Collaborative Research: Frameworks: hpcGPT: Enhancing Computing Center User Support with HPC-enriched Generative AI

协作研究：框架：hpcGPT：通过 HPC 丰富的生成式 AI 增强计算中心用户支持

• 批准号: 2411294
• 负责人: Zhao Zhang
• 金额: $ 119.91万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2411294&HistoricalAwards=false
• 关键词: Collaborative; Research; Frameworks; hpcGPT; Enhancing

hpcGPT is a question answering service for academic computing centers such as the National Center for Supercomputing Applications, Ohio Supercomputer Center, San Diego Supercomputer Center, and Texas Advanced Computing Center. These Centers provide high-performance computing (HPC) platforms to tens of thousands of users for science and engineering research. In collaboration with Princeton University and Rutgers University, hpcGPT uses generative artificial intelligence (AI) and integrates heterogeneous data sources with different update frequencies to enhance the user support service quality and efficiency, decrease the response time, and improve precision of the support. With hpcGPT, user support teams can leverage the historical knowledge, real-time system status, and external technical expertise to better support the HPC users. With the high-quality and timely answers from hpcGPT, HPC users can resolve many technical issues, thus reducing the workload of the user support teams. This will allow the support teams to focus more on new and novel support issues. hpcGPT will significantly enhance the user support service quality, capacity, and efficiency without increasing the human effort.hpcGPT combines the fine-tuning and Retrieval Augmented Generation (RAG) techniques to incorporate recent knowledge, past experience, domain expertise, documentations, and real-time system status of versatile computing. By building upon existing and recognized capabilities in large language model fine-tuning and hosting, retrieval augmentation generation, and external data source integration, hpcGPT reduces the complexity and effort required to align information and identify dependencies between questions, answers, and the supporting information. This is particularly beneficial for research groups and computing centers with diverse application requirements and limited staff. hpcGPT extends and translates a suite of Cyberinfrastructure building blocks and technologies such as large language model training and inference service hosting.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

HPCGPT是用于学术计算中心的问答服务，例如国家超级计算应用中心，俄亥俄州超级计算机中心，圣地亚哥超级计算机中心和德克萨斯州高级计算中心。这些中心为成千上万的科学和工程研究提供了高性能计算（HPC）平台。与普林斯顿大学和罗格斯大学合作，HPCGPT使用生成人工智能（AI），并将异构数据源与不同的更新频率集成在一起，以提高用户支持服务质量和效率，降低响应时间，并提高支持精度。使用HPCGPT，用户支持团队可以利用历史知识，实时系统状态和外部技术专长来更好地支持HPC用户。通过HPCGPT的高质量和及时答案，HPC用户可以解决许多技术问题，从而减少用户支持团队的工作量。这将使支持团队更多地专注于新的和新颖的支持问题。 HPCGPT将显着提高用户支持服务质量，能力和效率，而不会增加人类的努力。HPCGPT结合了微调和检索增强发电（RAG）技术，以结合最新的知识，过去的经验，域专业知识，文档，文档，文档，文档和实时系统状态。通过在大型语言模型中的现有和公认的功能进行微调和托管，检索增强生成以及外部数据源集成基础上，HPCGPT降低了与问题，答案和支持信息之间的依赖关系相结合并识别依赖性所需的复杂性和努力。这对于具有不同应用要求和人员有限的研究小组和计算中心特别有益。 HPCGPT扩展并翻译了一套网络基础设施的构建块和技术，例如大型语言模型培训和推理服务主机。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来进行评估的。