33 citations · 39 across the 3 of their papers we have counts for
3 papers
cs.DC2023★ 33 cited
Hanayo: Harnessing Wave-like Pipeline Parallelism for Enhanced Large Model Training Efficiency
Ziming Liu, Shenggan Cheng, Haotian Zhou +1
Large-scale language models have become increasingly challenging and expensive to train. Among various methods addressing this issue, Pipeline Parallelism has been widely employed…
cs.LG2023★ 5 cited
GenPhys: From Physical Processes to Generative Models
Ziming Liu, Di Luo, Yilun Xu +2
Since diffusion models (DM) and the more recent Poisson flow generative models (PFGM) are inspired by physical processes, it is reasonable to ask: Can physical processes offer addi…
cs.DC2023★ 1 cited
ATP: Adaptive Tensor Parallelism for Foundation Models
Shenggan Cheng, Ziming Liu, Jiangsu Du +1
Foundation models have impressive performance and generalization capabilities across a wide range of applications. The increasing size of the models introduces great challenges for…