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| Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs M Wang, L Yu, Q Gan, D Zheng, Y Gai, Z Ye, M Li, J Zhou, Q Huang, ... International Conference on Learning Representations, 2019 | 921 | 2019 |
| Self-Driving Database Management Systems. A Pavlo, G Angulo, J Arulraj, H Lin, J Lin, L Ma, P Menon, TC Mowry, ... CIDR 4, 1, 2017 | 372 | 2017 |
| GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing J Guo, H He, T He, L Lausen, M Li, H Lin, X Shi, C Wang, J Xie, S Zha, ... Journal of Machine Learning Research, 2019 | 256 | 2019 |
| {MegaScale}: Scaling Large Language Model Training to More Than 10,000 {GPUs} Z Jiang, H Lin, Y Zhong, Q Huang, Y Chen, Z Zhang, Y Peng, X Li, C Xie, ... 21st USENIX Symposium on Networked Systems Design and Implementation (NSDI …, 2024 | 114 | 2024 |
| Is Network the Bottleneck of Distributed Training? Z Zhang, C Chang, H Lin, Y Wang, R Arora, X Jin SIGCOMM NetAI, 2020 | 88 | 2020 |
| Temporal-Contextual Recommendation in Real-Time Y Ma, BM Narayanaswamy, H Lin, H Ding KDD 2020, 2020 | 82 | 2020 |
| Local AdaAlter: Communication-Efficient Stochastic Gradient Descent with Adaptive Learning Rates C Xie, O Koyejo, I Gupta, H Lin NeurIPS 2020, optimizations for machine learning, 2019 | 53 | 2019 |
| CSER: Communication-efficient SGD with Error Reset C Xie, S Zheng, OO Koyejo, I Gupta, M Li, H Lin Advances in Neural Information Processing Systems 33, 2020 | 47 | 2020 |
| Hi-Speed DNN Training with Espresso: Unleashing the Full Potential of Gradient Compression with Near-Optimal Usage Strategies Z Wang, H Lin, Y Zhu, TSE Ng Proceedings of the Eighteenth European Conference on Computer Systems, 867-882, 2023 | 29* | 2023 |
| Dynamic Mini-batch SGD for Elastic Distributed Training: Learning in the Limbo of Resources H Lin, H Zhang, Y Ma, T He, Z Zhang, S Zha, M Li arXiv preprint arXiv:1904.12043, 2019 | 23 | 2019 |
| Accelerated Large Batch Optimization of BERT Pretraining in 54 minutes S Zheng, H Lin, S Zha, M Li arXiv preprint arXiv:2006.13484, 2020 | 22 | 2020 |
| LEMON: Lossless model expansion Y Wang, J Su, H Lu, C Xie, T Liu, J Yuan, H Lin, R Sun, H Yang arXiv preprint arXiv:2310.07999, 2023 | 17 | 2023 |
| SAPipe: Staleness-Aware Pipeline for Data Parallel DNN Training Y Chen, C Xie, M Ma, J Gu, Y Peng, H Lin, C Wu, Y Zhu Advances in Neural Information Processing Systems 35, 17981-17993, 2022 | 15 | 2022 |
| dPRO: A Generic Performance Diagnosis and Optimization Toolkit for Expediting Distributed DNN Training H Hu, C Jiang, Y Zhong, Y Peng, C Wu, Y Zhu, H Lin, C Guo Proceedings of Machine Learning and Systems 4, 623-637, 2022 | 14 | 2022 |
| LLM-PQ: Serving LLM on Heterogeneous Clusters with Phase-Aware Partition and Adaptive Quantization J Zhao, B Wan, Y Peng, H Lin, C Wu arXiv preprint arXiv:2403.01136, 2024 | 12 | 2024 |
| HybridFlow: A Flexible and Efficient RLHF Framework G Sheng, C Zhang, Z Ye, X Wu, W Zhang, R Zhang, Y Peng, H Lin, C Wu arXiv preprint arXiv:2409.19256, 2024 | 10 | 2024 |
| Compressed Communication for Distributed Training: Adaptive Methods and System Y Zhong, C Xie, S Zheng, H Lin arXiv preprint arXiv:2105.07829, 2021 | 9 | 2021 |
| Flux: Fast software-based communication overlap on gpus through kernel fusion LW Chang, W Bao, Q Hou, C Jiang, N Zheng, Y Zhong, X Zhang, Z Song, ... arXiv preprint arXiv:2406.06858, 2024 | 8 | 2024 |
| Deep graph library M Wang, L Yu, Q Gan, D Zheng, Y Gai, Z Ye, M Li, J Zhou, Q Huang, ... | 7 | 2018 |
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