most citedShockwave: Fair and Efficient Cluster Scheduling for Dynamic Adaptation in Machine Learning

4 citations · 5 across the 3 of their papers we have counts for

collaborators
Showing cs.DCShow all

2 papers · 1 filter

cs.DC2023

Mirage: Towards Low-interruption Services on Batch GPU Clusters with Reinforcement Learning

Qiyang Ding, Pengfei Zheng, Shreyas Kudari +2

Accommodating long-running deep learning (DL) training and inference jobs is challenging on GPU clusters that use traditional batch schedulers, such as Slurm. Given fixed wall cloc…

cs.DC2022★ 4 cited

Shockwave: Fair and Efficient Cluster Scheduling for Dynamic Adaptation in Machine Learning

Pengfei Zheng, Rui Pan, Tarannum Khan +2

Dynamic adaptation has become an essential technique in accelerating distributed machine learning (ML) training. Recent studies have shown that dynamically adjusting model structur…