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With the increasing adoption of graph neural networks (GNNs) in the machine learning community, GPUs have become an essential tool to accelerate GNN training.
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Langley, P · 2000
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The WebGraph framework I: Compression techniques
Boldi, P. and Vigna, S · 2004
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Open graph benchmark: Datasets for machine learning on graphs
Hu, W., Fey, M., Zitnik, M., Dong, Y., Ren, H., Liu, B., Catasta, M., and Leskovec, J · 2005
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What is Twitter, a social network or a news media?
Kwak, H., Lee, C., Park, H., and Moon, S · 2010
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Torchvision the Machine-Vision Package of Torch
Marcel, S. and Rodriguez, Y · 2010
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Konect: The koblenz network collection
Kunegis, J · 2013
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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Beyond GPU Memory Limits with Unified Memory on Pascal, 2016
NVIDIA · 2016
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Inductive representation learning on large graphs
Hamilton, W. L., Ying, R., and Leskovec, J · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., and Leskovec, J · 2018
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
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Deep graph library: A graph-centric, highly-performant package for graph neural networks
Wang, M., Zheng, D., Ye, Z., Gan, Q., Li, M., Song, X., Zhou, J., Ma, C., Yu, L., Gai, Y., Xiao, T., He, T., Karypis, G., Li, J., and Zhang, Z · 2019
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Traversing large graphs on gpus with unified memory
Gera, P., Kim, H., Sao, P., Kim, H., and Bader, D · 2020
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Graph neural networks in tensorflow and keras with spektral
Grattarola, D. and Alippi, C · 2020
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G3: When graph neural networks meet parallel graph processing systems on gpus
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Zhou, J., Cui, G., Zhang, Z., Yang, C., Liu, Z., and Sun, M · 2018
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Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Chiang, W.-L., Liu, X., Si, S., Li, Y., Bengio, S., and Hsieh, C.-J · 2019
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Fast graph representation learning with PyTorch Geometric
Fey, M. and Lenssen, J. E · 2019
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Open graph benchmark: Datasets for machine learning on graphs, 2020b
Hu, W., Fey, M., Zitnik, M., Dong, Y., Ren, H., Liu, B., Catasta, M., and Leskovec, J
Cited in the paper.
Liu, H., Lu, S., Chen, X., and He, B · 2020
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Emogi: Efficient memory-access for out-of-memory graph-traversal in gpus
Min, S. W., Mailthody, V. S., Qureshi, Z., Xiong, J., Ebrahimi, E., and Hwu, W.-m · 2020
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Subway: Minimizing data transfer during out-of-gpu-memory graph processing
Sabet, A. H. N., Zhao, Z., and Gupta, R · 2020
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A comprehensive survey on graph neural networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Yu, P. S · 2020
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GraphSAINT: Graph sampling based inductive learning method
Zeng, H., Zhou, H., Srivastava, A., Kannan, R., and Prasanna, V · 2020
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