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cs.DC2024
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…
cs.DC2023
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…