67 citations · 76 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…
Towards Understanding the Generalizability of Delayed Stochastic Gradient Descent
Xiaoge Deng, Li Shen, Shengwei Li +3
Stochastic gradient descent (SGD) performed in an asynchronous manner plays a crucial role in training large-scale machine learning models. However, the generalization performance…
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…
EmbRace: Accelerating Sparse Communication for Distributed Training of NLP Neural Networks
Shengwei Li, Zhiquan Lai, Dongsheng Li +3
Distributed data-parallel training has been widely adopted for deep neural network (DNN) models. Although current deep learning (DL) frameworks scale well for dense models like ima…