HAMS: High Availability for Distributed Machine Learning Service Graphs. - Shixiong Zhao; Xusheng Chen; Cheng Wang 021; Fanxin Li; Qi Ji; Heming Cui; Cheng Li 001; Sen Wang 004 (DSN, 2020)

NASPipe: high performance and reproducible pipeline parallel supernet training via causal synchronous parallelism. - Shixiong Zhao; Fanxin Li; Xusheng Chen; Tianxiang Shen; Li Chen 008; Sen Wang 004; Nicholas Zhang; Cheng Li 001; Heming Cui (ASPLOS, 2022)

vPipe: A Virtualized Acceleration System for Achieving Efficient and Scalable Pipeline Parallel DNN Training. - Shixiong Zhao; Fanxin Li; Xusheng Chen; Xiuxian Guan; Jianyu Jiang; Dong Huang 005; Yuhao Qing; Sen Wang 004; Peng Wang 037; Gong Zhang 001; Cheng Li 001; Ping Luo 002; et al. (IEEE Trans. Parallel Distributed Syst., 2022)

Fold3D: Rethinking and Parallelizing Computational and Communicational Tasks in the Training of Large DNN Models. - Fanxin Li; Shixiong Zhao; Yuhao Qing; Xusheng Chen; Xiuxian Guan; Sen Wang 004; Gong Zhang 001; Heming Cui (IEEE Trans. Parallel Distributed Syst., 2023)

FoldMoE: Efficient Long Sequence MoE Training via Attention-MoE Pipelining. - Guichao Zhu; Lintian Lei; Yuhao Qing; Yichao Fu; Fanxin Li; Dong Huang 005; Zekai Sun; Heming Cui (ACL (1), 2025)

Hecate: Unlocking Efficient Sparse Model Training via Fully Sharded Sparse Data Parallelism. - Yuhao Qing; Guichao Zhu; Fanxin Li; Lintian Lei; Zekai Sun; Xiuxian Guan; Shixiong Zhao; Xusheng Chen; Dong Huang 005; Sen Wang 004; Heming Cui (CoRR, 2025)

PipeMesh: Achieving Memory-Efficient Computation-Communication Overlap for Training Large Language Models. - Fanxin Li; Shixiong Zhao; Yuhao Qing; Jianyu Jiang; Xusheng Chen; Heming Cui (IEEE Trans. Parallel Distributed Syst., 2025)