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MLaaS in the Wild: Workload Analysis and Scheduling in Large-Scale Heterogeneous GPU Clusters.

, , , , , , , , , and . NSDI, page 945-960. USENIX Association, (2022)

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MLaaS in the Wild: Workload Analysis and Scheduling in Large-Scale Heterogeneous GPU Clusters., , , , , , , , , and . NSDI, page 945-960. USENIX Association, (2022)Workload consolidation in alibaba clusters: the good, the bad, and the ugly., , , , , , , , , and 4 other author(s). SoCC, page 210-225. ACM, (2022)Metis: learning to schedule long-running applications in shared container clusters at scale., , , , and . SC, page 68. IEEE/ACM, (2020)Towards Framework-Independent, Non-Intrusive Performance Characterization for Dataflow Computation., , and . APSys, page 54-60. ACM, (2019)Semi-dynamic load balancing: efficient distributed learning in non-dedicated environments., , , , and . SoCC, page 431-446. ACM, (2020)CaraServe: CPU-Assisted and Rank-Aware LoRA Serving for Generative LLM Inference., , , , , , , , and . CoRR, (2024)InternLM2 Technical Report., , , , , , , , , and 60 other author(s). CoRR, (2024)Beware of Fragmentation: Scheduling GPU-Sharing Workloads with Fragmentation Gradient Descent., , , , , , and . USENIX ATC, page 995-1008. USENIX Association, (2023)Fast Distributed Deep Learning via Worker-adaptive Batch Sizing., , , , and . SoCC, page 521. ACM, (2018)Efficient Training of Large Language Models on Distributed Infrastructures: A Survey., , , , , , , , , and 6 other author(s). CoRR, (2024)