Hailin Zhang

According to our database, Hailin Zhang authored at least 14 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

A Unified Framework for Mining Batch and Periodic Batch in Data Streams.

Zirui Liu, Xiangyuan Wang, Yuhan Wu, Tong Yang, Kaicheng Yang, Hailin Zhang, Yaofeng Tu, Bin Cui

IEEE Trans. Knowl. Data Eng., November, 2024

CAFE: Towards Compact, Adaptive, and Fast Embedding for Large-scale Recommendation Models.

Hailin Zhang, Zirui Liu, Boxuan Chen, Yikai Zhao, Tong Zhao, Tong Yang, Bin Cui

Proc. ACM Manag. Data, February, 2024

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, Xiaonan Nie, Bin Cui

CoRR, 2024

PQCache: Product Quantization-based KVCache for Long Context LLM Inference.

Hailin Zhang, Xiaodong Ji, Yilin Chen, Fangcheng Fu, Xupeng Miao, Xiaonan Nie, Weipeng Chen, Bin Cui

CoRR, 2024

Efficiently Training 7B LLM with 1 Million Sequence Length on 8 GPUs.

Pinxue Zhao, Hailin Zhang, Fangcheng Fu, Xiaonan Nie, Qibin Liu, Fang Yang, Yuanbo Peng, Dian Jiao, Shuaipeng Li, Jinbao Xue, Yangyu Tao, Bin Cui

CoRR, 2024

Surge Phenomenon in Optimal Learning Rate and Batch Size Scaling.

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

CoRR, 2024

Retrieval-Augmented Generation for AI-Generated Content: A Survey.

Penghao Zhao, Hailin Zhang, Qinhan Yu, Zhengren Wang, Yunteng Geng, Fangcheng Fu, Ling Yang, Wentao Zhang, Bin Cui

CoRR, 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, Xupeng Miao, Bin Cui

Proceedings of the ACM SIGOPS 30th Symposium on Operating Systems Principles, 2024

2023

Experimental Analysis of Large-scale Learnable Vector Storage Compression.

Hailin Zhang, Penghao Zhao, Xupeng Miao, Yingxia Shao, Zirui Liu, Tong Yang, Bin Cui

Proc. VLDB Endow., December, 2023

Hetu: a highly efficient automatic parallel distributed deep learning system.

Xupeng Miao, Xiaonan Nie, Hailin Zhang, Tong Zhao, Bin Cui

Sci. China Inf. Sci., January, 2023

Model-enhanced Vector Index.

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism.

Xupeng Miao, Yujie Wang, Youhe Jiang, Chunan Shi, Xiaonan Nie, Hailin Zhang, Bin Cui

Proc. VLDB Endow., 2022

HET-GMP: A Graph-based System Approach to Scaling Large Embedding Model Training.

Xupeng Miao, Yining Shi, Hailin Zhang, Xin Zhang, Xiaonan Nie, Zhi Yang, Bin Cui

Proceedings of the SIGMOD '22: International Conference on Management of Data, Philadelphia, PA, USA, June 12, 2022

2021

HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework.

Xupeng Miao, Hailin Zhang, Yining Shi, Xiaonan Nie, Zhi Yang, Yangyu Tao, Bin Cui

Proc. VLDB Endow., 2021