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DSP: Efficient GNN Training with Multiple GPUs., , , , , , , и . PPoPP, стр. 392-404. ACM, (2023)4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs., , , , , , , , , и 10 other автор(ы). CoRR, (2024)4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on RDBs., , , , , , , , , и 10 other автор(ы). VLDB Workshops, VLDB.org, (2024)Improving resource utilization by timely fine-grained scheduling., , , , , и . EuroSys, стр. 20:1-20:16. ACM, (2020)DGI: An Easy and Efficient Framework for GNN Model Evaluation., , , , , , , и . KDD, стр. 5439-5450. ACM, (2023)PPS: Fair and efficient black-box scheduling for multi-tenant GPU clusters., , , , , , , , и . Parallel Comput., (2024)FlexPS: Flexible Parallelism Control in Parameter Server Architecture., , , , , , , , и . Proc. VLDB Endow., 11 (5): 566-579 (2018)Scalable De Novo Genome Assembly Using Pregel., , , , и . ICDE, стр. 1216-1219. IEEE Computer Society, (2018)DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs., , , и . CoRR, (2024)FEC: Efficient Deep Recommendation Model Training with Flexible Embedding Communication., , , , , и . Proc. ACM Manag. Data, 1 (2): 165:1-165:21 (2023)