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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)DL2: A Deep Learning-Driven Scheduler for Deep Learning Clusters., , , , , and . IEEE Trans. Parallel Distributed Syst., 32 (8): 1947-1960 (2021)Optimus: Accelerating Large-Scale Multi-Modal LLM Training by Bubble Exploitation., , , , , and . CoRR, (2024)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)SAPipe: Staleness-Aware Pipeline for Data Parallel DNN Training., , , , , , , and . NeurIPS, (2022)MegaScale: Scaling Large Language Model Training to More Than 10, 000 GPUs., , , , , , , , , and 22 other author(s). NSDI, page 745-760. USENIX Association, (2024)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)