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The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs With Hybrid Parallelism., , , , , , , , and . IEEE Trans. Parallel Distributed Syst., 32 (7): 1641-1652 (2021)Large-scale training of deep neural networks. University of Illinois Urbana-Champaign, USA, (2019)Cached Operator Reordering: A Unified View for Fast GNN Training., , , , , , and . CoRR, (2023)Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics., , , , , , , and . PLDI, page 752-768. ACM, (2018)Neural Parameter Allocation Search., , , , and . ICLR, OpenReview.net, (2022)Towards Scalable Parallel Training of Deep Neural Networks., , , and . MLHPC@SC, page 5:1-5:9. ACM, (2017)Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism., , , , , and . IPDPS, page 210-220. IEEE, (2019)STen: Productive and Efficient Sparsity in PyTorch., , , , and . CoRR, (2023)A Data-Centric Optimization Framework for Machine Learning., , , , , and . CoRR, (2021)A data-centric optimization framework for machine learning., , , , , and . ICS, page 36:1-36:13. ACM, (2022)