Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision. (arXiv:2205.11913v1 [cs.DC])

Deep learning (DL) shows its prosperity in a wide variety of fields. The
development of a DL model is a time-consuming and resource-intensive procedure.
Hence, dedicated GPU accelerators have been collectively constructed into a GPU
datacenter. An efficient scheduler design for such GPU datacenter is crucially
important to reduce the operational cost and improve resource utilization.
However, traditional approaches designed for big data or high performance
computing workloads can not support DL workloads to fully utilize the GPU
resources. Recently, …

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