54 citations · 88 across the 5 of their papers we have counts for
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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.DC2022★ 54 cited
Aryl: An Elastic Cluster Scheduler for Deep Learning
Jiamin Li, Hong Xu, Yibo Zhu +3
Companies build separate training and inference GPU clusters for deep learning, and use separate schedulers to manage them. This leads to problems for both training and inference:…
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…