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检索条件"机构=National Laboratory for Parallel and Distributed Computing"
172 条 记 录,以下是1-10 订阅
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Automatic parallelism strategy generation with minimalmemory redundancy
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Frontiers of Information Technology & Electronic Engineering 2025年 第1期26卷 109-118页
作者: Yanqi SHI Peng LIANG Hao ZHENG Linbo QIAO Dongsheng LI National Key Laboratory of Parallel and Distributed Computing National University of Defense TechnologyChangsha 410000China
Large-scale deep learning models are trained distributedly due to memory and computing resource *** existing strategy generation approaches take optimal memory minimization as the *** fill in this gap,we propose a nov... 详细信息
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Training large-scale language models with limited GPU memory:a survey
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Frontiers of Information Technology & Electronic Engineering 2025年 第3期26卷 309-331页
作者: Yu TANG Linbo QIAO Lujia YIN Peng LIANG Ao SHEN Zhilin YANG Lizhi ZHANG Dongsheng LI National Key Laboratory of Parallel and Distributed Computing College of ComputerNational University of Defense TechnologyChangsha 410073China
Large-scale models have gained significant attention in a wide range of fields,such as computer vision and natural language processing,due to their effectiveness across various ***,a notable hurdle in training these l... 详细信息
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Optimizing Fine-Tuning in Quantized Language Models:An In-Depth Analysis of Key Variables
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Computers, Materials & Continua 2025年 第1期82卷 307-325页
作者: Ao Shen Zhiquan Lai Dongsheng Li Xiaoyu Hu National Key Laboratory of Parallel and Distributed Computing National University of Defense TechnologyChangsha410073China Strategic Assessments and Consultation Institute Academy of Military ScienceBeijing100091China
Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning *** this approach allows models to specialize in specific tasks w... 详细信息
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Exploring Quantization Techniques for Large-Scale Language Models: Methods, Challenges and Future Directions  24
Exploring Quantization Techniques for Large-Scale Language M...
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9th International Conference on Cyber Security and Information Engineering, ICCSIE 2024
作者: Shen, Ao Lai, Zhiquan Li, Dongsheng National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology China
Breakthroughs in natural language processing (NLP) by large-scale language models (LLMs) have led to superior performance in multilingual tasks such as translation, summarization, and Q&A. However, the size and co... 详细信息
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U-shaped Dual Attention Transformer: An Efficient Transformer Based on Channel and Spatial Attention  4
U-shaped Dual Attention Transformer: An Efficient Transforme...
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4th International Conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024
作者: Zhai, Zhaoyuan Qiao, Peng Li, Rongchun Zhou, Zhen National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China
Transformer-based methods have demonstrated remarkable performance on image super-resolution tasks. Due to high computational complexity, researchers have been working to achieve a balance between computation costs an... 详细信息
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Funnel: An Efficient Sparse Attention Accelerator with Multi-Dataflow Fusion  22
Funnel: An Efficient Sparse Attention Accelerator with Multi...
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22nd IEEE International Symposium on parallel and distributed Processing with Applications, ISPA 2024
作者: Ma, Shenghong Xu, Jinwei Jiang, Jingfei Wang, Yaohua Li, Dongsheng National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing College of Computer Changsha China
The self-attention mechanism is the core component of Transformer, which provides a powerful ability to understand the sequence context. However, the self-attention mechanism also suffers from a large amount of redund... 详细信息
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Mbapp: Efficient Memory-Balanced Pipeline parallelism for Large Model Fine-Tuning on Commodity GPU Servers  24
Mbapp: Efficient Memory-Balanced Pipeline Parallelism for La...
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5th International Conference on Computer Information and Big Data Applications, CIBDA 2024
作者: Liu, Yujie Lai, Zhiquan Li, Dongsheng National Key Laboratory of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha410000 China
Large-scale models have demonstrated outstanding performance across various downstream tasks. Pipeline parallelism is essential for fine-tuning large models on commodity GPU servers, as it plays a crucial role in maki... 详细信息
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HAF: a hybrid annotation framework based on expert knowledge and learning technique
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Science China(Information Sciences) 2022年 第1期65卷 276-278页
作者: Zhixing LI Yue YU Tao WANG Gang YIN Xinjun MAO Huaimin WANG Key Laboratory of Parallel and Distributed Computing National University of Defense Technology College of Computer National University of Defense Technology
Dear editor,The increasing awareness of the potential value hidden in data has resulted in many data mining studies being conducted. In the domain of software engineering, for example, developers' behavioral data ...
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Communication Analysis for Multidimensional parallel Training of Large-scale DNN Models  25
Communication Analysis for Multidimensional Parallel Trainin...
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25th IEEE International Conferences on High Performance computing and Communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023
作者: Lai, Zhiquan Hao, Yanqi Li, Shengwei Li, Dongsheng College of Computer National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China
Multidimensional parallel training has been widely applied to train large-scale deep learning models like GPT-3. The efficiency of parameter communication among training devices/processes is often the performance bott... 详细信息
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Efficient Large Models Fine-tuning on Commodity Servers via Memory-balanced Pipeline parallelism  25
Efficient Large Models Fine-tuning on Commodity Servers via ...
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25th IEEE International Conferences on High Performance computing and Communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023
作者: Liu, Yujie Lai, Zhiquan Liu, Weijie Wang, Wei Li, Dongsheng College of Computer National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China
Large models have achieved impressive performance in many downstream tasks. Using pipeline parallelism to fine-tune large models on commodity GPU servers is an important way to make the excellent performance of large ... 详细信息
来源: 评论