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Redundancy-free and load-balanced TGNN training with hierarchical pipeline parallelism Journal article
Xia, Yaqi, Zhang, Zheng, Yang, Donglin, Hu, Chuang, Zhou, Xiaobo, Chen, Hongyang, Sang, Qianlong, Cheng, Dazhao. Redundancy-free and load-balanced TGNN training with hierarchical pipeline parallelism[J]. IEEE Transactions on Parallel and Distributed Systems, 2024, 35(11), 1904-1919.
Authors:  Xia, Yaqi;  Zhang, Zheng;  Yang, Donglin;  Hu, Chuang;  Zhou, Xiaobo; et al.
Favorite | TC[WOS]:0 TC[Scopus]:1  IF:5.6/4.5 | Submit date：2024/08/05
Communication Balance  Distributed Training  Dynamic Gnn  Pipeline Parallelism  Redundancy-free  
MPMoE: Memory Efficient MoE for Pre-Trained Models With Adaptive Pipeline Parallelism Journal article
Zhang, Zheng, Xia, Yaqi, Wang, Hulin, Yang, Donglin, Hu, Chuang, Zhou, Xiaobo, Cheng, Dazhao. MPMoE: Memory Efficient MoE for Pre-Trained Models With Adaptive Pipeline Parallelism[J]. IEEE Transactions on Parallel and Distributed Systems, 2024, 35(6), 843-856.
Authors:  Zhang, Zheng;  Xia, Yaqi;  Wang, Hulin;  Yang, Donglin;  Hu, Chuang; et al.
Favorite | TC[WOS]:0 TC[Scopus]:4  IF:5.6/4.5 | Submit date：2024/05/16
Distributed Training  Memory Redundancy  Mixture Of Experts  Performance Model  Pipeline Parallelism  
Planck: Optimizing LLM Inference Performance in Pipeline Parallelism with Fine-Grained SLO Constraint Journal article
Lin, Yanying, Peng, Shijie, Wu, Shuaipeng, Li, Yanbo, Lu, Chengzhi, Xu, Chengzhong, Ye, Kejiang. Planck: Optimizing LLM Inference Performance in Pipeline Parallelism with Fine-Grained SLO Constraint[J]. Proceedings of the IEEE International Conference on Web Services, ICWS, 2024, 1306-1313.
Authors:  Lin, Yanying;  Peng, Shijie;  Wu, Shuaipeng;  Li, Yanbo;  Lu, Chengzhi; et al.
Favorite | TC[WOS]:0 TC[Scopus]:1 | Submit date：2024/12/26
LLM Serving  Pipeline Bubble  Pipeline Parallelism  SLO Constraint  
Planck: Optimizing LLM Inference Performance in Pipeline Parallelism with Fine-Grained SLO Constraint Conference paper
Lin, Yanying, Peng, Shijie, Wu, Shuaipeng, Li, Yanbo, Lu, Chengzhi, Xu, Chengzhong, Ye, Kejiang. Planck: Optimizing LLM Inference Performance in Pipeline Parallelism with Fine-Grained SLO Constraint[C]:Institute of Electrical and Electronics Engineers Inc., 2024, 1306-1313.
Authors:  Lin, Yanying;  Peng, Shijie;  Wu, Shuaipeng;  Li, Yanbo;  Lu, Chengzhi; et al.
Favorite | TC[WOS]:0 TC[Scopus]:1 | Submit date：2024/12/05
Llm Serving  Pipeline Bubble  Pipeline Parallelism  Slo Constraint  
MPipeMoE: Memory Efficient MoE for Pre-trained Models with Adaptive Pipeline Parallelism Conference paper
Zhang, Zheng, Yang, Donglin, Xia, Yaqi, Ding, Liang, Tao, Dacheng, Zhou, Xiaobo, Cheng, Dazhao. MPipeMoE: Memory Efficient MoE for Pre-trained Models with Adaptive Pipeline Parallelism[C], USA:Institute of Electrical and Electronics Engineers Inc., 2023, 167-177.
Authors:  Zhang, Zheng;  Yang, Donglin;  Xia, Yaqi;  Ding, Liang;  Tao, Dacheng; et al.
Favorite | TC[WOS]:2 TC[Scopus]:6 | Submit date：2023/08/08
Mixture Of Experts  Pipeline Parallelism  Distributed Training  Memory Efficiency