{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T05:10:14Z","timestamp":1782450614027,"version":"3.54.5"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031697654","type":"print"},{"value":"9783031697661","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-69766-1_24","type":"book-chapter","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T19:02:05Z","timestamp":1724612525000},"page":"346-361","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["GDL-GNN: Applying GPU Dataloading of\u00a0Large Datasets for\u00a0Graph Neural Network Inference"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0750-5223","authenticated-orcid":false,"given":"Haoran","family":"Dang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9841-5962","authenticated-orcid":false,"given":"Meng","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6915-955X","authenticated-orcid":false,"given":"Mingyu","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4598-1685","authenticated-orcid":false,"given":"Xiaochun","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5219-0908","authenticated-orcid":false,"given":"Dongrui","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,26]]},"reference":[{"issue":"9","key":"24_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3477141","volume":"54","author":"S Abadal","year":"2021","unstructured":"Abadal, S., Jain, A., Guirado, R., L\u00f3pez-Alonso, J., Alarc\u00f3n, E.: Computing graph neural networks: a survey from algorithms to accelerators. ACM Comput. Surv. 54(9), 1\u201338 (2021)","journal-title":"ACM Comput. Surv."},{"issue":"10","key":"24_CR2","doi-asserted-by":"publisher","first-page":"2541","DOI":"10.1109\/TPDS.2021.3065737","volume":"32","author":"Y Bai","year":"2021","unstructured":"Bai, Y., et al.: Efficient data loader for fast sampling-based GNN training on large graphs. IEEE Trans. Parallel Distrib. Syst. 32(10), 2541\u20132556 (2021)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"24_CR3","unstructured":"DGL Team: DGL Documentation (2023). https:\/\/docs.dgl.ai\/en\/1.1.x\/"},{"key":"24_CR4","unstructured":"Duan, K., et al.: A comprehensive study on large-scale graph training: benchmarking and rethinking. Adv. Neural Inf. Process. Syst. 35 (2022)"},{"key":"24_CR5","unstructured":"Fey, M., Lenssen, J.E.: Fast graph representation learning with PyTorch geometric. In: ICLR Workshop on Representation Learning on Graphs and Manifolds (2019)"},{"key":"24_CR6","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"24_CR7","unstructured":"Hu, W., et al.: Open graph benchmark: datasets for machine learning on graphs. Adv. Neural Inf. Process. Syst. 33 (2020)"},{"key":"24_CR8","doi-asserted-by":"crossref","unstructured":"Jangda, A., Polisetty, S., Guha, A., Serafini, M.: Accelerating graph sampling for graph machine learning using GPUs. In: Proceedings of the Sixteenth European Conference on Computer Systems (2021)","DOI":"10.1145\/3447786.3456244"},{"key":"24_CR9","unstructured":"Juenger, D., Iskos, N., Wang, Y., Hemstad, J., Hundt, C., Sakharnykh, N.: Maximizing performance with massively parallel hash maps on GPUs (2023). https:\/\/developer.nvidia.com\/blog\/maximizing-performance-with-massively-parallel-hash-maps-on-gpus\/"},{"issue":"1","key":"24_CR10","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1137\/S1064827595287997","volume":"20","author":"G Karypis","year":"1998","unstructured":"Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput. 20(1), 359\u2013392 (1998)","journal-title":"SIAM J. Sci. Comput."},{"key":"24_CR11","doi-asserted-by":"crossref","unstructured":"Kim, D.H., Nagi, R., Chen, D.: Thanos: high-performance CPU-GPU based balanced graph partitioning using cross-decomposition. In: 2020 25th Asia and South Pacific Design Automation Conference (2020)","DOI":"10.1109\/ASP-DAC47756.2020.9045588"},{"issue":"12","key":"24_CR12","doi-asserted-by":"publisher","first-page":"1572","DOI":"10.1109\/JPROC.2023.3337442","volume":"111","author":"H Lin","year":"2023","unstructured":"Lin, H., et al.: A comprehensive survey on distributed training of graph neural networks. Proc. IEEE 111(12), 1572\u20131606 (2023)","journal-title":"Proc. IEEE"},{"key":"24_CR13","doi-asserted-by":"crossref","unstructured":"Lin, Z., Li, C., Miao, Y., Liu, Y., Xu, Y.: PaGraph: scaling GNN training on large graphs via computation-aware caching. In: Proceedings of the 11th ACM Symposium on Cloud Computing (2020)","DOI":"10.1145\/3419111.3421281"},{"key":"24_CR14","doi-asserted-by":"crossref","unstructured":"Liu, X., et al.: Survey on graph neural network acceleration: an algorithmic perspective. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (2022)","DOI":"10.24963\/ijcai.2022\/772"},{"key":"24_CR15","unstructured":"Lv, Z., et al.: A survey of graph pre-processing methods: from algorithmic to hardware perspectives (2023). arXiv:2309.07581"},{"key":"24_CR16","doi-asserted-by":"crossref","unstructured":"Min, S.W., et al.: Large graph convolutional network training with GPU-oriented data communication architecture. Proc. VLDB Endow. 14(11) (2021)","DOI":"10.14778\/3476249.3476264"},{"key":"24_CR17","unstructured":"PyG Team: PyG Documentation (2023). https:\/\/pytorch-geometric.readthedocs.io\/en\/2.4.0\/"},{"key":"24_CR18","doi-asserted-by":"crossref","unstructured":"Song, S., Jiang, P.: Rethinking graph data placement for graph neural network training on multiple GPUs. In: Proceedings of the 36th ACM International Conference on Supercomputing (2022)","DOI":"10.1145\/3524059.3532384"},{"key":"24_CR19","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: 6th International Conference on Learning Representations (2018)"},{"key":"24_CR20","unstructured":"Wang, M., et al.: Deep graph library: a graph-centric, highly-performant package for graph neural networks (2020). arXiv:1909.01315"},{"issue":"1","key":"24_CR21","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2021","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P.S.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 4\u201324 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, D., et al.: InferTurbo: a scalable system for boosting full-graph inference of graph neural network over huge graphs. In: 2023 IEEE 39th International Conference on Data Engineering (2023)","DOI":"10.1109\/ICDE55515.2023.00248"},{"key":"24_CR23","doi-asserted-by":"crossref","unstructured":"Zheng, D., et al.: DistDGL: distributed graph neural network training for billion-scale graphs. In: 2020 IEEE\/ACM 10th Workshop on Irregular Applications: Architectures and Algorithms (2020)","DOI":"10.1109\/IA351965.2020.00011"},{"key":"24_CR24","doi-asserted-by":"crossref","unstructured":"Zhou, H., Srivastava, A., Zeng, H., Kannan, R., Prasanna, V.: Accelerating large scale real-time GNN inference using channel pruning. Proc. VLDB Endow. 14(9) (2021)","DOI":"10.14778\/3461535.3461547"},{"key":"24_CR25","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","volume":"1","author":"J Zhou","year":"2020","unstructured":"Zhou, J., et al.: Graph neural networks: a review of methods and applications. AI Open 1, 57\u201381 (2020)","journal-title":"AI Open"},{"key":"24_CR26","unstructured":"Zhu, J., et al.: Simplifying distributed neural network training on massive graphs: randomized partitions improve model aggregation (2023). arXiv:2305.09887"}],"container-title":["Lecture Notes in Computer Science","Euro-Par 2024: Parallel Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-69766-1_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T19:10:52Z","timestamp":1724613052000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-69766-1_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031697654","9783031697661"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-69766-1_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"26 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Euro-Par","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Parallel Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Madrid","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"europar2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.euro-par.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}