Synergy: Resource Sensitive DNN Scheduling in Multi-Tenant Clusters. (arXiv:2110.06073v2 [cs.DC] UPDATED)

Training Deep Neural Networks (DNNs) is a widely popular workload in both
enterprises and cloud data centers. Existing schedulers for DNN training
consider GPU as the dominant resource, and allocate other resources such as CPU
and memory proportional to the number of GPUs requested by the job.
Unfortunately, these schedulers do not consider the impact of a job's
sensitivity to allocation of CPU, memory, and storage resources. In this work,
we propose Synergy, a resource-sensitive scheduler for shared GPU clusters. …

arxiv dnn scheduling