{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:46:13Z","timestamp":1775231173172,"version":"3.50.1"},"reference-count":73,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T00:00:00Z","timestamp":1739145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2025,2,10]]},"abstract":"<jats:p>\n                    Graph neural networks (GNNs) are models specialized for graph data and widely used in applications. To train GNNs on large graphs that exceed CPU memory, several systems have been designed to store data on disk and conduct out-of-core processing. However, these systems suffer from either\n                    <jats:italic toggle=\"yes\">read amplification<\/jats:italic>\n                    when conducting random reads for node features that are smaller than a disk page, or\n                    <jats:italic toggle=\"yes\">degraded model accuracy<\/jats:italic>\n                    by treating the graph as disconnected partitions. To close this gap, we build\n                    <jats:italic toggle=\"yes\">DiskGNN<\/jats:italic>\n                    for high I\/O efficiency and fast training without model accuracy degradation. The key technique is\n                    <jats:italic toggle=\"yes\">offline sampling<\/jats:italic>\n                    , which decouples\n                    <jats:italic toggle=\"yes\">graph sampling<\/jats:italic>\n                    from\n                    <jats:italic toggle=\"yes\">model computation<\/jats:italic>\n                    . In particular, by conducting graph sampling\n                    <jats:italic toggle=\"yes\">beforehand<\/jats:italic>\n                    for multiple mini-batches, DiskGNN acquires the node features that will be accessed during model computation and conducts pre-processing to pack the node features of each mini-batch contiguously on disk to avoid read amplification for computation. Given the feature access information acquired by offline sampling, DiskGNN also adopts designs including\n                    <jats:italic toggle=\"yes\">four-level feature store<\/jats:italic>\n                    to fully utilize the memory hierarchy of GPU and CPU to cache hot node features and reduce disk access,\n                    <jats:italic toggle=\"yes\">batched packing<\/jats:italic>\n                    to accelerate feature packing during pre-processing, and\n                    <jats:italic toggle=\"yes\">pipelined training<\/jats:italic>\n                    to overlap disk access with other operations. We compare DiskGNN with state-of-the-art out-of-core GNN training systems. The results show that DiskGNN has more than 8x speedup over existing systems while matching their best model accuracy. DiskGNN is open-source at https:\/\/github.com\/Liu-rj\/DiskGNN.\n                  <\/jats:p>","DOI":"10.1145\/3709738","type":"journal-article","created":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T15:45:06Z","timestamp":1739288706000},"page":"1-27","source":"Crossref","is-referenced-by-count":6,"title":["DiskGNN: Bridging I\/O Efficiency and Model Accuracy for Out-of-Core GNN Training"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4332-2762","authenticated-orcid":false,"given":"Renjie","family":"Liu","sequence":"first","affiliation":[{"name":"Southern University of Science and Technology, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3714-9326","authenticated-orcid":false,"given":"Yichuan","family":"Wang","sequence":"additional","affiliation":[{"name":"UC Berkeley, Berkeley, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2122-915X","authenticated-orcid":false,"given":"Xiao","family":"Yan","sequence":"additional","affiliation":[{"name":"Centre for Perceptual and Interactive Intelligence, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0350-1058","authenticated-orcid":false,"given":"Haitian","family":"Jiang","sequence":"additional","affiliation":[{"name":"New York University, New York, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0199-4866","authenticated-orcid":false,"given":"Zhenkun","family":"Cai","sequence":"additional","affiliation":[{"name":"Amazon, Santa Clara, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8156-1179","authenticated-orcid":false,"given":"Minjie","family":"Wang","sequence":"additional","affiliation":[{"name":"AWS Shanghai AI Lab, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8424-0092","authenticated-orcid":false,"given":"Bo","family":"Tang","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9574-1746","authenticated-orcid":false,"given":"Jinyang","family":"Li","sequence":"additional","affiliation":[{"name":"New York University, New York, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,2,11]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"https:\/\/aws.amazon.com\/. [Online","author":"AWS.","year":"2024","unstructured":"2024. AWS. https:\/\/aws.amazon.com\/. [Online; accessed Apirl-2024]."},{"key":"e_1_2_2_2_1","volume-title":"https:\/\/azure.microsoft.com. [Online","year":"2024","unstructured":"2024. Azure. https:\/\/azure.microsoft.com. [Online; accessed Apirl-2024]."},{"key":"e_1_2_2_3_1","volume-title":"Amazon EC2 G5 instance. https:\/\/aws.amazon.com\/cn\/ec2\/instance-types\/g5. [Online","author":"AWS.","year":"2024","unstructured":"AWS. 2024. Amazon EC2 G5 instance. https:\/\/aws.amazon.com\/cn\/ec2\/instance-types\/g5. [Online; accessed April-2024]."},{"key":"e_1_2_2_4_1","volume-title":"Tae Jun Ham, and Jae W. Lee","author":"Bae Jonghyun","year":"2021","unstructured":"Jonghyun Bae, Jongsung Lee, Yunho Jin, Sam Son, Shine Kim, Hakbeom Jang, Tae Jun Ham, and Jae W. Lee. 2021. FlashNeuron: SSD-Enabled Large-Batch Training of Very Deep Neural Networks. In FAST. 387--401."},{"key":"e_1_2_2_5_1","doi-asserted-by":"crossref","unstructured":"Zhenkun Cai Xiao Yan Yidi Wu Kaihao Ma James Cheng and Fan Yu. 2021. DGCL: an efficient communication library for distributed GNN training. In Eurosys. 130--144.","DOI":"10.1145\/3447786.3456233"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3572848.3577528"},{"key":"e_1_2_2_7_1","unstructured":"Jie Chen Tengfei Ma and Cao Xiao. 2018. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. In ICLR."},{"key":"e_1_2_2_8_1","unstructured":"Jianfei Chen Jun Zhu and Le Song. 2018. Stochastic Training of Graph Convolutional Networks with Variance Reduction. In ICML. 941--949."},{"key":"e_1_2_2_9_1","volume-title":"Deep Graph library. https:\/\/www.dgl.ai. [Online","author":"DGL.","year":"2024","unstructured":"DGL. 2024. Deep Graph library. https:\/\/www.dgl.ai. [Online; accessed Apirl-2024]."},{"key":"e_1_2_2_10_1","doi-asserted-by":"crossref","unstructured":"Zezhong Ding Yongan Xiang Shangyou Wang Xike Xie and S. Kevin Zhou. 2024. Play like a Vertex: A Stackelberg Game Approach for Streaming Graph Partitioning.","DOI":"10.1145\/3654965"},{"key":"e_1_2_2_11_1","doi-asserted-by":"crossref","unstructured":"Fuli Feng Xiangnan He Xiang Wang Cheng Luo Yiqun Liu and Tat-Seng Chua. 2019. Temporal Relational Ranking for Stock Prediction. ACM Trans. Inf. Syst. (2019) 27:1--27:30.","DOI":"10.1145\/3309547"},{"key":"e_1_2_2_12_1","volume-title":"Fast Graph Representation Learning with PyTorch Geometric. In ICLR 2019 Workshop on Representation Learning on Graphs and Manifolds.","author":"Fey Matthias","unstructured":"Matthias Fey and Jan E. Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. In ICLR 2019 Workshop on Representation Learning on Graphs and Manifolds."},{"key":"e_1_2_2_13_1","unstructured":"Swapnil Gandhi and Anand Padmanabha Iyer. 2021. P3: Distributed Deep Graph Learning at Scale. In OSDI. 551--568."},{"key":"e_1_2_2_14_1","unstructured":"Bin Gao Zhuomin He Puru Sharma Qingxuan Kang Djordje Jevdjic Junbo Deng Xingkun Yang Zhou Yu and Pengfei Zuo. 2024. Cost-Efficient Large Language Model Serving for Multi-turn Conversations with CachedAttention. In ATC. 111--126."},{"key":"e_1_2_2_15_1","unstructured":"Laura Garton Caroline Haythornthwaite and Barry Wellman. 1997. Studying Online Social Networks. J. Comput. Mediat. Commun. (1997)."},{"key":"e_1_2_2_16_1","doi-asserted-by":"crossref","unstructured":"Ping Gong Renjie Liu Zunyao Mao Zhenkun Cai Xiao Yan Cheng Li Minjie Wang and Zhuozhao Li. 2023. GSampler: General and Efficient GPU-Based Graph Sampling for Graph Learning. In SOSP. 562--578.","DOI":"10.1145\/3600006.3613168"},{"key":"e_1_2_2_17_1","unstructured":"William L. Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NeurIPS. 1024--1034."},{"key":"e_1_2_2_18_1","volume-title":"Reliable Graph Neural Networks for Drug Discovery Under Distributional Shift. In NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and Applications.","author":"Han Kehang","year":"2021","unstructured":"Kehang Han, Balaji Lakshminarayanan, and Jeremiah Zhe Liu. 2021. Reliable Graph Neural Networks for Drug Discovery Under Distributional Shift. In NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and Applications."},{"key":"e_1_2_2_19_1","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. In NeurIPS."},{"key":"e_1_2_2_20_1","unstructured":"Wenbing Huang Tong Zhang Yu Rong and Junzhou Huang. 2018. Adaptive Sampling towards Fast Graph Representation Learning. In NeurIPS. 4563--4572."},{"key":"e_1_2_2_21_1","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. In MLSys. 187--198."},{"key":"e_1_2_2_22_1","volume-title":"MuseGNN: Interpretable and Convergent Graph Neural Network Layers at Scale. arXiv preprint arXiv:2310.12457","author":"Jiang Haitian","year":"2023","unstructured":"Haitian Jiang, Renjie Liu, Xiao Yan, Zhenkun Cai, Minjie Wang, and David Wipf. 2023. MuseGNN: Interpretable and Convergent Graph Neural Network Layers at Scale. arXiv preprint arXiv:2310.12457 (2023)."},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1137\/S1064827595287997"},{"key":"e_1_2_2_24_1","volume-title":"Bhagyashree Taleka, Tengfei Ma, Xiang Song, and Wen-mei Hwu.","author":"Khatua Arpandeep","year":"2023","unstructured":"Arpandeep Khatua, Vikram Sharma Mailthody, Bhagyashree Taleka, Tengfei Ma, Xiang Song, and Wen-mei Hwu. 2023. IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research. In KDD."},{"key":"e_1_2_2_25_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR."},{"key":"e_1_2_2_26_1","unstructured":"Jure Leskovec and Andrej Krevl. 2014. SNAP Datasets: Stanford Large Network Dataset Collection. http:\/\/snap.stanford.edu\/data."},{"key":"e_1_2_2_27_1","unstructured":"Linux. 2024. io_uring. https:\/\/man7.org\/linux\/man-pages\/man7\/io_uring.7.html. accessed April-2024."},{"key":"e_1_2_2_28_1","unstructured":"Linux. 2024. POSIX open. https:\/\/man7.org\/linux\/man-pages\/man2\/open.2.html. accessed April-2024."},{"key":"e_1_2_2_29_1","unstructured":"Linux. 2024. POSIX pread. https:\/\/man7.org\/linux\/man-pages\/man2\/pwrite.2.html. accessed April-2024."},{"key":"e_1_2_2_30_1","unstructured":"Tianfeng Liu Yangrui Chen Dan Li Chuan Wu Yibo Zhu Jun He Yanghua Peng Hongzheng Chen Hongzhi Chen and Chuanxiong Guo. 2023. BGL:GPU-Efficient GNN training by optimizing graph data I\/O and preprocessing. In NSDI. 103--118."},{"key":"e_1_2_2_31_1","unstructured":"Ziqi Liu Zhengwei Wu Zhiqiang Zhang Jun Zhou Shuang Yang Le Song and Yuan Qi. 2020. Bandit Samplers for Training Graph Neural Networks. In NeurIPS."},{"key":"e_1_2_2_32_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 ATC. 443--458."},{"key":"e_1_2_2_33_1","volume-title":"Marius: Learning Massive Graph Embeddings on a Single Machine. In OSDI.","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 OSDI."},{"key":"e_1_2_2_34_1","volume-title":"https:\/\/developer.nvidia.com\/cuda-toolkit. [Online","author":"Toolkit NVIDIA.","year":"2024","unstructured":"NVIDIA. 2024. CUDA Toolkit. https:\/\/developer.nvidia.com\/cuda-toolkit. [Online; accessed Apirl-2024]."},{"key":"e_1_2_2_35_1","unstructured":"NVIDIA Corporation. 2024. Unified Addressing. https:\/\/docs.nvidia.com\/cuda\/cuda-driver-api\/group__CUDA__UNIFIED.html. accessed April-2024."},{"key":"e_1_2_2_36_1","unstructured":"OpenMP. 2024. OpenMP. https:\/\/www.openmp.org. accessed April-2024."},{"key":"e_1_2_2_37_1","doi-asserted-by":"crossref","unstructured":"Patrick O'Neil Edward Cheng Dieter Gawlick and Elizabeth O'Neil. 1996. The log-structured merge-tree (LSM-tree). Acta Inf. (1996) 351--385.","DOI":"10.1007\/s002360050048"},{"key":"e_1_2_2_38_1","volume-title":"Zaid Qureshi, and Wen-mei Hwu.","author":"Park Jeongmin Brian","year":"2024","unstructured":"Jeongmin Brian Park, Vikram Sharma Mailthody, Zaid Qureshi, and Wen-mei Hwu. 2024. Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses. In VLDB. 1227--1240."},{"key":"e_1_2_2_39_1","volume-title":"Lee","author":"Park Yeonhong","year":"2022","unstructured":"Yeonhong Park, Sunhong Min, and Jae W. Lee. 2022. Ginex: SSD-enabled billion-scale graph neural network training on a single machine via provably optimal in-memory caching. In VLDB. 2626--2639."},{"key":"e_1_2_2_40_1","volume-title":"Pytorch: An imperative style, high performance deep learning library. In NeurIPS.","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high performance deep learning library. In NeurIPS."},{"key":"e_1_2_2_41_1","volume-title":"PyTorch Geometric. https:\/\/www.pyg.org. [Online","author":"G.","year":"2024","unstructured":"PyG. 2024. PyTorch Geometric. https:\/\/www.pyg.org. [Online; accessed Apirl-2024]."},{"key":"e_1_2_2_42_1","volume-title":"https:\/\/www.python.org\/downloads\/release\/python-398\/. [Online","year":"2024","unstructured":"Python. 2024. Python. https:\/\/www.python.org\/downloads\/release\/python-398\/. [Online; accessed Apirl-2024]."},{"key":"e_1_2_2_43_1","volume-title":"https:\/\/pytorch.org. [Online","year":"2024","unstructured":"PyTorch. 2024. PyTroch. https:\/\/pytorch.org. [Online; accessed Apirl-2024]."},{"key":"e_1_2_2_44_1","doi-asserted-by":"crossref","unstructured":"Sungmin Rhee Seokjun Seo and Sun Kim. 2018. Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification. In IJCAI. 3527--3534.","DOI":"10.24963\/ijcai.2018\/490"},{"key":"e_1_2_2_45_1","volume-title":"Flexgen: High-throughput generative inference of large language models with a single gpu. In ICML. 31094--31116.","author":"Sheng Ying","year":"2023","unstructured":"Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin, Beidi Chen, Percy Liang, Christopher R\u00e9, Ion Stoica, and Ce Zhang. 2023. Flexgen: High-throughput generative inference of large language models with a single gpu. In ICML. 31094--31116."},{"key":"e_1_2_2_46_1","volume-title":"Legion: Automatically Pushing the Envelope of Multi-GPU System for Billion-Scale GNN Training. In ATC. 165--179.","author":"Sun Jie","year":"2023","unstructured":"Jie Sun, Li Su, Zuocheng Shi, Wenting Shen, Zeke Wang, Lei Wang, Jie Zhang, Yong Li, Wenyuan Yu, Jingren Zhou, et al. 2023. Legion: Automatically Pushing the Envelope of Multi-GPU System for Billion-Scale GNN Training. In ATC. 165--179."},{"key":"e_1_2_2_47_1","volume-title":"Helios: An Efficient Out-of-core GNN Training System on Terabyte-scale Graphs with In-memory Performance. arXiv preprint arXiv:2310.00837","author":"Sun Jie","year":"2023","unstructured":"Jie Sun, Mo Sun, Zheng Zhang, Jun Xie, Zuocheng Shi, Zihan Yang, Jie Zhang, Fei Wu, and Zeke Wang. 2023. Helios: An Efficient Out-of-core GNN Training System on Terabyte-scale Graphs with In-memory Performance. arXiv preprint arXiv:2310.00837 (2023)."},{"key":"e_1_2_2_48_1","volume-title":"Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness. arXiv:2305.10863","author":"Tan Zeyuan","year":"2023","unstructured":"Zeyuan Tan, Xiulong Yuan, Congjie He, Man-Kit Sit, Guo Li, Xiaoze Liu, Baole Ai, Kai Zeng, Peter Pietzuch, and Luo Mai. 2023. Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness. arXiv:2305.10863"},{"key":"e_1_2_2_49_1","unstructured":"Komal Teru Etienne Denis and Will Hamilton. 2020. Inductive relation prediction by subgraph reasoning. In ICML. 9448--9457."},{"key":"e_1_2_2_50_1","volume-title":"Graph clustering with graph neural networks. The Journal of Machine Learning Research","author":"Tsitsulin Anton","year":"2024","unstructured":"Anton Tsitsulin, John Palowitch, Bryan Perozzi, and Emmanuel M\u00fcller. 2024. Graph clustering with graph neural networks. The Journal of Machine Learning Research (2024)."},{"key":"e_1_2_2_51_1","unstructured":"Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR."},{"key":"e_1_2_2_52_1","volume-title":"Mariusgnn: Resource-efficient out-of-core training of graph neural networks. In Eurosys. 144--161.","author":"Waleffe Roger","year":"2023","unstructured":"Roger Waleffe, Jason Mohoney, Theodoros Rekatsinas, and Shivaram Venkataraman. 2023. Mariusgnn: Resource-efficient out-of-core training of graph neural networks. In Eurosys. 144--161."},{"key":"e_1_2_2_53_1","volume-title":"Nam Sung Kim, and Yingyan Lin","author":"Wan Cheng","year":"2022","unstructured":"Cheng Wan, Youjie Li, Ang Li, Nam Sung Kim, and Yingyan Lin. 2022. Bns-gcn: Efficient full-graph training of graph convolutional networks with partition-parallelism and random boundary node sampling. In MLSys. 673--693."},{"key":"e_1_2_2_54_1","volume-title":"Nam Sung Kim, and Yingyan Lin","author":"Wan Cheng","year":"2022","unstructured":"Cheng Wan, Youjie Li, Cameron R Wolfe, Anastasios Kyrillidis, Nam Sung Kim, and Yingyan Lin. 2022. PipeGCN: Efficient full-graph training of graph convolutional networks with pipelined feature communication. arXiv preprint arXiv:2203.10428 (2022)."},{"key":"e_1_2_2_55_1","doi-asserted-by":"crossref","unstructured":"Daixin Wang Yuan Qi Jianbin Lin Peng Cui Quanhui Jia Zhen Wang Yanming Fang Quan Yu Jun Zhou and Shuang Yang. 2019. A Semi-Supervised Graph Attentive Network for Financial Fraud Detection. In ICDM. 598--607.","DOI":"10.1109\/ICDM.2019.00070"},{"key":"e_1_2_2_56_1","doi-asserted-by":"crossref","unstructured":"Jizhe Wang Pipei Huang Huan Zhao Zhibo Zhang Binqiang Zhao and Dik Lun Lee. 2018. Billion-Scale Commodity Embedding for E-Commerce Recommendation in Alibaba. In KDD. 839--848.","DOI":"10.1145\/3219819.3219869"},{"key":"e_1_2_2_57_1","volume-title":"Proceedings of the ICLR Workshop on Representation Learning on Graphs and Manifolds.","author":"Wang Minjie","year":"2019","unstructured":"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. Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs. In Proceedings of the ICLR Workshop on Representation Learning on Graphs and Manifolds."},{"key":"e_1_2_2_58_1","doi-asserted-by":"crossref","unstructured":"Yuke Wang Boyuan Feng and Yufei Ding. 2022. QGTC: accelerating quantized graph neural networks via GPU tensor core. In PPoPP. 107--119.","DOI":"10.1145\/3503221.3508408"},{"key":"e_1_2_2_59_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 OSDI. 515--531."},{"key":"e_1_2_2_60_1","unstructured":"Yuke Wang Boyuan Feng Zheng Wang Guyue Huang and Yufei Ding. 2023. TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUs. In ATC. 149--164."},{"key":"e_1_2_2_61_1","volume-title":"Leiserson","author":"Weber Mark","year":"2019","unstructured":"Mark Weber, Giacomo Domeniconi, Jie Chen, Daniel Karl I. Weidele, Claudio Bellei, Tom Robinson, and Charles E. Leiserson. 2019. Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics. CoRR (2019)."},{"key":"e_1_2_2_62_1","volume-title":"Can Lu, and Xuemin Lin.","author":"Wei Hao","year":"2016","unstructured":"Hao Wei, Jeffrey Xu Yu, Can Lu, and Xuemin Lin. 2016. Speedup Graph Processing by Graph Ordering. In SIGMOD. 1813--1828."},{"key":"e_1_2_2_63_1","unstructured":"Shiwen Wu Fei Sun Wentao Zhang Xu Xie and Bin Cui. 2022. Graph Neural Networks in Recommender Systems: A Survey. ACM Comput. Surv. (2022)."},{"key":"e_1_2_2_64_1","volume-title":"Graphiler: Optimizing graph neural networks with message passing data flow graph. In MLSys. 515--528.","author":"Xie Zhiqiang","year":"2022","unstructured":"Zhiqiang Xie, Minjie Wang, Zihao Ye, Zheng Zhang, and Rui Fan. 2022. Graphiler: Optimizing graph neural networks with message passing data flow graph. In MLSys. 515--528."},{"key":"e_1_2_2_65_1","doi-asserted-by":"crossref","unstructured":"Jianbang Yang Dahai Tang Xiaoniu Song Lei Wang Qiang Yin Rong Chen Wenyuan Yu and Jingren Zhou. 2022. GNNLab: a factored system for sample-based GNN training over GPUs. In Eurosys. 417--434.","DOI":"10.1145\/3492321.3519557"},{"key":"e_1_2_2_66_1","doi-asserted-by":"crossref","unstructured":"Minji Yoon Th\u00e9ophile Gervet Baoxu Shi Sufeng Niu Qi He and Jaewon Yang. 2021. Performance-Adaptive Sampling Strategy Towards Fast and Accurate Graph Neural Networks. In KDD. 2046--2056.","DOI":"10.1145\/3447548.3467284"},{"key":"e_1_2_2_67_1","unstructured":"Hanqing Zeng Muhan Zhang Yinglong Xia Ajitesh Srivastava Andrey Malevich Rajgopal Kannan Viktor Prasanna Long Jin and Ren Chen. 2021. Decoupling the Depth and Scope of Graph Neural Networks. In NeurIPS."},{"key":"e_1_2_2_68_1","volume-title":"Prasanna","author":"Zeng Hanqing","year":"2019","unstructured":"Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor K. Prasanna. 2019. Accurate, Efficient and Scalable Graph Embedding. In IPDPS. 462--471."},{"key":"e_1_2_2_69_1","unstructured":"Muhan Zhang and Yixin Chen. 2018. Link Prediction Based on Graph Neural Networks. In NeurIPS. 5171--5181."},{"key":"e_1_2_2_70_1","unstructured":"Qingru Zhang David Wipf Quan Gan and Le Song. 2021. A Biased Graph Neural Network Sampler with Near-Optimal Regret. In NeurIPS. 8833--8844."},{"key":"e_1_2_2_71_1","unstructured":"Tianyi Zhang Aditya Desai Gaurav Gupta and Anshumali Shrivastava. 2024. HashOrder: Accelerating Graph Processing Through Hashing-based Reordering."},{"key":"e_1_2_2_72_1","doi-asserted-by":"publisher","DOI":"10.1109\/IA351965.2020.00011"},{"key":"e_1_2_2_73_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. 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