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Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning | | Aurick Qiao
; Willie Neiswanger
; Qirong Ho
; Hao Zhang
; Gregory R. Ganger
; Eric P. Xing
; | | Date: |
27 Aug 2020 | | 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.
Pollux simultaneously considers both aspects. By observing each job during
training, Pollux models how their goodput (system throughput combined with
statistical efficiency) would change by adding or removing resources.
Leveraging these models, Pollux dynamically (re-)assigns resources to maximize
cluster-wide goodput, while continually optimizing each DL job to better
utilize those resources.
In experiments with real DL training jobs and with trace-driven simulations,
Pollux reduces average job completion time by 25%-50% relative to
state-of-the-art DL schedulers, even when all jobs are submitted with ideal
resource and training configurations. Based on the observation that the
statistical efficiency of DL training can change over time, we also show that
Pollux can reduce the cost of training large models in cloud environments by
25%. | | Source: | arXiv, 2008.12260 | | Services: | Forum | Review | PDF | Favorites | |
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