5 citations · 5 across the 1 of their papers we have counts for
3 papers
cs.DC2022
dPRO: A Generic Profiling and Optimization System for Expediting Distributed DNN Training
Hanpeng Hu, Chenyu Jiang, Yuchen Zhong +5
Distributed training using multiple devices (e.g., GPUs) has been widely adopted for learning DNN models over large datasets. However, the performance of large-scale distributed tr…
cs.DC2021
Serving DNN Models with Multi-Instance GPUs: A Case of the Reconfigurable Machine Scheduling Problem
Cheng Tan, Zhichao Li, Jian Zhang +5
Multi-Instance GPU (MIG) is a new feature introduced by NVIDIA A100 GPUs that partitions one physical GPU into multiple GPU instances. With MIG, A100 can be the most cost-efficient…
cs.LG2021★ 5 cited
AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly
Yuchen Jin, Tianyi Zhou, Liangyu Zhao +4
The learning rate (LR) schedule is one of the most important hyper-parameters needing careful tuning in training DNNs. However, it is also one of the least automated parts of machi…