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Exploiting CXL-based Memory for Distributed Deep Learning., , , и . ICPP, стр. 19:1-19:11. ACM, (2022)Optimizing the Training of Co-Located Deep Learning Models Using Cache-Aware Staggering., , и . HiPC, стр. 246-255. IEEE, (2023)Collaboration and transportation resource sharing in multiple centers vehicle routing optimization with delivery and pickup., , , , , и . Knowl. Based Syst., (2018)Asynchronous Multi-Level Checkpointing: An Enabler of Reproducibility using Checkpoint History Analytics., , , и . SC Workshops, стр. 1748-1756. ACM, (2023)PredictDDL: Reusable Workload Performance Prediction for Distributed Deep Learning., , , и . CLUSTER, стр. 13-24. IEEE, (2023)Building the I (Interoperability) of FAIR for Performance Reproducibility of Large-Scale Composable Workflows in RECUP., , , , , , , , , и 2 other автор(ы). e-Science, стр. 1-7. IEEE, (2023)Two-echelon logistics delivery and pickup network optimization based on integrated cooperation and transportation fleet sharing., , , , , , и . Expert Syst. Appl., (2018)On Realizing Efficient Deep Learning Using Serverless Computing., , , и . CCGRID, стр. 220-229. IEEE, (2022)Canary: Fault-Tolerant FaaS for Stateful Time-Sensitive Applications., , и . SC, стр. 41:1-41:16. IEEE, (2022)