Mining of Attack Models in IDS Alerts from Network Backbone by a Two-stage Clustering Method. - Lin-Bo Qiao; Bo-Feng Zhang; Zhiquan Lai; Jinshu Su (IPDPS Workshops, 2012)

Rhymes: A shared virtual memory system for non-coherent tiled many-core architectures. - King Tin Lam; Jinghao Shi; Dominic Hung; Cho-Li Wang; Zhiquan Lai; Wangbin Zhu; Youliang Yan (ICPADS, 2014)

Efficient DVFS to Prevent Hard Faults for Many-Core Architectures. - Zhiquan Lai; Baokang Zhao; Jinshu Su (ICT-EurAsia, 2014)

A Power Modelling Approach for Many-Core Architectures. - Zhiquan Lai; King Tin Lam; Cho-Li Wang; Jinshu Su (SKG, 2014)

Latency-aware DVFS for efficient power state transitions on many-core architectures. - Zhiquan Lai; King Tin Lam; Cho-Li Wang; Jinshu Su (J. Supercomput., 2015)

A Two-Tiered Defence of Techniques to Prevent SQL Injection Attacks. - Yan Zhu; Guidong Zhang; Zhiquan Lai; Boya Niu; Yongjun Shen (IMIS, 2017)

PoweRock: Power Modeling and Flexible Dynamic Power Management for Many-Core Architectures. - Zhiquan Lai; King Tin Lam; Cho-Li Wang; Jinshu Su (IEEE Syst. J., 2017)

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)

Poster Abstract: Model Average-based Distributed Training for Sparse Deep Neural Networks. - Yuetong Yang; Zhiquan Lai; Lei Cai; Dongsheng Li 001 (INFOCOM Workshops, 2020)

ADMMiRNN: Training RNN with Stable Convergence via an Efficient ADMM Approach. - Yu Tang; Zhigang Kan; Dequan Sun; Linbo Qiao; Jingjing Xiao; Zhiquan Lai; Dongsheng Li 001 (ECML/PKDD (2), 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)

2PGraph: Accelerating GNN Training over Large Graphs on GPU Clusters. - Lizhi Zhang; Zhiquan Lai; Shengwei Li; Yu Tang; Feng Liu; Dongsheng Li 001 (CLUSTER, 2021)

Prediction of the Cyanobacteria Coverage in Time-series Images based on Convolutional Neural Network. - Xiangyu Ye; Zhiquan Lai; Dongsheng Li 001 (ICCCV, 2021)

HMA: An Efficient Training Method for NLP Models. - Yuetong Yang; Zhiquan Lai; Lei Cai; Dongsheng Li 001 (ICIAI, 2021)

Hippie: A Data-Paralleled Pipeline Approach to Improve Memory-Efficiency and Scalability for Large DNN Training. - Xiangyu Ye; Zhiquan Lai; Shengwei Li; Lei Cai; Ding Sun; Linbo Qiao; Dongsheng Li 001 (ICPP, 2021)

Accelerate Graph Neural Network Training by Reusing Batch Data on GPUs. - Zhejiang Ran; Zhiquan Lai; Lizhi Zhang; Dongsheng Li 001 (IPCCC, 2021)

PCGraph: Accelerating GNN Inference on Large Graphs via Partition Caching. - Lizhi Zhang; Zhiquan Lai; Yu Tang; Dongsheng Li 001; Feng Liu; Xiaochun Luo (ISPA/BDCloud/SocialCom/SustainCom, 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)

EmbRace: Accelerating Sparse Communication for Distributed Training of NLP Neural Networks. - Shengwei Li; Zhiquan Lai; Dongsheng Li 001; Xiangyu Ye; Yabo Duan (CoRR, 2021)

Coordinative Scheduling of Computation and Communication in Data-Parallel Systems. - Dongsheng Li 001; Zhiyao Hu; Zhiquan Lai; Yiming Zhang 003; Kai Lu 001 (IEEE Trans. Computers, 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)

EmbRace: Accelerating Sparse Communication for Distributed Training of Deep Neural Networks. - Shengwei Li; Zhiquan Lai; Dongsheng Li 001; Yiming Zhang 003; Xiangyu Ye; Yabo Duan (ICPP, 2022)

SCGraph: Accelerating Sample-based GNN Training by Staged Caching of Features on GPUs. - Yuqi He; Zhiquan Lai; Zhejiang Ran; Lizhi Zhang; Dongsheng Li 001 (ISPA/BDCloud/SocialCom/SustainCom, 2022)

Accelerating Sample-based GNN Training by Feature Caching on GPUs. - Yuqi He; Zhiquan Lai; Zhejiang Ran; Lizhi Zhang; Dongsheng Li 001 (SmartCloud, 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)

DELTA: Dynamically Optimizing GPU Memory beyond Tensor Recomputation. - Yu Tang; Chenyu Wang; Yufan Zhang; Yuliang Liu; Xingcheng Zhang; Linbo Qiao; Zhiquan Lai; Dongsheng Li 001 (CoRR, 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)

Communication Analysis for Multidimensional Parallel Training of Large-scale DNN Models. - Zhiquan Lai; Yanqi Hao; Shengwei Li; Dongsheng Li 001 (HPCC/DSS/SmartCity/DependSys, 2023)

Rethinking the Distributed DNN Training Cluster Design from the Cost-effectiveness View. - Zhiquan Lai; Yujie Liu; Wei Wang; Yanqi Hao; Dongsheng Li 001 (HPCC/DSS/SmartCity/DependSys, 2023)

Efficient Large Models Fine-tuning on Commodity Servers via Memory-balanced Pipeline Parallelism. - Yujie Liu; Zhiquan Lai; Weijie Liu; Wei Wang; Dongsheng Li 001 (HPCC/DSS/SmartCity/DependSys, 2023)

CD-Sched: An Automated Scheduling Framework for Accelerating Neural Network Training on Shared Memory CPU-DSP Platforms. - Yuanyuan Xiao; Zhiquan Lai; Dongsheng Li 001 (PCCNT, 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)

Hierarchical Adaptive Pooling by Capturing High-Order Dependency for Graph Representation Learning. - Ning Liu 015; Songlei Jian; Dongsheng Li 001; Yiming Zhang 003; Zhiquan Lai; Hongzuo Xu (IEEE Trans. Knowl. Data Eng., 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)

A Survey on Auto-Parallelism of Large-Scale Deep Learning Training. - Peng Liang 017; Yu Tang; Xiaoda Zhang; Youhui Bai; Teng Su; Zhiquan Lai; Linbo Qiao; Dongsheng Li 001 (IEEE Trans. Parallel Distributed Syst., 2023)

Accelerating GNN Training by Adapting Large Graphs to Distributed Heterogeneous Architectures. - Lizhi Zhang; Kai Lu 001; Zhiquan Lai; Yongquan Fu; Yu Tang; Dongsheng Li 001 (IEEE Trans. Computers, 2023)

Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning (Extended Abstract). - Ning Liu 015; Songlei Jian; Dongsheng Li 001; Yiming Zhang 003; Zhiquan Lai; Hongzuo Xu (ICDE, 2024)

The Self-adaptive and Topology-aware MPI_Bcast leveraging Collective offload on Tianhe Express Interconnect. - Chongshan Liang; Yi Dai; Jun Xia; Jinbo Xu; Jintao Peng; Weixia Xu; Ming Xie; Jie Liu; Zhiquan Lai; Sheng Ma; Qi Zhu (IPDPS, 2024)

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)

Accurate and Efficient Fine-Tuning of Quantized Large Language Models Through Optimal Balance. - Ao Shen; Qiang Wang 006; Zhiquan Lai; Xionglve Li; Dong-sheng Li 001 (CoRR, 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)

A Memory-Efficient Hybrid Parallel Framework for Deep Neural Network Training. - Dongsheng Li 001; Shengwei Li; Zhiquan Lai; Yongquan Fu; Xiangyu Ye; Lei Cai; Linbo Qiao (IEEE Trans. Parallel Distributed Syst., 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)

FMCC-RT: a scalable and fine-grained all-reduce algorithm for large-scale SMP clusters. - Jintao Peng; Jie Liu 002; Jianbin Fang; Min Xie; Yi Dai; Zhiquan Lai; Bo Yang 023; Chunye Gong; Xinjun Mao; Guo Mao; Jie Ren 007 (Sci. China Inf. Sci., 2025)

Accurate and Efficient Fine-Tuning of Quantized Large Language Models Through Optimal Balance in Adaptation. - Ao Shen; Zhiquan Lai; Qiang Wang 006; Xionglve Li; Lizhi Zhang; Dongsheng Li 001; Jiaxin Li (Trans. Assoc. Comput. Linguistics, 2025)

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)