MRI: Acquisition of a GPU-accelerated cluster for research, training and outreach

MRI：获取 GPU 加速集群用于研究、培训和推广

• 批准号: 2215734
• 负责人: Dukka KC
• 金额: $ 43.21万
• 依托单位: Michigan Technological University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2215734&HistoricalAwards=false
• 关键词: MRI; Acquisition; GPU; accelerated; cluster

This project will enable ground-breaking research at Michigan Technological University (Michigan Tech) by acquiring a high-performance computing cluster to be named DeepBlizzard. DeepBlizzard will accelerate scientific discoveries in basic research and technological innovations by addressing emergent and longer-term needs with broad societal impacts in multiple disciplines: chemistry, forestry, mathematics, physics, engineering (biomedical, mechanical, materials science), and computer science. DeepBlizzard will be utilized by over 125 users across 20 departments and 5 Colleges at Michigan Tech and by partners at North Carolina A&T University. DeepBlizzard will catalyze and accelerate research, enable dissemination of results, and expand opportunities for collaboration, thereby promoting the advancement of these diverse scientific domains. The project will also provide various training, teaching, and outreach activities to produce a highly trained and diverse technical workforce, including the next generation of scientists. Throughout its life, DeepBlizzard will serve as the epicenter of innovative research by enabling and supporting cross-disciplinary and collaborative research opportunities.The DeepBlizzard high-performance computing cluster is designed by a team of experts from Computer Science, Physics, Chemistry, and Biomedical Engineering in coordination with Information Technology (IT) professionals. The instrument architecture is based on graphical processing unit (GPU) based accelerators. DeepBlizzard is configured to meet three major requirements: high-performance deep learning and inference; high-performance single, double, and mixed-precision calculations; and the ability to execute codes using high levels of parallelism. These requirements map to the needs of ongoing and proposed computational research endeavors at Michigan Tech. In addition, several outreach and training activities – developed in partnership with Michigan Tech’s existing NSF Research Experience for Undergraduates (REU) site, NSF/NSA GenCyber Camp, and other programs involving K-12, undergraduate, and graduate student, and historically marginalized groups in STEM – will provide seamless integration of research activities with outreach.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.

该项目将通过收购名为DeepBlizzard的高性能计算集群，使密歇根理工大学(Michigan Tech)的突破性研究得以实现。DeepBlizzard将通过解决在化学、林业、数学、物理、工程(生物医学、机械、材料科学)和计算机科学等多个学科中产生广泛社会影响的紧急和长期需求，加快基础研究和技术创新的科学发现。DeepBlizzard将被密歇根理工大学20个系和5个学院的超过125名用户以及北卡罗来纳农工大学的合作伙伴使用。DeepBlizzard将催化和加速研究，使成果得以传播，并扩大合作机会，从而促进这些不同科学领域的进步。该项目还将提供各种培训、教学和外联活动，以培养一支训练有素的多样化技术队伍，包括下一代科学家。在DeepBlizzard的整个生命周期中，DeepBlizzard将通过支持和支持跨学科和协作研究机会，成为创新研究的中心。DeepBlizzard高性能计算集群由来自计算机科学、物理、化学和生物医学工程的专家团队与信息技术(IT)专业人员协调设计。仪器架构基于基于图形处理单元(GPU)的加速器。DeepBlizzard被配置为满足三个主要要求：高性能的深度学习和推理；高性能的单精度、双精度和混合精度计算；以及使用高水平并行性执行代码的能力。这些要求与密歇根理工学院正在进行的和拟议的计算研究工作的需求相对应。此外，与密歇根理工大学现有的NSF本科生研究经验(REU)网站、NSF/NSA GenCyber Camp以及其他涉及K-12、本科生和研究生以及STEM中历史上被边缘化的群体的项目合作开发的几项外展和培训活动将提供研究活动与户外活动的无缝结合。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。