{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T01:38:09Z","timestamp":1772933889165,"version":"3.50.1"},"reference-count":43,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62461146205,62322213,U25B2018"],"award-info":[{"award-number":["62461146205,62322213,U25B2018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,8]]},"DOI":"10.1109\/bigdata66926.2025.11401737","type":"proceedings-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T20:57:57Z","timestamp":1772830677000},"page":"7311-7320","source":"Crossref","is-referenced-by-count":0,"title":["Accelerating Graph Neural Network Inference in Heterogeneous Computing Environments"],"prefix":"10.1109","author":[{"given":"Yukun","family":"Cui","sequence":"first","affiliation":[{"name":"School of Information, Renmin University of China,Key Laboratory of Data Engineering and Knowledge Engineering (MOE)"}]},{"given":"Feng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information, Renmin University of China,Key Laboratory of Data Engineering and Knowledge Engineering (MOE)"}]},{"given":"Zheng","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information, Renmin University of China,Key Laboratory of Data Engineering and Knowledge Engineering (MOE)"}]},{"given":"Wei","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Information, Renmin University of China,Key Laboratory of Data Engineering and Knowledge Engineering (MOE)"}]},{"given":"Tong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information, Renmin University of China,Key Laboratory of Data Engineering and Knowledge Engineering (MOE)"}]},{"given":"Xinyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information, Renmin University of China,Key Laboratory of Data Engineering and Knowledge Engineering (MOE)"}]},{"given":"Shuang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information, Renmin University of China,Key Laboratory of Data Engineering and Knowledge Engineering (MOE)"}]},{"given":"Yahui","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Information, Renmin University of China,Key Laboratory of Data Engineering and Knowledge Engineering (MOE)"}]},{"given":"Xiaoyong","family":"Du","sequence":"additional","affiliation":[{"name":"School of Information, Renmin University of China,Key Laboratory of Data Engineering and Knowledge Engineering (MOE)"}]}],"member":"263","reference":[{"key":"ref1","volume-title":"Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness","author":"Tan","year":"2023"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3458817.3480856"},{"key":"ref3","first-page":"1195","volume-title":"A Data-Driven Approach to Dataflow-Aware Online Scheduling for Graph Neural Network Inference","author":"Puigdemont","year":"2025"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3710848.3710854"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3059108"},{"key":"ref6","doi-asserted-by":"crossref","DOI":"10.1109\/ICCVW54120.2021.00237","volume-title":"Towards efficient point cloud graph neural networks through architectural simplification","author":"Tailor","year":"2021"},{"key":"ref7","first-page":"495","article-title":"Dorylus: Affordable, scalable, and accurate GNN training with distributed CPU servers and serverless threads","volume-title":"15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21)","author":"Thorpe","year":"2021"},{"key":"ref8","first-page":"515","article-title":"GNNAdvisor: An adaptive and efficient runtime system for GNN acceleration on GPUs","volume-title":"15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21)","author":"Wang","year":"2021"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2017.98"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645383"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2022.3229422"},{"key":"ref12","doi-asserted-by":"crossref","DOI":"10.1145\/3219819.3220077","volume-title":"Deepinf: Modeling influence locality in large social networks","author":"Qiu","year":"2018"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"ref14","volume-title":"Retrosynthesis prediction with conditional graph logic network","author":"Dai","year":"2019"},{"key":"ref15","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/D17-1159","volume-title":"Encoding sentences with graph convolutional networks for semantic role labeling","author":"Marcheggiani","year":"2017"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3511982"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2023.3288755"},{"key":"ref18","author":"Kipf","year":"2017","journal-title":"Semi-supervised classification with graph convolutional networks"},{"key":"ref19","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems, ser. NIPS\u201917","author":"Hamilton"},{"key":"ref20","volume-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2018"},{"key":"ref21","volume-title":"How powerful are graph neural networks","author":"Xu","year":"2019"},{"key":"ref22","volume-title":"PyTorch: an imperative style, highperformance deep learning library","author":"Paszke","year":"2019"},{"key":"ref23","first-page":"265","article-title":"Tensorflow: a system for large-scale machine learning","volume-title":"Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation","author":"Abadi","year":"2016"},{"key":"ref24","author":"Wang","year":"2020","journal-title":"Deep graph library: A graph-centric, highly-performant package for graph neural networks"},{"key":"ref25","volume-title":"Distdgl: Distributed graph neural network training for billion-scale graphs","author":"Zheng","year":"2021"},{"key":"ref26","volume-title":"Fast graph representation learning with pytorch geometric","author":"Fey","year":"2019"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.14778\/3352063.3352127"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1137\/s1064827595287997"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1287\/opre.16.3.687"},{"key":"ref30","first-page":"3149","article-title":"LightGBM: a highly efficient gradient boosting decision tree","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems, ser. NIPS\u201917","author":"Ke","year":"2017"},{"key":"ref31","volume-title":"Snap datasets: Stanford large network dataset collection","author":"Jure","year":"2021"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2025.3559346"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2018.00019"},{"key":"ref34","doi-asserted-by":"crossref","DOI":"10.1109\/ICCV48922.2021.00373","volume-title":"Towards efficient graph convolutional networks for point cloud handling","author":"Li","year":"2021"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3711896.3736888"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-020-00636-3"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.14778\/3236187.3236203"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3526130"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/3205289.3205325"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/71.993206"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2004.1264795"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/HCW.2000.843747"},{"key":"ref43","article-title":"Swarm parallelism: training large models can be surprisingly communicationefficient","volume-title":"Proceedings of the 40th International Conference on Machine Learning, ser. ICML\u201923. JMLR.org","author":"Ryabinin","year":"2023"}],"event":{"name":"2025 IEEE International Conference on Big Data (BigData)","location":"Macau, China","start":{"date-parts":[[2025,12,8]]},"end":{"date-parts":[[2025,12,11]]}},"container-title":["2025 IEEE International Conference on Big Data (BigData)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11400704\/11400712\/11401737.pdf?arnumber=11401737","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T06:54:32Z","timestamp":1772866472000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11401737\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,8]]},"references-count":43,"URL":"https:\/\/doi.org\/10.1109\/bigdata66926.2025.11401737","relation":{},"subject":[],"published":{"date-parts":[[2025,12,8]]}}}