Fangcheng Fu

According to our database, Fangcheng Fu authored at least 57 papers between 2018 and 2025.

Collaborative distances:

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

2025

Efficient Mixed-Precision Large Language Model Inference with TurboMind.

Li Zhang, Youhe Jiang, Guoliang He, Xin Chen, Han Lv, Qian Yao, Fangcheng Fu, Kai Chen

CoRR, August, 2025

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

Proc. ACM Manag. Data, June, 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, Xiaonan Nie, Bin Cui

Proc. ACM Manag. Data, June, 2025

How Significant Are the Real Performance Gains? An Unbiased Evaluation Framework for GraphRAG.

Qiming Zeng, Xiao Yan, Hao Luo, Yuhao Lin, Yuxiang Wang, Fangcheng Fu, Bo Du, Quanqing Xu, Jiawei Jiang

CoRR, June, 2025

Cascadia: A Cascade Serving System for Large Language Models.

Youhe Jiang, Fangcheng Fu, Wanru Zhao, Stephan Rabanser, Nicholas D. Lane, Binhang Yuan

CoRR, June, 2025

SALE : Low-bit Estimation for Efficient Sparse Attention in Long-context LLM Prefilling.

Xiaodong Ji, Hailin Zhang, Fangcheng Fu, Bin Cui

CoRR, May, 2025

Thinking Short and Right Over Thinking Long: Serving LLM Reasoning Efficiently and Accurately.

Yuhang Wang, Youhe Jiang, Bin Cui, Fangcheng Fu

CoRR, May, 2025

LobRA: Multi-tenant Fine-tuning over Heterogeneous Data.

Sheng Lin, Fangcheng Fu, Haoyang Li, Hao Ge, Xuanyu Wang, Jiawen Niu, Yaofeng Tu, Bin Cui

Proc. VLDB Endow., April, 2025

Galvatron: An Automatic Distributed System for Efficient Foundation Model Training.

Xinyi Liu, Yujie Wang, Shenhan Zhu, Fangcheng Fu, Qingshuo Liu, Guangming Lin, Bin Cui

CoRR, April, 2025

Hetu v2: A General and Scalable Deep Learning System with Hierarchical and Heterogeneous Single Program Multiple Data Annotations.

Haoyang Li, Fangcheng Fu, Hao Ge, Sheng Lin, Xuanyu Wang, Jiawen Niu, Xupeng Miao, Bin Cui

CoRR, April, 2025

Detecting and Analyzing Motifs in Large-Scale Online Transaction Networks.

Jiawei Jiang, Hao Huang, Zhigao Zheng, Yi Wei, Fangcheng Fu, Xiaosen Li, Bin Cui

IEEE Trans. Knowl. Data Eng., February, 2025

MEMO: Fine-grained Tensor Management For Ultra-long Context LLM Training.

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

Proc. ACM Manag. Data, February, 2025

ByteScale: Efficient Scaling of LLM Training with a 2048K Context Length on More Than 12,000 GPUs.

Hao Ge, Junda Feng, Qi Huang, Fangcheng Fu, Xiaonan Nie, Lei Zuo, Haibin Lin, Bin Cui, Xin Liu

CoRR, February, 2025

Training-free and Adaptive Sparse Attention for Efficient Long Video Generation.

Yifei Xia, Suhan Ling, Fangcheng Fu, Yujie Wang, Huixia Li, Xuefeng Xiao, Bin Cui

CoRR, February, 2025

ThunderServe: High-performance and Cost-efficient LLM Serving in Cloud Environments.

Youhe Jiang, Fangcheng Fu, Xiaozhe Yao, Taiyi Wang, Bin Cui, Ana Klimovic, Eiko Yoneki

CoRR, February, 2025

Demystifying Cost-Efficiency in LLM Serving over Heterogeneous GPUs.

Youhe Jiang, Fangcheng Fu, Xiaozhe Yao, Guoliang He, Xupeng Miao, Ana Klimovic, Bin Cui, Binhang Yuan, Eiko Yoneki

CoRR, February, 2025

NetMoE: Accelerating MoE Training through Dynamic Sample Placement.

Xinyi Liu, Yujie Wang, Fangcheng Fu, Xupeng Miao, Shenhan Zhu, Xiaonan Nie, Bin Cui

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Hounding Data Diversity: Towards Participant Selection in Vertical Federated Learning.

Xiaokai Zhou, Xiao Yan, Fangcheng Fu, Xinyan Li, Hao Huang, Quanqing Xu, Chuanhui Yang, Bo Du, Tieyun Qian, Jiawei Jiang

Proceedings of the 41st IEEE International Conference on Data Engineering, 2025

Towards Scalable and Efficient Graph Structure Learning.

Siqi Shen, Wentao Zhang, Chengshuo Du, Chong Chen, Fangcheng Fu, Yingxia Shao, Bin Cui

Proceedings of the 41st IEEE International Conference on Data Engineering, 2025

Spindle: Efficient Distributed Training of Multi-Task Large Models via Wavefront Scheduling.

Yujie Wang, Shenhan Zhu, Fangcheng Fu, Xupeng Miao, Jie Zhang, Juan Zhu, Fan Hong, Yong Li, Bin Cui

Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2025

FlexSP: Accelerating Large Language Model Training via Flexible Sequence Parallelism.

Yujie Wang, Shiju Wang, Shenhan Zhu, Fangcheng Fu, Xinyi Liu, Xuefeng Xiao, Huixia Li, Jiashi Li, Faming Wu, Bin Cui

Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2025

Enhancing Unsupervised Sentence Embeddings via Knowledge-Driven Data Augmentation and Gaussian-Decayed Contrastive Learning.

Peichao Lai, Zhengfeng Zhang, Wentao Zhang, Fangcheng Fu, Bin Cui

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

HaCore: Efficient Coreset Construction with Locality Sensitive Hashing for Vertical Federated Learning.

Qinbo Zhang, Xiao Yan, Yukai Ding, Fangcheng Fu, Quanqing Xu, Ziyi Li, Chuang Hu, Jiawei Jiang

Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024

ProjPert: Projection-Based Perturbation for Label Protection in Split Learning Based Vertical Federated Learning.

Fangcheng Fu, Xuanyu Wang, Jiawei Jiang, Huanran Xue, Bin Cui

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

Improving Automatic Parallel Training via Balanced Memory Workload Optimization.

Yujie Wang, Youhe Jiang, Xupeng Miao, Fangcheng Fu, Shenhan Zhu, Xiaonan Nie, Yaofeng Tu, Bin Cui

IEEE Trans. Knowl. Data Eng., 2024

Demystifying Workload Imbalances in Large Transformer Model Training over Variable-length Sequences.

Haoyang Li, Fangcheng Fu, Sheng Lin, Hao Ge, Xuanyu Wang, Jiawen Niu, Jie Jiang, Bin Cui

CoRR, 2024

Data-Centric and Heterogeneity-Adaptive Sequence Parallelism for Efficient LLM Training.

Yujie Wang, Shiju Wang, Shenhan Zhu, Fangcheng Fu, Xinyi Liu, Xuefeng Xiao, Huixia Li, Jiashi Li, Faming Wu, Bin Cui

CoRR, 2024

Gradual Learning: Optimizing Fine-Tuning with Partially Mastered Knowledge in Large Language Models.

Bozhou Li, Hao Liang, Yang Li, Fangcheng Fu, Hongzhi Yin, Conghui He, Wentao Zhang

CoRR, 2024

Retrofitting Temporal Graph Neural Networks with Transformer.

Qiang Huang, Xiao Yan, Xin Wang, Susie Xi Rao, Zhichao Han, Fangcheng Fu, Wentao Zhang, Jiawei Jiang

CoRR, 2024

Efficient Multi-Task Large Model Training via Data Heterogeneity-aware Model Management.

Yujie Wang, Shenhan Zhu, Fangcheng Fu, Xupeng Miao, Jie Zhang, Juan Zhu, Fan Hong, Yong Li, 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

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

Efficient Multi-task LLM Quantization and Serving for Multiple LoRA Adapters.

Yifei Xia, Fangcheng Fu, Wentao Zhang, Jiawei Jiang, Bin Cui

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing.

Xiaonan Nie, Qibin Liu, Fangcheng Fu, Shenhan Zhu, Xupeng Miao, Xiaoyang Li, Yang Zhang, Shouda Liu, Bin Cui

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

X-former Elucidator: Reviving Efficient Attention for Long Context Language Modeling.

Xupeng Miao, Shenhan Zhu, Fangcheng Fu, Ziyu Guo, Zhi Yang, Yaofeng Tu, Zhihao Jia, Bin Cui

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning.

Yuxiang Wang, Xiao Yan, Chuang Hu, Quanqing Xu, Chuanhui Yang, Fangcheng Fu, Wentao Zhang, Hao Wang, Bo Du, Jiawei Jiang

Proceedings of the 40th IEEE International Conference on Data Engineering, 2024

Accelerating Text-to-Image Editing via Cache-Enabled Sparse Diffusion Inference.

Zihao Yu, Haoyang Li, Fangcheng Fu, Xupeng Miao, Bin Cui

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

P²CG: a privacy preserving collaborative graph neural network training framework.

Xupeng Miao, Wentao Zhang, Yuezihan Jiang, Fangcheng Fu, Yingxia Shao, Lei Chen, Yangyu Tao, Gang Cao, Bin Cui

VLDB J., July, 2023

Angel-PTM: A Scalable and Economical Large-scale Pre-training System in Tencent.

Xiaonan Nie, Yi Liu, Fangcheng Fu, Jinbao Xue, Dian Jiao, Xupeng Miao, Yangyu Tao, Bin Cui

Proc. VLDB Endow., 2023

Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning.

Yuxiang Wang, Xiao Yan, Chuang Hu, Fangcheng Fu, Wentao Zhang, Hao Wang, Shuo Shang, Jiawei Jiang

CoRR, 2023

Improving Automatic Parallel Training via Balanced Memory Workload Optimization.

Yujie Wang, Youhe Jiang, Xupeng Miao, Fangcheng Fu, Xiaonan Nie, Bin Cui

CoRR, 2023

FISEdit: Accelerating Text-to-image Editing via Cache-enabled Sparse Diffusion Inference.

Zihao Yu, Haoyang Li, Fangcheng Fu, Xupeng Miao, Bin Cui

CoRR, 2023

OSDP: Optimal Sharded Data Parallel for Distributed Deep Learning.

Youhe Jiang, Fangcheng Fu, Xupeng Miao, Xiaonan Nie, Bin Cui

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

KVSAgg: Secure Aggregation of Distributed Key-Value Sets.

Yuhan Wu, Siyuan Dong, Yi Zhou, Yikai Zhao, Fangcheng Fu, Tong Yang, Chaoyue Niu, Fan Wu, Bin Cui

Proceedings of the 39th IEEE International Conference on Data Engineering, 2023

2022

Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Update.

Fangcheng Fu, Xupeng Miao, Jiawei Jiang, Huanran Xue, Bin Cui

Proc. VLDB Endow., 2022

Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates.

Fangcheng Fu, Xupeng Miao, Jiawei Jiang, Huanran Xue, Bin Cui

CoRR, 2022

BlindFL: Vertical Federated Machine Learning without Peeking into Your Data.

Fangcheng Fu, Huanran Xue, Yong Cheng, Yangyu Tao, Bin Cui

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

K-core decomposition on super large graphs with limited resources.

Shicheng Gao, Jie Xu, Xiaosen Li, Fangcheng Fu, Wentao Zhang, Wen Ouyang, Yangyu Tao, Bin Cui

Proceedings of the SAC '22: The 37th ACM/SIGAPP Symposium on Applied Computing, Virtual Event, April 25, 2022

VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely?

Jiawei Jiang, Lukas Burkhalter, Fangcheng Fu, Bolin Ding, Bo Du, Anwar Hithnawi, Bo Li, Ce Zhang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Analyzing Online Transaction Networks with Network Motifs.

Jiawei Jiang, Yusong Hu, Xiaosen Li, Wen Ouyang, Zhitao Wang, Fangcheng Fu, Bin Cui

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

2021

VF²Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning.

Fangcheng Fu, Yingxia Shao, Lele Yu, Jiawei Jiang, Huanran Xue, Yangyu Tao, Bin Cui

Proceedings of the SIGMOD '21: International Conference on Management of Data, 2021

2020

SKCompress: compressing sparse and nonuniform gradient in distributed machine learning.

Jiawei Jiang, Fangcheng Fu, Tong Yang, Yingxia Shao, Bin Cui

VLDB J., 2020

Don't Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript.

Fangcheng Fu, Yuzheng Hu, Yihan He, Jiawei Jiang, Yingxia Shao, Ce Zhang, Bin Cui

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

An Experimental Evaluation of Large Scale GBDT Systems.

Fangcheng Fu, Jiawei Jiang, Yingxia Shao, Bin Cui

Proc. VLDB Endow., 2019

2018

SketchML: Accelerating Distributed Machine Learning with Data Sketches.

Jiawei Jiang, Fangcheng Fu, Tong Yang, Bin Cui

Proceedings of the 2018 International Conference on Management of Data, 2018

DimBoost: Boosting Gradient Boosting Decision Tree to Higher Dimensions.

Jiawei Jiang, Bin Cui, Ce Zhang, Fangcheng Fu

Proceedings of the 2018 International Conference on Management of Data, 2018