54 citations · 56 across the 2 of their papers we have counts for
3 papers
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.LG2022★ 2 cited
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…
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…