9 citations · 10 across the 6 of their papers we have counts for
6 papers · 1 filter
DCP: Addressing Input Dynamism In Long-Context Training via Dynamic Context Parallelism
Chenyu Jiang, Zhenkun Cai, Ye Tian +3
Context parallelism has emerged as a key technique to support long-context training, a growing trend in generative AI for modern large models. However, existing context parallel me…
AIBrix: Towards Scalable, Cost-Effective Large Language Model Inference Infrastructure
The AIBrix Team, Jiaxin Shan, Varun Gupta +24
We introduce AIBrix, a cloud-native, open-source framework designed to optimize and simplify large-scale LLM deployment in cloud environments. Unlike traditional cloud-native stack…
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…
DynaPipe: Optimizing Multi-task Training through Dynamic Pipelines
Chenyu Jiang, Zhen Jia, Shuai Zheng +2
Multi-task model training has been adopted to enable a single deep neural network model (often a large language model) to handle multiple tasks (e.g., question answering and text s…
dPRO: A Generic Profiling and Optimization System for Expediting Distributed DNN Training
Hanpeng Hu, Chenyu Jiang, Yuchen Zhong +5
Distributed training using multiple devices (e.g., GPUs) has been widely adopted for learning DNN models over large datasets. However, the performance of large-scale distributed tr…
Pyramid: A General Framework for Distributed Similarity Search
Shiyuan Deng, Xiao Yan, Kelvin K. W. Ng +2
Similarity search is a core component in various applications such as image matching, product recommendation and low-shot classification. However, single machine solutions are usua…