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Doing more with less: training large DNN models on commodity servers for the masses.

, , , and . HotOS, page 119-127. ACM, (2021)

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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)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)Workload-Aware Hardware Accelerator Mining for Distributed Deep Learning Training., , , , and . CoRR, (2024)The Case for Unifying Data Loading in Machine Learning Clusters., , , and . HotCloud, USENIX Association, (2019)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)PLATO: Predictive Latency-Aware Total Ordering., , and . SRDS, page 175-188. IEEE Computer Society, (2006)