Efficient parallel implementation of a density peaks clustering algorithm on graphics processing unit. - Ke-shi Ge; Huayou Su; Dong-sheng Li 001; Xicheng Lu (Frontiers Inf. Technol. Electron. Eng., 2017)

An Efficient ADMM-Based Algorithm to Nonconvex Penalized Support Vector Machines. - Lei Guan; Linbo Qiao; Dongsheng Li 001; Tao Sun 005; Keshi Ge; Xicheng Lu (ICDM Workshops, 2018)

Deep Discriminative Clustering Network. - Xuying Shaol; Ke-shi Ge; Huayou Su; Lei Luo 002; Baoyun Peng; Dongsheng Li 001 (IJCNN, 2018)

HPDL: Towards a General Framework for High-performance Distributed Deep Learning. - Dongsheng Li 001; Zhiquan Lai; Keshi Ge; Yiming Zhang 003; Zhaoning Zhang 001; Qinglin Wang; Huaimin Wang (ICDCS, 2019)

Tag Pollution Detection in Web Videos via Cross-Modal Relevance Estimation. - Yixin Chen 004; Xinye Lin; Keshi Ge; Wenbo He; Dongsheng Li 001 (IWQoS, 2020)

An efficient parallel and distributed solution to nonconvex penalized linear SVMs. - Lei Guan; Tao Sun 005; Linbo Qiao; Zhi-hui Yang; Dongsheng Li 001; Ke-shi Ge; Xicheng Lu (Frontiers Inf. Technol. Electron. Eng., 2020)

CASQ: Accelerate Distributed Deep Learning with Sketch-Based Gradient Quantization. - Keshi Ge; Yiming Zhang 003; Yongquan Fu; Zhiquan Lai; Xiaoge Deng; Dongsheng Li 001 (CLUSTER, 2021)

S2 Reducer: High-Performance Sparse Communication to Accelerate Distributed Deep Learning. - Keshi Ge; Yongquan Fu; Zhiquan Lai; Xiaoge Deng; Dongsheng Li 001 (CoRR, 2021)

HPH: Hybrid Parallelism on Heterogeneous Clusters for Accelerating Large-scale DNNs Training. - Yabo Duan; Zhiquan Lai; Shengwei Li; Weijie Liu; Keshi Ge; Peng Liang 017; Dongsheng Li 001 (CLUSTER, 2022)

AutoPipe: A Fast Pipeline Parallelism Approach with Balanced Partitioning and Micro-batch Slicing. - Weijie Liu; Zhiquan Lai; Shengwei Li; Yabo Duan; Keshi Ge; Dongsheng Li 001 (CLUSTER, 2022)

S2 Reducer: High-Performance Sparse Communication to Accelerate Distributed Deep Learning. - Keshi Ge; Yongquan Fu; Yiming Zhang 003; Zhiquan Lai; Xiaoge Deng; Dongsheng Li 001 (ICASSP, 2022)

BRGraph: An efficient graph neural network training system by reusing batch data on GPU. - Keshi Ge; Zhejiang Ran; Zhiquan Lai; Lizhi Zhang; Dongsheng Li 001 (Concurr. Comput. Pract. Exp., 2022)

Prophet: Fine-grained Load Balancing for Parallel Training of Large-scale MoE Models. - Wei Wang; Zhiquan Lai; Shengwei Li; Weijie Liu; Keshi Ge; Yujie Liu; Ao Shen; Dongsheng Li 001 (CLUSTER, 2023)

Auto-Divide GNN: Accelerating GNN Training with Subgraph Division. - Hongyu Chen; Zhejiang Ran; Keshi Ge; Zhiquan Lai; Jingfei Jiang; Dongsheng Li 001 (Euro-Par, 2023)

Automated Tensor Model Parallelism with Overlapped Communication for Efficient Foundation Model Training. - Shengwei Li; Zhiquan Lai; Yanqi Hao; Weijie Liu; Keshi Ge; Xiaoge Deng; Dongsheng Li 001; Kai Lu 001 (CoRR, 2023)

Compressed Collective Sparse-Sketch for Distributed Data-Parallel Training of Deep Learning Models. - Keshi Ge; Kai Lu 001; Yongquan Fu; Xiaoge Deng; Zhiquan Lai; Dongsheng Li 001 (IEEE J. Sel. Areas Commun., 2023)

Merak: An Efficient Distributed DNN Training Framework With Automated 3D Parallelism for Giant Foundation Models. - Zhiquan Lai; Shengwei Li; Xudong Tang; Keshi Ge; Weijie Liu; Yabo Duan; Linbo Qiao; Dongsheng Li 001 (IEEE Trans. Parallel Distributed Syst., 2023)

HSDP: Accelerating Large-scale Model Training via Efficient Sharded Data Parallelism. - Yanqi Hao; Zhiquan Lai; Wei Wang; Shengwei Li; Weijie Liu; Keshi Ge; Dongsheng Li 001 (ISPA, 2024)

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models. - Wei Wang; Zhiquan Lai; Shengwei Li; Weijie Liu; Keshi Ge; Ao Shen; Huayou Su; Dong Sheng Li 001 (CoRR, 2024)

Advances of Pipeline Model Parallelism for Deep Learning Training: An Overview. - Lei Guan; Dong-Sheng Li; Jiye Liang; Wen-Jian Wang; Ke-shi Ge; Xicheng Lu (J. Comput. Sci. Technol., 2024)

A Multidimensional Communication Scheduling Method for Hybrid Parallel DNN Training. - Shengwei Li; Kai Lu 001; Zhiquan Lai; Weijie Liu; Keshi Ge; Dong Sheng Li 001 (IEEE Trans. Parallel Distributed Syst., 2024)

AutoPipe-H: A Heterogeneity-Aware Data-Paralleled Pipeline Approach on Commodity GPU Servers. - Weijie Liu; Kai Lu 001; Zhiquan Lai; Shengwei Li; Keshi Ge; Dongsheng Li 001; Xicheng Lu (IEEE Trans. Computers, 2025)

Oases: Efficient Large-Scale Model Training on Commodity Servers via Overlapped and Automated Tensor Model Parallelism. - Shengwei Li; Zhiquan Lai; Dongsheng Li 001; Yanqi Hao; Weijie Liu; Keshi Ge; Xiaoge Deng; Kai Lu 001 (IEEE Trans. Parallel Distributed Syst., 2025)

Efficient deep neural network training via decreasing precision with layer capacity. - Ao Shen; Zhiquan Lai; Tao Sun 005; Shengwei Li; Keshi Ge; Weijie Liu; Dongsheng Li 001 (Frontiers Comput. Sci., 2025)