Hailin Zhang (张海林)

Hailin Zhang (张海林)

PhD student at CS, PKU

Hailin Zhang (张海林)

Email: z.hl [AT] pku.edu.cn

I am a 4th-year PhD student at the School of Computer Science, Peking University, under the supervision of Professor Bin Cui. My research interests primarily lie in the field of MLSys (Machine Learning System), with a focus on large-scale LLM (Large Language Model), DLRM (Deep Learning Recommendation Model), IR (Information Retrieval), and general distributed computing.

I am currently on the job market. Please feel free to reach out if you have openings.

Education

  • PhD, major in Computer Science
    Peking University 2020-Now

  • BS, major in Computer Science; BEc, double major in Economics
    Peking University 2016-2020

Publications

* represents co-first author.

2024

  1. PQCache: Product Quantization-based KVCache for Long Context LLM Inference. PDF
    Hailin Zhang, Xiaodong Ji, Yilin Chen, Fangcheng Fu, Xupeng Miao, Xiaonan Nie, Weipeng Chen, Bin Cui.
    Preprint.

  2. Efficiently Training 7B LLM with 1 Million Sequence Length on 8 GPUs. PDF
    Pinxue Zhao, Hailin Zhang, Fangcheng Fu, Xiaonan Nie, Qibin Liu, Fang Yang, Yuanbo Peng, Dian Jiao, Shuaipeng Li, Jinbao Xue, Yangyu Tao, Bin Cui.
    Preprint.

  3. Retrieval-Augmented Generation for AI-Generated Content: A Survey. PDF
    Penghao Zhao*, Hailin Zhang*, Qinhan Yu, Zhengren Wang, Yunteng Geng, Fangcheng Fu, Ling Yang, Wentao Zhang, Jie Jiang, Bin Cui.
    Preprint.

  4. Surge Phenomenon in Optimal Learning Rate and Batch Size Scaling. PDF
    Shuaipeng Li*, Penghao Zhao*, Hailin Zhang*, Xingwu Sun, Hao Wu, Dian Jiao, Weiyan Wang, Chengjun Liu, Zheng Fang, Jinbao Xue, Yangyu Tao, Bin Cui, Di Wang.
    Preprint.

  5. CAFE: Towards Compact, Adaptive, and Fast Embedding for Large-scale Recommendation Models. PDF
    Hailin Zhang*, Zirui Liu*, Boxuan Chen, Yikai Zhao, Tong Zhao, Tong Yang, Bin Cui.
    ACM SIGMOD International Conference on Management of Data.
    SIGMOD 2024, CCF-A.

  6. Experimental Analysis of Large-scale Learnable Vector Storage Compression. PDF
    Hailin Zhang, Penghao Zhao, Xupeng Miao, Yingxia Shao, Zirui Liu, Tong Yang, Bin Cui.
    International Conference on Very Large Data Bases.
    VLDB 2024, CCF-A.

  7. A Unified Framework for Mining Batch and Periodic Batch in Data Streams. PDF
    Zirui Liu, Xiangyuan Wang, Yuhan Wu, Tong Yang, Kaicheng Yang, Hailin Zhang, Yaofeng Tu, Bin Cui.
    IEEE Transactions on Knowledge and Data Engineering.
    TKDE 2024, CCF-A.

2023

  1. Model-enhanced Vector Index. PDF
    Hailin Zhang, Yujing Wang, Qi Chen, Ruiheng Chang, Ting Zhang, Ziming Miao, Yingyan Hou, Yang Ding, Xupeng Miao, Haonan Wang, Bochen Pang, Yuefeng Zhan, Hao Sun, Weiwei Deng, Qi Zhang, Fan Yang, Xing Xie, Mao Yang, Bin Cui.
    Conference on Neural Information Processing Systems.
    NeurIPS 2023, CCF-A.

  2. Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism. PDF
    Xupeng Miao, Yujie Wang, Youhe Jiang, Chunan Shi, Xiaonan Nie, Hailin Zhang, Bin Cui.
    International Conference on Very Large Data Bases.
    VLDB 2023, CCF-A.

2022

  1. Hetu: A Highly Efficient Automatic Parallel Distributed Deep Learning System. PDF
    Xupeng Miao, Xiaonan Nie, Hailin Zhang, Tong Zhao, Bin Cui.
    Science China Information Sciences.
    SCIS 2022, CCF-A.

  2. HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework. PDF
    Xupeng Miao*, Hailin Zhang*, Yining Shi, Xiaonan Nie, Zhi Yang, Yangyu Tao, Bin Cui.
    International Conference on Very Large Data Bases.
    VLDB 2022, CCF-A, Best Scalable Data Science Paper!

  3. HET-GMP: A Graph-based System Approach to Scaling Large Embedding Model Training. PDF
    Xupeng Miao, Yining Shi, Hailin Zhang, Xin Zhang, Xiaonan Nie, Zhi Yang, Bin Cui.
    ACM SIGMOD International Conference on Management of Data.
    SIGMOD 2022, CCF-A.

Systems