Alpa is developed as a research project with collaborators from multiple institutions. This page includes references to publications describing the ideas behind Alpa.
Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning
Lianmin Zheng*, Zhuohan Li*, Hao Zhang*, Yonghao Zhuang, Zhifeng Chen, Yanping Huang, Yida Wang, Yuanzhong Xu, Danyang Zhuo, Eric P. Xing, Joseph E. Gonzalez, Ion Stoica
OSDI 2022
On Optimizing the Communication of Model Parallelism
Yonghao Zhuang*, Hexu Zhao*, Lianmin Zheng, Zhuohan Li, Eric P. Xing, Qirong Ho, Joseph E. Gonzalez, Ion Stoica, Hao Zhang
MLSys 2023
AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving
Zhuohan Li*, Lianmin Zheng*, Yinmin Zhong*, Vincent Liu, Ying Sheng, Xin Jin, Yanping Huang, Zhifeng Chen, Hao Zhang, Joseph E. Gonzalez, Ion Stoica
OSDI 2023
Lianmin Zheng*, Zhuohan Li*, Hao Zhang*, Yonghao Zhuang, Zhifeng Chen, Yanping Huang, Yida Wang, Yuanzhong Xu, Danyang Zhuo, Eric P. Xing, Joseph E. Gonzalez, Ion Stoica
OSDI 2022
On Optimizing the Communication of Model Parallelism
Yonghao Zhuang*, Hexu Zhao*, Lianmin Zheng, Zhuohan Li, Eric P. Xing, Qirong Ho, Joseph E. Gonzalez, Ion Stoica, Hao Zhang
MLSys 2023
AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving
Zhuohan Li*, Lianmin Zheng*, Yinmin Zhong*, Vincent Liu, Ying Sheng, Xin Jin, Yanping Huang, Zhifeng Chen, Hao Zhang, Joseph E. Gonzalez, Ion Stoica
OSDI 2023