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2020 – today
- 2024
[j11]Dongsheng Li
, Shengwei Li, Zhiquan Lai, Yongquan Fu, Xiangyu Ye, Lei Cai, Linbo Qiao:
A Memory-Efficient Hybrid Parallel Framework for Deep Neural Network Training. IEEE Trans. Parallel Distributed Syst. 35(4): 577-591 (2024)- [j10]Shengwei Li, Kai Lu, Zhiquan Lai, Weijie Liu, Keshi Ge, Dong Sheng Li:
A Multidimensional Communication Scheduling Method for Hybrid Parallel DNN Training. IEEE Trans. Parallel Distributed Syst. 35(8): 1415-1428 (2024) - [c29]Ning Liu, Songlei Jian, Dongsheng Li, Yiming Zhang, Zhiquan Lai, Hongzuo Xu:
Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning (Extended Abstract). ICDE 2024: 5683-5684 - [c28]Chongshan Liang, Yi Dai, Jun Xia, Jinbo Xu, Jintao Peng, Weixia Xu, Ming Xie, Jie Liu, Zhiquan Lai, Sheng Ma, Qi Zhu:
The Self-adaptive and Topology-aware MPI_Bcast leveraging Collective offload on Tianhe Express Interconnect. IPDPS 2024: 791-801 - [i8]Ao Shen, Qiang Wang, Zhiquan Lai, Xionglve Li, Dong-sheng Li:
Accurate and Efficient Fine-Tuning of Quantized Large Language Models Through Optimal Balance. CoRR abs/2407.17029 (2024) - 2023
- [j9]Keshi Ge, Kai Lu, Yongquan Fu, Xiaoge Deng, Zhiquan Lai, Dongsheng Li:
Compressed Collective Sparse-Sketch for Distributed Data-Parallel Training of Deep Learning Models. IEEE J. Sel. Areas Commun. 41(4): 941-963 (2023) - [j8]Lizhi Zhang, Kai Lu, Zhiquan Lai, Yongquan Fu, Yu Tang, Dongsheng Li:
Accelerating GNN Training by Adapting Large Graphs to Distributed Heterogeneous Architectures. IEEE Trans. Computers 72(12): 3473-3488 (2023) - [j7]Ning Liu, Songlei Jian, Dongsheng Li, Yiming Zhang, Zhiquan Lai, Hongzuo Xu:
Hierarchical Adaptive Pooling by Capturing High-Order Dependency for Graph Representation Learning. IEEE Trans. Knowl. Data Eng. 35(4): 3952-3965 (2023) - [j6]Zhiquan Lai, Shengwei Li, Xudong Tang, Keshi Ge, Weijie Liu, Yabo Duan, Linbo Qiao, Dongsheng Li:
Merak: An Efficient Distributed DNN Training Framework With Automated 3D Parallelism for Giant Foundation Models. IEEE Trans. Parallel Distributed Syst. 34(5): 1466-1478 (2023) - [j5]Peng Liang, Yu Tang, Xiaoda Zhang, Youhui Bai, Teng Su, Zhiquan Lai, Linbo Qiao, Dongsheng Li:
A Survey on Auto-Parallelism of Large-Scale Deep Learning Training. IEEE Trans. Parallel Distributed Syst. 34(8): 2377-2390 (2023) - [c27]Wei Wang, Zhiquan Lai, Shengwei Li, Weijie Liu, Keshi Ge, Yujie Liu, Ao Shen, Dongsheng Li:
Prophet: Fine-grained Load Balancing for Parallel Training of Large-scale MoE Models. CLUSTER 2023: 82-94 - [c26]Hongyu Chen, Zhejiang Ran, Keshi Ge, Zhiquan Lai, Jingfei Jiang, Dongsheng Li:
Auto-Divide GNN: Accelerating GNN Training with Subgraph Division. Euro-Par 2023: 367-382 - [c25]Yujie Liu, Zhiquan Lai, Weijie Liu, Wei Wang, Dongsheng Li:
Efficient Large Models Fine-tuning on Commodity Servers via Memory-balanced Pipeline Parallelism. HPCC/DSS/SmartCity/DependSys 2023: 726-727 - [c24]Zhiquan Lai, Yanqi Hao, Shengwei Li, Dongsheng Li:
Communication Analysis for Multidimensional Parallel Training of Large-scale DNN Models. HPCC/DSS/SmartCity/DependSys 2023: 728-729 - [c23]Zhiquan Lai, Yujie Liu, Wei Wang, Yanqi Hao, Dongsheng Li:
Rethinking the Distributed DNN Training Cluster Design from the Cost-effectiveness View. HPCC/DSS/SmartCity/DependSys 2023: 730-731 - [c22]Yuanyuan Xiao, Zhiquan Lai, Dongsheng Li:
CD-Sched: An Automated Scheduling Framework for Accelerating Neural Network Training on Shared Memory CPU-DSP Platforms. PCCNT 2023: 41:1-41:6 - [i7]Shengwei Li, Zhiquan Lai, Yanqi Hao, Weijie Liu, Keshi Ge, Xiaoge Deng, Dongsheng Li, Kai Lu:
Automated Tensor Model Parallelism with Overlapped Communication for Efficient Foundation Model Training. CoRR abs/2305.16121 (2023) - 2022
- [j4]Keshi Ge, Zhejiang Ran, Zhiquan Lai, Lizhi Zhang, Dongsheng Li:
BRGraph: An efficient graph neural network training system by reusing batch data on GPU. Concurr. Comput. Pract. Exp. 34(15) (2022) - [c21]Weijie Liu, Zhiquan Lai, Shengwei Li, Yabo Duan, Keshi Ge, Dongsheng Li:
AutoPipe: A Fast Pipeline Parallelism Approach with Balanced Partitioning and Micro-batch Slicing. CLUSTER 2022: 301-312 - [c20]Yabo Duan, Zhiquan Lai, Shengwei Li, Weijie Liu, Keshi Ge, Peng Liang, Dongsheng Li:
HPH: Hybrid Parallelism on Heterogeneous Clusters for Accelerating Large-scale DNNs Training. CLUSTER 2022: 313-323 - [c19]Keshi Ge, Yongquan Fu, Yiming Zhang, Zhiquan Lai, Xiaoge Deng, Dongsheng Li:
S2 Reducer: High-Performance Sparse Communication to Accelerate Distributed Deep Learning. ICASSP 2022: 5233-5237 - [c18]Shengwei Li, Zhiquan Lai, Dongsheng Li, Yiming Zhang, Xiangyu Ye, Yabo Duan:
EmbRace: Accelerating Sparse Communication for Distributed Training of Deep Neural Networks. ICPP 2022: 7:1-7:11 - [c17]Yuqi He, Zhiquan Lai, Zhejiang Ran, Lizhi Zhang, Dongsheng Li:
SCGraph: Accelerating Sample-based GNN Training by Staged Caching of Features on GPUs. ISPA/BDCloud/SocialCom/SustainCom 2022: 106-113 - [c16]Yuqi He, Zhiquan Lai, Zhejiang Ran, Lizhi Zhang, Dongsheng Li:
Accelerating Sample-based GNN Training by Feature Caching on GPUs. SmartCloud 2022: 163-164 - [i6]Yu Tang, Chenyu Wang, Yufan Zhang, Yuliang Liu, Xingcheng Zhang, Linbo Qiao, Zhiquan Lai, Dongsheng Li:
DELTA: Dynamically Optimizing GPU Memory beyond Tensor Recomputation. CoRR abs/2203.15980 (2022) - [i5]Zhiquan Lai, Shengwei Li, Xudong Tang, Keshi Ge, Weijie Liu, Yabo Duan, Linbo Qiao, Dongsheng Li:
Merak: An Efficient Distributed DNN Training Framework with Automated 3D Parallelism for Giant Foundation Models. CoRR abs/2206.04959 (2022) - 2021
- [j3]Dongsheng Li, Zhiyao Hu, Zhiquan Lai, Yiming Zhang, Kai Lu:
Coordinative Scheduling of Computation and Communication in Data-Parallel Systems. IEEE Trans. Computers 70(12): 2182-2197 (2021) - [c15]Lizhi Zhang, Zhiquan Lai, Shengwei Li, Yu Tang, Feng Liu, Dongsheng Li:
2PGraph: Accelerating GNN Training over Large Graphs on GPU Clusters. CLUSTER 2021: 103-113 - [c14]Keshi Ge, Yiming Zhang, Yongquan Fu, Zhiquan Lai, Xiaoge Deng, Dongsheng Li:
CASQ: Accelerate Distributed Deep Learning with Sketch-Based Gradient Quantization. CLUSTER 2021: 825-826 - [c13]Xiangyu Ye, Zhiquan Lai, Dongsheng Li:
Prediction of the Cyanobacteria Coverage in Time-series Images based on Convolutional Neural Network. ICCCV 2021: 153-158 - [c12]Yuetong Yang, Zhiquan Lai, Lei Cai, Dongsheng Li:
HMA: An Efficient Training Method for NLP Models. ICIAI 2021: 20-25 - [c11]Xiangyu Ye, Zhiquan Lai, Shengwei Li, Lei Cai, Ding Sun, Linbo Qiao, Dongsheng Li:
Hippie: A Data-Paralleled Pipeline Approach to Improve Memory-Efficiency and Scalability for Large DNN Training. ICPP 2021: 71:1-71:10 - [c10]Zhejiang Ran, Zhiquan Lai, Lizhi Zhang, Dongsheng Li:
Accelerate Graph Neural Network Training by Reusing Batch Data on GPUs. IPCCC 2021: 1-8 - [c9]Lizhi Zhang, Zhiquan Lai, Yu Tang, Dongsheng Li, Feng Liu, Xiaochun Luo:
PCGraph: Accelerating GNN Inference on Large Graphs via Partition Caching. ISPA/BDCloud/SocialCom/SustainCom 2021: 279-287 - [i4]Ning Liu, Songlei Jian, Dongsheng Li, Yiming Zhang, Zhiquan Lai, Hongzuo Xu:
Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning. CoRR abs/2104.05960 (2021) - [i3]Keshi Ge, Yongquan Fu, Zhiquan Lai, Xiaoge Deng, Dongsheng Li:
S2 Reducer: High-Performance Sparse Communication to Accelerate Distributed Deep Learning. CoRR abs/2110.02140 (2021) - [i2]Shengwei Li, Zhiquan Lai, Dongsheng Li, Xiangyu Ye, Yabo Duan:
EmbRace: Accelerating Sparse Communication for Distributed Training of NLP Neural Networks. CoRR abs/2110.09132 (2021) - 2020
- [c8]Yuetong Yang, Zhiquan Lai, Lei Cai, Dongsheng Li:
Poster Abstract: Model Average-based Distributed Training for Sparse Deep Neural Networks. INFOCOM Workshops 2020: 1346-1347 - [c7]Yu Tang, Zhigang Kan, Dequan Sun, Linbo Qiao, Jingjing Xiao, Zhiquan Lai, Dongsheng Li:
ADMMiRNN: Training RNN with Stable Convergence via an Efficient ADMM Approach. ECML/PKDD (2) 2020: 3-18 - [i1]Yu Tang, Zhigang Kan, Dequan Sun, Linbo Qiao, Jingjing Xiao, Zhiquan Lai, Dongsheng Li:
ADMMiRNN: Training RNN with Stable Convergence via An Efficient ADMM Approach. CoRR abs/2006.05622 (2020)
2010 – 2019
- 2019
- [c6]Dongsheng Li, Zhiquan Lai, Keshi Ge, Yiming Zhang, Zhaoning Zhang, Qinglin Wang, Huaimin Wang:
HPDL: Towards a General Framework for High-performance Distributed Deep Learning. ICDCS 2019: 1742-1753 - 2017
- [j2]Zhiquan Lai, King Tin Lam, Cho-Li Wang, Jinshu Su:
PoweRock: Power Modeling and Flexible Dynamic Power Management for Many-Core Architectures. IEEE Syst. J. 11(2): 600-612 (2017) - [c5]Yan Zhu, Guidong Zhang, Zhiquan Lai, Boya Niu, Yongjun Shen:
A Two-Tiered Defence of Techniques to Prevent SQL Injection Attacks. IMIS 2017: 286-295 - 2015
- [j1]Zhiquan Lai, King Tin Lam, Cho-Li Wang, Jinshu Su:
Latency-aware DVFS for efficient power state transitions on many-core architectures. J. Supercomput. 71(7): 2720-2747 (2015) - 2014
- [c4]King Tin Lam, Jinghao Shi, Dominic Hung, Cho-Li Wang, Zhiquan Lai, Wangbin Zhu, Youliang Yan:
Rhymes: A shared virtual memory system for non-coherent tiled many-core architectures. ICPADS 2014: 183-190 - [c3]Zhiquan Lai, Baokang Zhao, Jinshu Su:
Efficient DVFS to Prevent Hard Faults for Many-Core Architectures. ICT-EurAsia 2014: 674-679 - [c2]Zhiquan Lai, King Tin Lam, Cho-Li Wang, Jinshu Su:
A Power Modelling Approach for Many-Core Architectures. SKG 2014: 128-132 - 2012
- [c1]Lin-Bo Qiao, Bo-Feng Zhang, Zhiquan Lai, Jinshu Su:
Mining of Attack Models in IDS Alerts from Network Backbone by a Two-stage Clustering Method. IPDPS Workshops 2012: 1263-1269
Coauthor Index
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last updated on 2024-09-22 00:33 CEST by the dblp team
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