MRI Acquistion: High Performance Computing Cluster for Data Intensive Research

MRI 采集：用于数据密集型研究的高性能计算集群

• 批准号: 1920304
• 负责人: Gwen Jacobs
• 金额: $ 70万
• 依托单位: University of Hawaii
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1920304&HistoricalAwards=false
• 关键词: MRI; Acquistion; Performance; Computing; Cluster

This award to the University of Hawaii (UH) System results in the acquisition, deployment, and maintenance of a high-performance cluster to support computational and data intensive research for the UH campuses, supporting data science research that uses machine learning and artificial intelligence. Focusing on three multi-disciplinary research efforts (Data Science Institute (HI-DSI), Ike Wai: Securing Water Future for Hawaii (EPSCoR RII Track 1), and Center for Microbiome Analysis through Island Knowledge and Investigation (C-MAIKI)), the investigators aim to impact a broad range of research that primarily involves 15 pre-tenured faculty. The cluster will be managed as a shared resource servicing various goals that include: 1. Advance technical training, 2. Support for researchers who use Graphics Processing Units (GPUs) in their research, 3. Support High Throughput Computing (HTC) users, 4. Broadening the multi-campus user base through training activities and support, and 5. Expanding educational and research opportunities in the classroom and in hands-on immersive training workshops. The HI-DSI is diverse UH system-wide effort that serves as a nexus for expertise in data science research and education. This effort creates new knowledge important to the islands and the global community to improve national security and derive economic value from its research. This project provides the critical computational resources, and technical human infrastructure (a cadre of early career faculty from top tier institutions recently recruited) to ensure that these new investigators thrive in the university. The UH Information Technology Services-CyberInfrastructure (ITS-CI) will continue training and collaborate with HI-DSI to add new data science training modules and workshops. These activities will augment formal classroom education and support graduate and undergraduate research projects. The investigators will work to engage new user communities and help design training programs to meet the needs of the researchers. The cluster, managed as a shared campus resource free to all researchers, will contribute to keep researchers at the leading edge of their programs and better prepare students for careers in research and industry.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.

授予夏威夷大学（UH）系统的这一奖项导致了高性能集群的采购、部署和维护，以支持夏威夷大学校园的计算和数据密集型研究，支持使用机器的数据科学研究学习和人工智能。专注于三个多学科的研究工作（数据科学研究所（HI-DSI），艾克阿威：确保夏威夷的水资源未来（EPSCoR RII轨道1），并通过岛屿知识和调查（C-MAIKI）微生物组分析中心），调查人员的目标是影响广泛的研究，主要涉及15个预终身教职员工。集群将作为共享资源进行管理，为各种目标服务，包括：1.技术培训，2。支持在研究中使用图形处理单元（GPU）的研究人员，3。支持高吞吐量计算（HTC）用户，4.通过培训活动和支持扩大多校区用户群，以及5。在课堂上和动手沉浸式培训研讨会中扩大教育和研究机会。HI-DSI是UH全系统的多元化努力，是数据科学研究和教育专业知识的纽带。这一努力创造了对群岛和国际社会重要的新知识，以加强国家安全，并从其研究中获得经济价值。该项目提供了关键的计算资源和技术人力基础设施（最近招募的来自顶级机构的早期职业教师骨干），以确保这些新的研究人员在大学中茁壮成长。UH信息技术服务网络基础设施（ITS-CI）将继续培训并与HI-DSI合作，增加新的数据科学培训模块和研讨会。这些活动将加强正规的课堂教育，支持研究生和本科生的研究项目。研究人员将努力吸引新的用户社区，并帮助设计培训计划，以满足研究人员的需求。该集群作为一个共享的校园资源免费向所有研究人员管理，将有助于保持研究人员在其计划的领先优势，并更好地为学生在研究和行业的职业生涯做好准备。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Learning Intonation Pattern Embeddings for Arabic Dialect Identification

学习阿拉伯语方言识别的语调模式嵌入

• DOI: 10.21437/interspeech.2020-2906
• 发表时间: 2020
• 期刊: INTERSPEECH 2020
• 作者: Alvarez, Aitor Arronte;Issa, Elsayed Sabry
• 通讯作者: Issa, Elsayed Sabry

The Potential Energy Profile of the HBr + + HCl Bimolecular Collision

HBr HCl 双分子碰撞的势能分布

• DOI: 10.1021/acs.jpca.1c08300
• 发表时间: 2022
• 期刊: The Journal of Physical Chemistry A
• 作者: Fujioka, Kazuumi;Weitzel, Karl-Michael;Sun, Rui
• 通讯作者: Sun, Rui

Deep Sensing of Ocean Wave Heights with Synthetic Aperture Radar.

利用合成孔径雷达深度感知海浪高度。

• 发表时间: 2020
• 期刊: AAAI 2020 Spring Symposium
• 作者: Quach, B;Glaser, Y;Stopa, J;Sadowski, P
• 通讯作者: Sadowski, P

Deep Learning for Climate Models of the Atlantic Ocean

大西洋气候模型的深度学习

• 发表时间: 2020
• 作者: Nikolaev, A;Richter, I;Sadowski, P
• 通讯作者: Sadowski, P

Cosmic antihelium-3 nuclei sensitivity of the GAPS experiment

GAPS 实验的宇宙反氦 3 核敏感性

• DOI: 10.1016/j.astropartphys.2021.102580
• 发表时间: 2021
• 期刊: Astroparticle Physics
• 影响因子: 3.5
• 作者: Saffold, N.;Aramaki, T.;Bird, R.;Boezio, M.;Boggs, S.E.;Bonvicini, V.;Campana, D.;Craig, W.W.;von Doetinchem, P.;Everson, E.
• 通讯作者: Everson, E.