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Fast inference services for alternative deep learning structures., , and . SEC, page 329-331. ACM, (2019)Early work on modeling computational sprinting., , , , and . SoCC, page 661. ACM, (2017)Model-driven computational sprinting., , , , and . EuroSys, page 38:1-38:13. ACM, (2018)PipeTune: Pipeline Parallelism of Hyper and System Parameters Tuning for Deep Learning Clusters., , , , , and . Middleware, page 89-104. ACM, (2020)Characterizing Service Level Objectives for Cloud Services: Realities and Myths., , , , and . ICAC, page 200-206. IEEE, (2019)Rapid In-situ Profiling of Colocated Workloads., and . INFOCOM Workshops, page 528-534. IEEE, (2019)Obtaining and Managing Answer Quality for Online Data-Intensive Services., , , , , and . ACM Trans. Model. Perform. Evaluation Comput. Syst., 2 (2): 11:1-11:31 (2017)Memory Mapping and Parallelizing Random Forests for Speed and Cache Efficiency., , , , and . ICPP Workshops, page 29:1-29:5. ACM, (2021)Performance Modeling for Short-Term Cache Allocation., , , and . ICPP, page 31:1-31:11. ACM, (2022)Bolt: Fast Inference for Random Forests., , , , and . Middleware, page 94-106. ACM, (2022)