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The Case for Unifying Data Loading in Machine Learning Clusters., , , and . HotCloud, USENIX Association, (2019)PipeDream: generalized pipeline parallelism for DNN training., , , , , , , and . SOSP, page 1-15. ACM, (2019)PLATO: Predictive Latency-Aware Total Ordering., , and . SRDS, page 175-188. IEEE Computer Society, (2006)Measurement and Analysis of TCP Throughput Collapse in Cluster-based Storage Systems., , , , , , and . FAST, page 175-188. USENIX, (2008)Daydream: Accurately Estimating the Efficacy of Optimizations for DNN Training., , and . USENIX Annual Technical Conference, page 337-352. USENIX Association, (2020)Safe and effective fine-grained TCP retransmissions for datacenter communication., , , , , , , and . SIGCOMM, page 303-314. ACM, (2009)RAIL: A Case for Redundant Arrays of Inexpensive Links in Data Center Networks., , , , , , , and . NSDI, page 561-576. USENIX Association, (2017)Themis: Fair and Efficient GPU Cluster Scheduling., , , , , , and . NSDI, page 289-304. USENIX Association, (2020)Parameter Hub: High Performance Parameter Servers for Efficient Distributed Deep Neural Network Training., , , , and . CoRR, (2018)Memory-Efficient Pipeline-Parallel DNN Training., , , , and . CoRR, (2020)