Jiangsu Du

Jiangsu Du

Seek New Memory, Feel Alive.

About me

Hi, I am currently a postdoctoral research fellow at Sun Yat-sen University, working on High Performance Computing, and Efficient ML Inference/Training System.

You can find my email address by clicking this button:


:school: Education

  • Ph.D in Computer Science, Sun Yat-sen University (National SuperComputer Center in Guangzhou).
  • Visiting Scholar, National University of Singapore (HPC-AI lab).
  • M.Sc in High Performance Computing and Data Science, The University of Edinburgh (Edinburgh Parallel Computing Center).
  • B.Sc. in Spatial Information and Digital Technology, Wuhan University.

:fire: News

  • [2024-10] One Paper accepted for ASPLOS 2025, congratulations to Shenggan Cheng!
  • [2024-04] One Paper accepted for SC 2024, congratulations to Yuanxin Wei!
  • [2023-11] One Paper accepted for PPoPP 2024.
  • [2023-02] Started PostDoc at the Sun Yat-sen University with Prof. Yutong Lu.
  • [2021-10] Started visiting scholar at the University of Singapore with Prof. Yang You.
  • [2021-10] Joined LuChen as a Research Intern, leading the large-scale Model Inference Project, EnergonAI, cooperating with Jiarui Fang, Shenggan Cheng, and Ziming Liu.
  • [2021-06] Joined Tencent Shanghai, Visualization Group, as a Research Intern, working on technical verification of GPU pooling, mentored by Song Jike and Feng Kehuan.
  • [2019-02] Visited Svalbard, what an amazing place.
  • [2018-09] Started my Ph.D. at Sun Yat-sen University with Prof. Xiangke Liao and Prof. Yunfei Du.

:blue_book: Selected Publications

Conference Publications

  • [ASPLOS 2025] Shenggan Cheng, Shengjie Lin, Lansong Diao, Hao Wu, Siyu Wang, Chang Si, Ziming Liu, Xuanlei Zhao, Jiangsu Du, Wei Lin, and Yang You, “Concerto: Automatic Communication Optimization and Scheduling for Large-Scale Deep Learning”.
  • [PPoPP 2024] Jiangsu Du, Jinhui Wei, Jiazhi Jiang, Shenggan Cheng, Dan Huang, Zhiguang Chen, Yulong Lu, “Liger: Interleaving Intra- and Inter-Operator Parallelism for Distributed Large Model Inference”.
  • [SC 2024] Yuanxin Wei, Jiangsu Du*, Jiazhi Jiang, Xiao Shi, Xianwei Zhang, Dan Huang*, Nong Xiao, Yutong LU, “APTMoE: Affinity-Aware Pipeline Tuning for MoE Models on Bandwidth-Constrained GPU Nodes”.
  • [INFOCOM 2024] Shengyuan Ye, Jiangsu Du*, Liekang Zeng, Wenzhong Ou, Xiaowen Chu, Yutong Lu, Xu Chen*, “Galaxy: A Resource-Efficient Collaborative Edge AI System for In-situ Transformer Inference”.
  • [DATE 2024] Yuanxin Wei, Shengyuan Ye, Jiazhi Jiang, Xu Chen, Dan Huang*, Jiangsu Du*, Yutong Lu, “Communication-Efficient Model Parallelism for Distributed In-situ Transformer Inference”.
  • [NPC 2024] Yu Li, Yuanxin Wei, Jiangsu Du, Dan Huang, Nong Xiao, “Understanding the Inference Performance of Spatial Temporal Diffusion Transformer”.
  • [ICCD 2023] Jiazhi Jiang, Rui Tian, Jiangsu Du, Dan Huang, Yutong Lu, “MixRec: Orchestrating Concurrent Recommendation Model Training on CPU-GPU platform”.
  • [DATE 2023] Jiazhi Jiang, Zhijian Huang, Dan Huang, Jiangsu Du, Yutong Lu, “Accelerating Inference of 3D-CNN on ARM Many-core CPU via Hierarchical Model Partition”.
  • [ICS 2022] Jiangsu Du, Jiazhi Jiang, Yang You, Dan Huang, Yutong Lu, “Handling Heavy-tailed Input of Transformer Inference on GPUs”.
  • [ICCD 2020] Jiangsu Du, Minghua Shen, Yunfei Du. “A Distributed In-Situ CNN Inference System for IoT Applications”.
  • [ICPP 2022] Jiazhi Jiang, Jiangsu Du, Dan Huang, Dongsheng Li, Jiang Zheng, Yutong Lu. “Characterizing and optimizing transformer inference on arm many-core processor”.

Journal Publications

  • [TPDS] Jiangsu Du, Xin Zhu, Minghua Shen, Yunfei Du, Yutong Lu, Nong Xiao, and Xiangke Liao, “Model Parallelism Optimization for Distributed Inference via Decoupled CNN Structure”.
  • [TACO] Jiangsu Du, Jiazhi Jiang, Jiang Zheng, Hongbin Zhang, Dan Huang, Yutong Lu, “Improving Computation and Memory Efficiency for Real-world Transformer Inference on GPUs”.
  • [JCST] Jiangsu Du, Dongsheng Li, Yingpeng Wen, Jiazhi Jiang, Dan Huang, Xiangke Liao, and Yutong Lu, “SAIH: A Scalable Evaluation Methodology for Understanding AI Performance Trend on HPC Systems”.
  • [IOTJ] Jiangsu Du, Yunfei Du, Dan Huang, Yutong Lu, and Xiangke Liao, “Enhancing Distributed In-Situ CNN Inference in the Internet of Things”
  • [TPDS] Rui Tian, Jiazhi Jiang, Jiangsu Du, Dan Huang, Yutong Lu, “Sophisticated Orchestrating Concurrent DLRM Training on CPU/GPU Platform”.
  • [TPDS] Jiazhi Jiang, Jiangsu Du, Dan Huang, Zhiguang Chen, Yutong Lu, Xiangke Liao, “Full-Stack Optimizing Transformer Inference on ARM Many-Core CPU”.