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FlexPS: Flexible Parallelism Control in Parameter Server Architecture.

, , , , , , , , and . Proc. VLDB Endow., 11 (5): 566-579 (2018)

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TensorOpt: Exploring the Tradeoffs in Distributed DNN Training With Auto-Parallelism., , , , , , , and . IEEE Trans. Parallel Distributed Syst., 33 (8): 1967-1981 (2022)DSP: Efficient GNN Training with Multiple GPUs., , , , , , , and . PPoPP, page 392-404. ACM, (2023)FlexPS: Flexible Parallelism Control in Parameter Server Architecture., , , , , , , , and . Proc. VLDB Endow., 11 (5): 566-579 (2018)DGI: An Easy and Efficient Framework for GNN Model Evaluation., , , , , , , and . KDD, page 5439-5450. ACM, (2023)Improving resource utilization by timely fine-grained scheduling., , , , , and . EuroSys, page 20:1-20:16. ACM, (2020)Elastic Deep Learning in Multi-Tenant GPU Clusters., , , , , , , , and . IEEE Trans. Parallel Distributed Syst., 33 (1): 144-158 (2022)gSampler: General and Efficient GPU-based Graph Sampling for Graph Learning., , , , , , , and . SOSP, page 562-578. ACM, (2023)Seastar: vertex-centric programming for graph neural networks., , , , , , , and . EuroSys, page 359-375. ACM, (2021)MuseGNN: Interpretable and Convergent Graph Neural Network Layers at Scale., , , , , and . CoRR, (2023)TensorOpt: Exploring the Tradeoffs in Distributed DNN Training with Auto-Parallelism., , , , , , , and . CoRR, (2020)