Anwar, L. Zhao, Y. Cheng, and H. Rangwala · 2021
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On the importance of sampling in learning graph convolutional networks
W. Cong, M. Ramezani, and M. Mahdavi · 2021
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Gnnautoscale: Scalable and expressive graph neural networks via historical embeddings
M. Fey, J. E. Lenssen, F. Weichert, and J. Leskovec · 2021
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P3: Distributed deep graph learning at scale
S. Gandhi and A. P. Iyer · 2021
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Learn locally, correct globally: A distributed algorithm for training graph neural networks
M. Ramezani, W. Cong, M. Mahdavi, M. T. Kandemir, and A. Sivasubramaniam · 2021
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Dorylus: Affordable, scalable, and accurate GNN training with distributed CPU servers and serverless threads
J. Thorpe, Y. Qiao, J. Eyolfson, S. Teng, G. Hu, Z. Jia, J. Wei, K. Vora, R. Netravali, M. Kim, and G. H. Xu · 2021
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GNNAdvisor: An adaptive and efficient runtime system for GNN acceleration on GPUs
Y. Wang, B. Feng, G. Li, S. Li, L. Deng, Y. Xie, and Y. Ding · 2021
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Memory harvesting in Multi-GPU systems with hierarchical unified virtual memory
S. Choi, T. Kim, J. Jeong, R. Ausavarungnirun, M. Jeon, Y. Kwon, and J. Ahn · 2022
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C. Wan, Y. Li, C. R. Wolfe, A. Kyrillidis, N. S. Kim, and Y. Lin · 2022
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