13 citations · 16 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 2 cited
CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Framework
Yiyang Zhao, Yunzhuo Liu, Bo Jiang +1
This work presents a novel approach to neural architecture search (NAS) that aims to increase carbon efficiency for the model design process. The proposed framework CE-NAS addresse…
cs.DC2023★ 1 cited
Isolated Scheduling for Distributed Training Tasks in GPU Clusters
Xinchi Han, Weihao Jiang, Peirui Cao +5
Distributed machine learning (DML) technology makes it possible to train large neural networks in a reasonable amount of time. Meanwhile, as the computing power grows much faster t…
cs.DC2022★ 13 cited
FuncPipe: A Pipelined Serverless Framework for Fast and Cost-efficient Training of Deep Learning Models
Yunzhuo Liu, Bo Jiang, Tian Guo +4
Training deep learning (DL) models in the cloud has become a norm. With the emergence of serverless computing and its benefits of true pay-as-you-go pricing and scalability, system…