9 citations · 11 across the 11 of their papers we have counts for
4 papers · 1 filter
Marconi: Prefix Caching for the Era of Hybrid LLMs
Rui Pan, Zhuang Wang, Zhen Jia +5
Hybrid models that combine the language modeling capabilities of Attention layers with the efficiency of Recurrent layers (e.g., State Space Models) have gained traction in practic…
PipeFill: Using GPUs During Bubbles in Pipeline-parallel LLM Training
Daiyaan Arfeen, Zhen Zhang, Xinwei Fu +2
Training Deep Neural Networks (DNNs) with billions of parameters generally involves pipeline-parallel (PP) execution. Unfortunately, PP model training can use GPUs inefficiently, e…
DiffusionPipe: Training Large Diffusion Models with Efficient Pipelines
Ye Tian, Zhen Jia, Ziyue Luo +2
Diffusion models have emerged as dominant performers for image generation. To support training large diffusion models, this paper studies pipeline parallel training of diffusion mo…
Lancet: Accelerating Mixture-of-Experts Training via Whole Graph Computation-Communication Overlapping
Chenyu Jiang, Ye Tian, Zhen Jia +3
The Mixture-of-Expert (MoE) technique plays a crucial role in expanding the size of DNN model parameters. However, it faces the challenge of extended all-to-all communication laten…