HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework. - Xupeng Miao; Hailin Zhang 004; Yining Shi 001; Xiaonan Nie; Zhi Yang 001; Yangyu Tao; Bin Cui 001 (Proc. VLDB Endow., 2021)

HET-GMP: A Graph-based System Approach to Scaling Large Embedding Model Training. - Xupeng Miao; Yining Shi 001; Hailin Zhang 004; Xin Zhang; Xiaonan Nie; Zhi Yang 001; Bin Cui 001 (SIGMOD Conference, 2022)

Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism. - Xupeng Miao; Yujie Wang; Youhe Jiang; Chunan Shi; Xiaonan Nie; Hailin Zhang 004; Bin Cui 001 (Proc. VLDB Endow., 2022)

Model-enhanced Vector Index. - Hailin Zhang 004; Yujing Wang; Qi Chen 009; Ruiheng Chang; Ting Zhang 002; Ziming Miao; Yingyan Hou; Yang Ding; Xupeng Miao; Haonan Wang; Bochen Pang; Yuefeng Zhan; et al. (NeurIPS, 2023)

Hetu: a highly efficient automatic parallel distributed deep learning system. - Xupeng Miao; Xiaonan Nie; Hailin Zhang 004; Tong Zhao; Bin Cui 001 (Sci. China Inf. Sci., 2023)

Experimental Analysis of Large-scale Learnable Vector Storage Compression. - Hailin Zhang 004; Penghao Zhao; Xupeng Miao; Yingxia Shao; Zirui Liu 002; Tong Yang 003; Bin Cui 001 (Proc. VLDB Endow., 2023)

Surge Phenomenon in Optimal Learning Rate and Batch Size Scaling. - Shuaipeng Li; Penghao Zhao; Hailin Zhang 004; Xingwu Sun; Hao Wu; Dian Jiao; Weiyan Wang; Chengjun Liu; Zheng Fang; Jinbao Xue; Yangyu Tao; Bin Cui 001; et al. (NeurIPS, 2024)

Enabling Parallelism Hot Switching for Efficient Training of Large Language Models. - Hao Ge; Fangcheng Fu; Haoyang Li; Xuanyu Wang; Sheng Lin; Yujie Wang; Xiaonan Nie; Hailin Zhang 004; Xupeng Miao; Bin Cui 001 (SOSP, 2024)

Retrieval-Augmented Generation for AI-Generated Content: A Survey. - Penghao Zhao; Hailin Zhang 004; Qinhan Yu; Zhengren Wang; Yunteng Geng; Fangcheng Fu; Ling Yang 006; Wentao Zhang 001; Bin Cui 001 (CoRR, 2024)

Efficiently Training 7B LLM with 1 Million Sequence Length on 8 GPUs. - Pinxue Zhao; Hailin Zhang 004; Fangcheng Fu; Xiaonan Nie; Qibin Liu; Fang Yang; Yuanbo Peng; Dian Jiao; Shuaipeng Li; Jinbao Xue; Yangyu Tao; Bin Cui 001 (CoRR, 2024)

CAFE: Towards Compact, Adaptive, and Fast Embedding for Large-scale Recommendation Models. - Hailin Zhang 004; Zirui Liu 002; Boxuan Chen; Yikai Zhao 001; Tong Zhao; Tong Yang 003; Bin Cui 001 (Proc. ACM Manag. Data, 2024)

A Unified Framework for Mining Batch and Periodic Batch in Data Streams. - Zirui Liu 002; Xiangyuan Wang; Yuhan Wu 001; Tong Yang 003; Kaicheng Yang 001; Hailin Zhang 004; Yaofeng Tu; Bin Cui 001 (IEEE Trans. Knowl. Data Eng., 2024)

MEMO: Fine-grained Tensor Management For Ultra-long Context LLM Training. - Pinxue Zhao; Hailin Zhang 004; Fangcheng Fu; Xiaonan Nie; Qibin Liu; Fang Yang; Yuanbo Peng; Dian Jiao; Shuaipeng Li; Jinbao Xue; Yangyu Tao; Bin Cui 001 (Proc. ACM Manag. Data, 2025)

SALE : Low-bit Estimation for Efficient Sparse Attention in Long-context LLM Prefilling. - Xiaodong Ji; Hailin Zhang 004; Fangcheng Fu; Bin Cui 001 (CoRR, 2025)

CAFE+: Towards Compact, Adaptive, and Fast Embedding for Large-scale Online Recommendation Models. - Zirui Liu 002; Hailin Zhang 004; Boxuan Chen; Zihan Jiang; Yikai Zhao 001; Yangyu Tao; Tong Yang 003; Bin Cui 001 (ACM Trans. Inf. Syst., 2025)

Efficient and scalable huge embedding model training via distributed cache management. - Xupeng Miao; Hailin Zhang 004; Yining Shi 001; Xiaonan Nie; Zhi Yang 001; Yangyu Tao; Jie Jiang; Bin Cui 001 (VLDB J., 2025)

Malleus: Straggler-Resilient Hybrid Parallel Training of Large-scale Models via Malleable Data and Model Parallelization. - Haoyang Li; Fangcheng Fu; Hao Ge; Sheng Lin; Xuanyu Wang; Jiawen Niu; Yujie Wang; Hailin Zhang 004; Xiaonan Nie; Bin Cui 001 (Proc. ACM Manag. Data, 2025)

PQCache: Product Quantization-based KVCache for Long Context LLM Inference. - Hailin Zhang 004; Xiaodong Ji; Yilin Chen; Fangcheng Fu; Xupeng Miao; Xiaonan Nie; Weipeng Chen; Bin Cui 001 (Proc. ACM Manag. Data, 2025)