{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T19:27:37Z","timestamp":1762543657442,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":41,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T00:00:00Z","timestamp":1656374400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NSF","award":["CCF-2028825"],"award-info":[{"award-number":["CCF-2028825"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,6,28]]},"DOI":"10.1145\/3524059.3532384","type":"proceedings-article","created":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T16:13:11Z","timestamp":1655395991000},"page":"1-10","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Rethinking graph data placement for graph neural network training on multiple GPUs"],"prefix":"10.1145","author":[{"given":"Shihui","family":"Song","sequence":"first","affiliation":[{"name":"The University of Iowa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Jiang","sequence":"additional","affiliation":[{"name":"The University of Iowa"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,6,28]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3065737"},{"key":"e_1_3_2_1_2_1","volume-title":"International Conference on Learning Representations.","author":"Chen Jie","year":"2018","unstructured":"Jie Chen , Tengfei Ma , and Cao Xiao . 2018 . FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling . In International Conference on Learning Representations. Jie Chen, Tengfei Ma, and Cao Xiao. 2018. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330925"},{"key":"e_1_3_2_1_4_1","unstructured":"Alberto Garcia Duran and Mathias Niepert. 2017. Learning graph representations with embedding propagation. In Advances in neural information processing systems. 5119--5130.  Alberto Garcia Duran and Mathias Niepert. 2017. Learning graph representations with embedding propagation. In Advances in neural information processing systems. 5119--5130."},{"volume-title":"Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds.","author":"Fey Matthias","key":"e_1_3_2_1_5_1","unstructured":"Matthias Fey and Jan E. Lenssen . 2019 . Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds. Matthias Fey and Jan E. Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds."},{"volume-title":"Neural Message Passing for Quantum Chemistry. In International Conference on Machine Learning. 1263--1272","author":"Gilmer Justin","key":"e_1_3_2_1_6_1","unstructured":"Justin Gilmer , Samuel S. Schoenholz , Patrick F. Riley , Oriol Vinyals , and George E. Dahl . 2017 . Neural Message Passing for Quantum Chemistry. In International Conference on Machine Learning. 1263--1272 . Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. 2017. Neural Message Passing for Quantum Chemistry. In International Conference on Machine Learning. 1263--1272."},{"key":"e_1_3_2_1_7_1","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec 2017. Inductive representation learning on large graphs. In Advances in neural information processing systems. 1024--1034.  Will Hamilton Zhitao Ying and Jure Leskovec 2017. Inductive representation learning on large graphs. In Advances in neural information processing systems. 1024--1034."},{"key":"e_1_3_2_1_8_1","unstructured":"Loc Hoang Xuhao Chen Hochan Lee Roshan Dathathri Gurbinder Gill and Keshav Pingali. [n.d.]. EFFICIENT DISTRIBUTION FOR DEEP LEARNING ON LARGE GRAPHS update 1050 ([n.d.]) 1.  Loc Hoang Xuhao Chen Hochan Lee Roshan Dathathri Gurbinder Gill and Keshav Pingali. [n.d.]. EFFICIENT DISTRIBUTION FOR DEEP LEARNING ON LARGE GRAPHS update 1050 ([n.d.]) 1."},{"key":"e_1_3_2_1_9_1","volume-title":"Ogb-lsc: A large-scale challenge for machine learning on graphs. arXiv preprint arXiv:2103.09430","author":"Hu Weihua","year":"2021","unstructured":"Weihua Hu , Matthias Fey , Hongyu Ren , Maho Nakata , Yuxiao Dong , and Jure Leskovec . 2021 . Ogb-lsc: A large-scale challenge for machine learning on graphs. arXiv preprint arXiv:2103.09430 (2021). Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec. 2021. Ogb-lsc: A large-scale challenge for machine learning on graphs. arXiv preprint arXiv:2103.09430 (2021)."},{"key":"e_1_3_2_1_10_1","volume-title":"Open graph benchmark: Datasets for machine learning on graphs. arXiv preprint arXiv:2005.00687","author":"Hu Weihua","year":"2020","unstructured":"Weihua Hu , Matthias Fey , Marinka Zitnik , Yuxiao Dong , Hongyu Ren , Bowen Liu , Michele Catasta , and Jure Leskovec . 2020. Open graph benchmark: Datasets for machine learning on graphs. arXiv preprint arXiv:2005.00687 ( 2020 ). Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020. Open graph benchmark: Datasets for machine learning on graphs. arXiv preprint arXiv:2005.00687 (2020)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/SC41405.2020.00075"},{"key":"e_1_3_2_1_12_1","volume-title":"Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis","author":"Huang Guyue","year":"2020","unstructured":"Guyue Huang , Guohao Dai , Yu Wang , and Huazhong Yang . 2020 . GE-SpMM: General-Purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks . In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis ( Atlanta, Georgia) (SC '20). Guyue Huang, Guohao Dai, Yu Wang, and Huazhong Yang. 2020. GE-SpMM: General-Purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (Atlanta, Georgia) (SC '20)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437801.3441585"},{"key":"e_1_3_2_1_14_1","volume-title":"Proceedings of Machine Learning and Systems (MLSys)","author":"Jia Zhihao","year":"2020","unstructured":"Zhihao Jia , Sina Lin , Mingyu Gao , Matei Zaharia , and Alex Aiken . 2020 . Improving the accuracy, scalability, and performance of graph neural networks with roc . Proceedings of Machine Learning and Systems (MLSys) (2020), 187--198. Zhihao Jia, Sina Lin, Mingyu Gao, Matei Zaharia, and Alex Aiken. 2020. Improving the accuracy, scalability, and performance of graph neural networks with roc. Proceedings of Machine Learning and Systems (MLSys) (2020), 187--198."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.5555\/305219.305248"},{"volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations (ICLR).","author":"Thomas","key":"e_1_3_2_1_16_1","unstructured":"Thomas N. Kipf and Max Welling. 2017 . Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations (ICLR). Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_17_1","first-page":"120","article-title":"Pytorch-biggraph: A large scale graph embedding system","volume":"1","author":"Lerer Adam","year":"2019","unstructured":"Adam Lerer , Ledell Wu , Jiajun Shen , Timothee Lacroix , Luca Wehrstedt , Abhijit Bose , and Alex Peysakhovich . 2019 . Pytorch-biggraph: A large scale graph embedding system . Proceedings of Machine Learning and Systems 1 (2019), 120 -- 131 . Adam Lerer, Ledell Wu, Jiajun Shen, Timothee Lacroix, Luca Wehrstedt, Abhijit Bose, and Alex Peysakhovich. 2019. Pytorch-biggraph: A large scale graph embedding system. Proceedings of Machine Learning and Systems 1 (2019), 120--131.","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_3_2_1_18_1","volume-title":"Adaptive Graph Convolutional Neural Networks. In AAAI Conference on Artificial Intelligence.","author":"Li Ruoyu","year":"2018","unstructured":"Ruoyu Li , Sheng Wang , Feiyun Zhu , and Junzhou Huang . 2018 . Adaptive Graph Convolutional Neural Networks. In AAAI Conference on Artificial Intelligence. Ruoyu Li, Sheng Wang, Feiyun Zhu, and Junzhou Huang. 2018. Adaptive Graph Convolutional Neural Networks. In AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_1_19_1","volume-title":"Learning deep generative models of graphs. arXiv preprint arXiv:1803.03324","author":"Li Yujia","year":"2018","unstructured":"Yujia Li , Oriol Vinyals , Chris Dyer , Razvan Pascanu , and Peter Battaglia . 2018. Learning deep generative models of graphs. arXiv preprint arXiv:1803.03324 ( 2018 ). Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, and Peter Battaglia. 2018. Learning deep generative models of graphs. arXiv preprint arXiv:1803.03324 (2018)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3419111.3421281"},{"key":"e_1_3_2_1_21_1","unstructured":"Lingxiao Ma Zhi Yang Youshan Miao Jilong Xue Ming Wu Lidong Zhou and Yafei Dai. 2019. Neugraph: parallel deep neural network computation on large graphs. In 2019 {USENIX} Annual Technical Conference ({USENIX} {ATC} 19). 443--458.  Lingxiao Ma Zhi Yang Youshan Miao Jilong Xue Ming Wu Lidong Zhou and Yafei Dai. 2019. Neugraph: parallel deep neural network computation on large graphs. In 2019 { USENIX } Annual Technical Conference ( { USENIX } { ATC } 19). 443--458."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476249.3476264"},{"key":"e_1_3_2_1_23_1","volume-title":"Marius: Learning Massive Graph Embeddings on a Single Machine. In 15th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 21). 533--549.","author":"Mohoney Jason","year":"2021","unstructured":"Jason Mohoney , Roger Waleffe , Henry Xu , Theodoros Rekatsinas , and Shivaram Venkataraman . 2021 . Marius: Learning Massive Graph Embeddings on a Single Machine. In 15th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 21). 533--549. Jason Mohoney, Roger Waleffe, Henry Xu, Theodoros Rekatsinas, and Shivaram Venkataraman. 2021. Marius: Learning Massive Graph Embeddings on a Single Machine. In 15th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 21). 533--549."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/SC41405.2020.00060"},{"key":"e_1_3_2_1_25_1","volume-title":"Conference on Learning Theory. JMLR Workshop and Conference Proceedings, 11--1.","author":"Recht Benjamin","year":"2012","unstructured":"Benjamin Recht and Christopher R\u00e9 . 2012 . Toward a noncommutative arithmetic-geometric mean inequality: conjectures, case-studies, and consequences . In Conference on Learning Theory. JMLR Workshop and Conference Proceedings, 11--1. Benjamin Recht and Christopher R\u00e9. 2012. Toward a noncommutative arithmetic-geometric mean inequality: conjectures, case-studies, and consequences. In Conference on Learning Theory. JMLR Workshop and Conference Proceedings, 11--1."},{"key":"e_1_3_2_1_26_1","unstructured":"John Thorpe Yifan Qiao Jonathan Eyolfson Shen Teng Guanzhou Hu Zhihao Jia Jinliang Wei Keval Vora Ravi Netravali Miryung Kim etal 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_3_2_1_27_1","unstructured":"Minjie Wang Lingfan Yu Da Zheng Quan Gan Yu Gai Zihao Ye Mufei Li Jinjing Zhou Qi Huang Chao Ma etal 2019. Deep graph library: Towards efficient and scalable deep learning on graphs. arXiv preprint arXiv:1909.01315 (2019).  Minjie Wang Lingfan Yu Da Zheng Quan Gan Yu Gai Zihao Ye Mufei Li Jinjing Zhou Qi Huang Chao Ma et al. 2019. Deep graph library: Towards efficient and scalable deep learning on graphs. arXiv preprint arXiv:1909.01315 (2019)."},{"volume-title":"15th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 21). 515--531.","author":"Wang Yuke","key":"e_1_3_2_1_28_1","unstructured":"Yuke Wang , Boyuan Feng , Gushu Li , Shuangchen Li , Lei Deng , Yuan Xie , and Yufei Ding . 2021. GNNAdvisor: An Adaptive and Efficient Runtime System for {GNN} Acceleration on GPUs . In 15th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 21). 515--531. Yuke Wang, Boyuan Feng, Gushu Li, Shuangchen Li, Lei Deng, Yuan Xie, and Yufei Ding. 2021. GNNAdvisor: An Adaptive and Efficient Runtime System for {GNN} Acceleration on GPUs. In 15th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 21). 515--531."},{"key":"e_1_3_2_1_29_1","volume-title":"International Conference on Learning Representations.","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu , Weihua Hu , Jure Leskovec , and Stefanie Jegelka . 2019 . How Powerful are Graph Neural Networks? . In International Conference on Learning Representations. Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_1_31_1","unstructured":"Zhitao Ying Jiaxuan You Christopher Morris Xiang Ren Will Hamilton and Jure Leskovec. 2018. Hierarchical graph representation learning with differentiable pooling. In Advances in neural information processing systems. 4800--4810.  Zhitao Ying Jiaxuan You Christopher Morris Xiang Ren Will Hamilton and Jure Leskovec. 2018. Hierarchical graph representation learning with differentiable pooling. In Advances in neural information processing systems. 4800--4810."},{"key":"e_1_3_2_1_32_1","volume-title":"GraphSAINT: Graph Sampling Based Inductive Learning Method. In International Conference on Learning Representations.","author":"Zeng Hanqing","year":"2020","unstructured":"Hanqing Zeng , Hongkuan Zhou , Ajitesh Srivastava , Rajgopal Kannan , and Viktor Prasanna . 2020 . GraphSAINT: Graph Sampling Based Inductive Learning Method. In International Conference on Learning Representations. Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. 2020. GraphSAINT: Graph Sampling Based Inductive Learning Method. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415539"},{"key":"e_1_3_2_1_34_1","volume-title":"Gaan: Gated attention networks for learning on large and spatiotemporal graphs. arXiv preprint arXiv:1803.07294","author":"Zhang Jiani","year":"2018","unstructured":"Jiani Zhang , Xingjian Shi , Junyuan Xie , Hao Ma , Irwin King , and Dit-Yan Yeung . 2018 . Gaan: Gated attention networks for learning on large and spatiotemporal graphs. arXiv preprint arXiv:1803.07294 (2018). Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, and Dit-Yan Yeung. 2018. Gaan: Gated attention networks for learning on large and spatiotemporal graphs. arXiv preprint arXiv:1803.07294 (2018)."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3097996"},{"key":"e_1_3_2_1_36_1","unstructured":"Muhan Zhang and Yixin Chen. 2018. Link prediction based on graph neural networks. In Advances in Neural Information Processing Systems. 5165--5175.  Muhan Zhang and Yixin Chen. 2018. Link prediction based on graph neural networks. In Advances in Neural Information Processing Systems. 5165--5175."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/IA351965.2020.00011"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401172"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.14778\/3352063.3352127"},{"key":"e_1_3_2_1_40_1","volume-title":"Gemini: A computation-centric distributed graph processing system. In 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16). 301--316.","author":"Zhu Xiaowei","year":"2016","unstructured":"Xiaowei Zhu , Wenguang Chen , Weimin Zheng , and Xiaosong Ma . 2016 . Gemini: A computation-centric distributed graph processing system. In 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16). 301--316. Xiaowei Zhu, Wenguang Chen, Weimin Zheng, and Xiaosong Ma. 2016. Gemini: A computation-centric distributed graph processing system. In 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16). 301--316."},{"key":"e_1_3_2_1_41_1","unstructured":"Difan Zou Ziniu Hu Yewen Wang Song Jiang Yizhou Sun and Quanquan Gu. 2019. Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks. In Advances in Neural Information Processing Systems. 11249--11259.  Difan Zou Ziniu Hu Yewen Wang Song Jiang Yizhou Sun and Quanquan Gu. 2019. Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks. In Advances in Neural Information Processing Systems. 11249--11259."}],"event":{"name":"ICS '22: 2022 International Conference on Supercomputing","sponsor":["SIGARCH ACM Special Interest Group on Computer Architecture"],"location":"Virtual Event","acronym":"ICS '22"},"container-title":["Proceedings of the 36th ACM International Conference on Supercomputing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3524059.3532384","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3524059.3532384","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3524059.3532384","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:38Z","timestamp":1750188638000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3524059.3532384"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,28]]},"references-count":41,"alternative-id":["10.1145\/3524059.3532384","10.1145\/3524059"],"URL":"https:\/\/doi.org\/10.1145\/3524059.3532384","relation":{},"subject":[],"published":{"date-parts":[[2022,6,28]]},"assertion":[{"value":"2022-06-28","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}