| Retrieval-augmented generation for ai-generated content: A survey P Zhao, H Zhang, Q Yu, Z Wang, Y Geng, F Fu, L Yang, W Zhang, J Jiang, ... arXiv preprint arXiv:2402.19473, 2024 | 199 | 2024 |
| Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism X Miao, Y Wang, Y Jiang, C Shi, X Nie, H Zhang, B Cui Proceedings of the VLDB Endowment 16.3 (2022): 470–479., 2022 | 59 | 2022 |
| HET: scaling out huge embedding model training via cache-enabled distributed framework X Miao, H Zhang, Y Shi, X Nie, Z Yang, Y Tao, B Cui Proceedings of the VLDB Endowment 15.2 (2021): 312-320., 2021 | 56 | 2021 |
| Hetu: A highly efficient automatic parallel distributed deep learning system X Miao, X Nie, H Zhang, T Zhao, B Cui Science China. Information Sciences 66 (1), 117101, 2023 | 19 | 2023 |
| HET-GMP: A graph-based system approach to scaling large embedding model training X Miao, Y Shi, H Zhang, X Zhang, X Nie, Z Yang, B Cui Proceedings of the 2022 International Conference on Management of Data, 470-480, 2022 | 19 | 2022 |
| Model-enhanced vector index H Zhang, Y Wang, Q Chen, R Chang, T Zhang, Z Miao, Y Hou, Y Ding, ... Advances in Neural Information Processing Systems 36, 54903-54917, 2023 | 15 | 2023 |
| PQCache: Product Quantization-based KVCache for Long Context LLM Inference H Zhang, X Ji, Y Chen, F Fu, X Miao, X Nie, W Chen, B Cui arXiv preprint arXiv:2407.12820, 2024 | 12 | 2024 |
| Experimental analysis of large-scale learnable vector storage compression H Zhang, P Zhao, X Miao, Y Shao, Z Liu, T Yang, B Cui Proceedings of the VLDB Endowment 17.4 (2023): 808–822., 2023 | 11 | 2023 |
| Cafe: Towards compact, adaptive, and fast embedding for large-scale recommendation models H Zhang, Z Liu, B Chen, Y Zhao, T Zhao, T Yang, B Cui Proceedings of the ACM on Management of Data 2 (1), 1-28, 2024 | 9 | 2024 |
| MEMO: Fine-grained Tensor Management For Ultra-long Context LLM Training P Zhao, H Zhang, F Fu, X Nie, Q Liu, F Yang, Y Peng, D Jiao, S Li, J Xue, ... Proceedings of the ACM on Management of Data 3 (1), 1-28, 2025 | 4* | 2025 |
| Surge phenomenon in optimal learning rate and batch size scaling S Li, P Zhao, H Zhang, X Sun, H Wu, D Jiao, W Wang, C Liu, Z Fang, ... arXiv preprint arXiv:2405.14578, 2024 | 4 | 2024 |
| Enabling Parallelism Hot Switching for Efficient Training of Large Language Models H Ge, F Fu, H Li, X Wang, S Lin, Y Wang, X Nie, H Zhang, X Miao, B Cui Proceedings of the ACM SIGOPS 30th Symposium on Operating Systems Principles …, 2024 | 2 | 2024 |
| CAFE+: Towards Compact, Adaptive, and Fast Embedding for Large-scale Online Recommendation Models Z Liu, H Zhang, B Chen, Z Jiang, Y Zhao, Y Tao, T Yang, B Cui ACM Transactions on Information Systems, 2025 | | 2025 |
| Malleus: Straggler-Resilient Hybrid Parallel Training of Large-scale Models via Malleable Data and Model Parallelization H Li, F Fu, H Ge, S Lin, X Wang, J Niu, Y Wang, H Zhang, X Nie, B Cui arXiv preprint arXiv:2410.13333, 2024 | | 2024 |
| A Unified Framework for Mining Batch and Periodic Batch in Data Streams Z Liu, X Wang, Y Wu, T Yang, K Yang, H Zhang, Y Tu, B Cui IEEE Transactions on Knowledge and Data Engineering, 2024 | | 2024 |
