3 citations · 15 across the 7 of their papers we have counts for
7 papers
Energy-Efficient GPU Clusters Scheduling for Deep Learning
Diandian Gu, Xintong Xie, Gang Huang +2
Training deep neural networks (DNNs) is a major workload in datacenters today, resulting in a tremendously fast growth of energy consumption. It is important to reduce the energy c…
A Soft Coordination Method of Heterogeneous Devices in Distribution System Voltage Control
Licheng Wang, Tao Wang, Gang Huang +4
With the continuous increase of photovoltaic (PV) penetration, the voltage control interactions between newly installed PV inverters and previously deployed on-load tap-changer (OL…
Personalized Federated Learning on Long-Tailed Data via Adversarial Feature Augmentation
Yang Lu, Pinxin Qian, Gang Huang +1
Personalized Federated Learning (PFL) aims to learn personalized models for each client based on the knowledge across all clients in a privacy-preserving manner. Existing PFL metho…
MuxFlow: Efficient and Safe GPU Sharing in Large-Scale Production Deep Learning Clusters
Yihao Zhao, Xin Liu, Shufan Liu +5
Large-scale GPU clusters are widely-used to speed up both latency-critical (online) and best-effort (offline) deep learning (DL) workloads. However, most DL clusters either dedicat…
Federated Semi-Supervised Learning with Annotation Heterogeneity
Xinyi Shang, Gang Huang, Yang Lu +4
Federated Semi-Supervised Learning (FSSL) aims to learn a global model from different clients in an environment with both labeled and unlabeled data. Most of the existing FSSL work…
Characterizing and Detecting WebAssembly Runtime Bugs
Yixuan Zhang, Shangtong Cao, Haoyu Wang +6
WebAssembly (abbreviated WASM) has emerged as a promising language of the Web and also been used for a wide spectrum of software applications such as mobile applications and deskto…