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In our solution, the large adjacency and vertex-feature matrices are partitioned among processors. We exploit the vertex-partitioning of the graph to use non-blocking point-to-point communication operations between processors for better scalability. To further minimize the parallelization overheads, we introduce a sparse matrix partitioning scheme based on a hypergraph partitioning model for full-batch training. We also propose a novel stochastic hypergraph model to encode the expected communication volume in mini-batch training. We show the merits of the hypergraph model, previously unexplored for GCN training, over the standard graph partitioning model which does not accurately encode the communication costs. Experiments performed on real-world graph datasets demonstrate that the proposed algorithms achieve considerable speedups over alternative solutions. The optimizations achieved on communication costs become even more pronounced at high scalability with many processors. The performance benefits are preserved in deeper GCNs having more layers as well as on billion-scale graphs.<\/jats:p>","DOI":"10.14778\/3574245.3574256","type":"journal-article","created":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T23:14:12Z","timestamp":1677021252000},"page":"711-724","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Scalable Graph Convolutional Network Training on Distributed-Memory Systems"],"prefix":"10.14778","volume":"16","author":[{"given":"Gunduz Vehbi","family":"Demirci","sequence":"first","affiliation":[{"name":"Imagination Technologies, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aparajita","family":"Haldar","sequence":"additional","affiliation":[{"name":"University of Warwick, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hakan","family":"Ferhatosmanoglu","sequence":"additional","affiliation":[{"name":"University of Warwick, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,2,21]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2966884.2966912"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2312005.2312044"},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. 2883--2885","author":"Band Neil","year":"2020","unstructured":"Neil Band . 2020 . 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The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains . IEEE signal processing magazine 30, 3 ( 2013 ), 83--98. David I Shuman, Sunil K Narang, Pascal Frossard, Antonio Ortega, and Pierre Vandergheynst. 2013. The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30, 3 (2013), 83--98."},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3079079.3079097"},{"key":"e_1_2_1_52_1","volume-title":"15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21)","author":"Thorpe John","year":"2021","unstructured":"John Thorpe , Yifan Qiao , Jonathan Eyolfson , Shen Teng , Guanzhou Hu , Zhihao Jia , Jinliang Wei , Keval Vora , Ravi Netravali , Miryung Kim , 2021 . Dorylus: Affordable, Scalable, and Accurate {GNN} Training with Distributed {CPU} Servers and Serverless Threads . In 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21) . 495--514. John Thorpe, Yifan Qiao, Jonathan Eyolfson, Shen Teng, Guanzhou Hu, Zhihao Jia, Jinliang Wei, Keval Vora, Ravi Netravali, Miryung Kim, et al. 2021. Dorylus: Affordable, Scalable, and Accurate {GNN} Training with Distributed {CPU} Servers and Serverless Threads. In 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21). 495--514."},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.14778\/2732232.2732238"},{"key":"e_1_2_1_54_1","volume-title":"Reducing Communication in Graph Neural Network Training. In 2020 SC20: International Conference for High Performance Computing, Networking, Storage and Analysis (SC). IEEE Computer Society, 987--1000","author":"Tripathy Alok","unstructured":"Alok Tripathy , Katherine Yelick , and Aydin Buluc . [n.d.]. Reducing Communication in Graph Neural Network Training. In 2020 SC20: International Conference for High Performance Computing, Networking, Storage and Analysis (SC). IEEE Computer Society, 987--1000 . Alok Tripathy, Katherine Yelick, and Aydin Buluc. [n.d.]. Reducing Communication in Graph Neural Network Training. In 2020 SC20: International Conference for High Performance Computing, Networking, Storage and Analysis (SC). IEEE Computer Society, 987--1000."},{"key":"e_1_2_1_55_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Veli\u010dkovi\u0107 Petar","year":"2017","unstructured":"Petar Veli\u010dkovi\u0107 , Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 ( 2017 ). Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_2_1_56_1","unstructured":"Minjie Wang Da Zheng Zihao Ye Quan Gan Mufei Li Xiang Song Jinjing Zhou Chao Ma Lingfan Yu Yu Gai etal 2019. Deep graph library: A graph-centric highly-performant package for graph neural networks. arXiv preprint arXiv:1909.01315 (2019).  Minjie Wang Da Zheng Zihao Ye Quan Gan Mufei Li Xiang Song Jinjing Zhou Chao Ma Lingfan Yu Yu Gai et al. 2019. Deep graph library: A graph-centric highly-performant package for graph neural networks. arXiv preprint arXiv:1909.01315 (2019)."},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3003665.3003669"},{"key":"e_1_2_1_58_1","volume-title":"International conference on machine learning. PMLR, 6861--6871","author":"Wu Felix","year":"2019","unstructured":"Felix Wu , Amauri Souza , Tianyi Zhang , Christopher Fifty , Tao Yu , and Kilian Weinberger . 2019 . Simplifying graph convolutional networks . In International conference on machine learning. PMLR, 6861--6871 . Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In International conference on machine learning. PMLR, 6861--6871."},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/2484425.2484427"},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.14778\/2733085.2733103"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3340404"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/2213836.2213895"},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_2_1_64_1","volume-title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875","author":"Yu Bing","year":"2017","unstructured":"Bing Yu , Haoteng Yin , and Zhanxing Zhu . 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 ( 2017 ). Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017)."},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415539"},{"key":"e_1_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.14778\/3467861.3467867"},{"key":"e_1_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.14778\/2732232.2732236"},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.14778\/3514061.3514069"},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1109\/IA351965.2020.00011"},{"key":"e_1_2_1_70_1","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4582--4591. 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[71] Hongkuan Zhou, Ajitesh Srivastava, Hanqing Zeng, Rajgopal Kannan, and Viktor Prasanna. 2021. Accelerating large scale real-time GNN inference using channel pruning. 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