5 citations · 7 across the 2 of their papers we have counts for
4 papers
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…
Prediction of GPU Failures Under Deep Learning Workloads
Heting Liu, Zhichao Li, Cheng Tan +4
Graphics processing units (GPUs) are the de facto standard for processing deep learning (DL) tasks. Meanwhile, GPU failures, which are inevitable, cause severe consequences in DL t…
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…
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…