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On application-level approaches to avoiding TCP throughput collapse in cluster-based storage systems.

, , , , , , and . PDSW, page 1-4. ACM Press, (2007)

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Themis: Fair and Efficient GPU Cluster Scheduling for Machine Learning Workloads., , , , , , , , and . CoRR, (2019)Compositional programming and testing of dynamic distributed systems., , , and . Proc. ACM Program. Lang., 2 (OOPSLA): 159:1-159:30 (2018)Packrat: Automatic Reconfiguration for Latency Minimization in CPU-based DNN Serving., , , , and . CoRR, (2023)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)Efficient Algorithms for Device Placement of DNN Graph Operators., , , , and . NeurIPS, (2020)On application-level approaches to avoiding TCP throughput collapse in cluster-based storage systems., , , , , , and . PDSW, page 1-4. ACM Press, (2007)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)