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Optimus: an efficient dynamic resource scheduler for deep learning clusters.

, , , , and . EuroSys, page 3:1-3:14. ACM, (2018)

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BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing., , , , , , , , , and . CoRR, (2021)Dynamic Scaling of Virtualized, Distributed Service Chains: A Case Study of IMS., , , , and . CoRR, (2017)Optimus: an efficient dynamic resource scheduler for deep learning clusters., , , , and . EuroSys, page 3:1-3:14. ACM, (2018)Elastic parameter server load distribution in deep learning clusters., , , , , and . SoCC, page 507-521. ACM, (2020)A generic communication scheduler for distributed DNN training acceleration., , , , , , , and . SOSP, page 16-29. ACM, (2019)Preemptive All-reduce Scheduling for Expediting Distributed DNN Training., , , and . INFOCOM, page 626-635. IEEE, (2020)DL2: A Deep Learning-driven Scheduler for Deep Learning Clusters., , , , , and . CoRR, (2019)SP-GNN: Learning structure and position information from graphs., , , , , , and . Neural Networks, (April 2023)BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing., , , , , , , , , and . NSDI, page 103-118. USENIX Association, (2023)Multi-resource interleaving for deep learning training., , , , , and . SIGCOMM, page 428-440. ACM, (2022)