67 citations · 70 across the 5 of their papers we have counts for
5 papers
Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models
Wei Wang, Zhiquan Lai, Shengwei Li +5
The size of deep learning models has been increasing to enhance model quality. The linear increase in training computation budget with model size means that training an extremely l…
Oases: Efficient Large-Scale Model Training on Commodity Servers via Overlapped and Automated Tensor Model Parallelism
Shengwei Li, Zhiquan Lai, Dongsheng Li +5
Deep learning is experiencing a rise in large-scale models. Training large-scale models is costly, prompting researchers to train large-scale models on commodity servers that more…
Merak: An Efficient Distributed DNN Training Framework with Automated 3D Parallelism for Giant Foundation Models
Zhiquan Lai, Shengwei Li, Xudong Tang +5
Foundation models are becoming the dominant deep learning technologies. Pretraining a foundation model is always time-consumed due to the large scale of both the model parameter an…
S2 Reducer: High-Performance Sparse Communication to Accelerate Distributed Deep Learning
Keshi Ge, Yongquan Fu, Zhiquan Lai +2
Distributed stochastic gradient descent (SGD) approach has been widely used in large-scale deep learning, and the gradient collective method is vital to ensure the training scalabi…
An Efficient ADMM-Based Algorithm to Nonconvex Penalized Support Vector Machines
Lei Guan, Linbo Qiao, Dongsheng Li +3
Support vector machines (SVMs) with sparsity-inducing nonconvex penalties have received considerable attentions for the characteristics of automatic classification and variable sel…