- Aurick Qiao, W. Neiswanger, Qirong Ho, Hao Zhang, G. Ganger, E. Xing. 2020. Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning. Abstract: Pollux improves scheduling performance in deep learning (DL) clusters by adaptively co-optimizing inter-dependent factors both at the per-job level and at the cluster-wide level. Most existing schedulers will assign each job a number of resources requested by the user, which can allow jobs to use those resources inefficiently. Some recent schedulers choose job resources for users, but do so without awareness of how DL training can be re-optimized to better utilize those resources. 
