MRI: Acquisition of an HPC System for Data-Driven Discovery in Computational Astrophysics, Biology, Chemistry, and Materials Science

MRI：获取 HPC 系统，用于计算天体物理学、生物学、化学和材料科学中的数据驱动发现

• 批准号: 1828187
• 负责人: Srinivas Aluru
• 金额: $ 369.93万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1828187&HistoricalAwards=false
• 关键词: MRI; Acquisition; HPC; System; Data

The project funds the purchase of a high-performance computing and storage system at the Georgia Institute of Technology. This computing instrument will support data-driven research in astrophysics, biosciences, computational chemistry, materials and manufacturing, and computational science. These projects contribute to national initiatives in big data, strategic computing, materials genome, and manufacturing partnership; and NSF supported observatories such as the gravitational wave observatory and the South Pole neutrino observatory. The system also serves as a springboard for developments of codes, software prototyping, and scalability studies prior to using national supercomputers. Advances made in computational methods and scientific software are disseminated in the form of open-source codes and data analysis portals. Over 33 faculty, 54 research scientists/postdocs, 195 graduate students, and 56 undergraduate students will immediately benefit from the instrument. In addition, the system provides training opportunity at all levels from undergraduate students to early career researchers, in important interdisciplinary areas of national need. A fifth of the system capacity is utilized to enable research activities of regional partners, researchers from minority serving institutions, and other users nationally through XSEDE participation. The project involves undergraduate student participation from historically black colleges from Atlanta metropolitan area. Public outreach efforts are planned through videos of public interest and local events such as the Atlanta Science Festival.The cluster will combine regular compute nodes with others configured to emphasize one of the following: big memory, big local storage, solid state storage, Graphics Processing Units (GPU), and ARM processors. In doing so, the system can be employed by a diversity of projects. In astrophysics, the instrument bolsters data-driven research including detection of gravitational waves, astrophysical neutrinos, and gamma rays. It does it by leveraging data from leading astroparticle observatories and contributing to their mission. It also leads to improved insights into formation of supermassive black holes and large-scale structure of the universe. The computing system also aids the development of parallel software in computational genomics, systems biology, and health analytics. Important applications in assembly and network analysis of plant genomes, and environmental metagenomics are pursued. The instrument also enables next generation algorithms and software for computational chemistry and expands the boundaries of molecular simulation. The system enables advances in density function theory, enhances studies of crystal defects and nanostructures, and injects novel use of machine learning techniques in computational chemistry. It also fosters the development of data science methodologies to identify building blocks of materials at multiple scales, thus significantly reducing the development and deployments cycles for new materials.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.

该项目资助佐治亚理工学院购买高性能计算和存储系统。该计算仪器将支持天体物理学、生物科学、计算化学、材料和制造以及计算科学领域的数据驱动研究。这些项目为大数据、战略计算、材料基因组和制造伙伴关系方面的国家举措做出了贡献；美国国家科学基金会支持引力波天文台和南极中微子天文台等天文台。该系统还可以作为使用国家超级计算机之前代码开发、软件原型设计和可扩展性研究的跳板。计算方法和科学软件方面取得的进步以开源代码和数据分析门户的形式传播。超过 33 名教员、54 名研究科学家/博士后、195 名研究生和 56 名本科生将立即从该仪器中受益。此外，该系统还在国家需要的重要跨学科领域提供从本科生到早期职业研究人员的各个级别的培训机会。系统容量的五分之一用于支持区域合作伙伴、少数族裔服务机构的研究人员以及通过 XSEDE 参与的全国其他用户的研究活动。该项目涉及来自亚特兰大都会区历史悠久的黑人大学的本科生的参与。通过公众感兴趣的视频和亚特兰大科学节等当地活动来规划公共宣传工作。该集群将常规计算节点与其他配置节点结合起来，以强调以下之一：大内存、大本地存储、固态存储、图形处理单元 (GPU) 和 ARM 处理器。这样，该系统就可以被多种项目所采用。在天体物理学中，该仪器支持数据驱动的研究，包括引力波、天体物理中微子和伽马射线的探测。它通过利用领先天体粒子观测站的数据并为其任务做出贡献来实现这一目标。它还可以提高人们对超大质量黑洞形成和宇宙大尺度结构的认识。该计算系统还有助于计算基因组学、系统生物学和健康分析领域并行软件的开发。寻求在植物基因组组装和网络分析以及环境宏基因组学中的重要应用。该仪器还支持下一代计算化学算法和软件，并扩展了分子模拟的边界。该系统推动了密度函数理论的进步，增强了对晶体缺陷和纳米结构的研究，并在计算化学中注入了机器学习技术的新用途。它还促进了数据科学方法的发展，以识别多种尺度的材料构建模块，从而显着缩短新材料的开发和部署周期。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力评估进行评估，被认为值得支持。优点和更广泛的影响审查标准。

项目成果

Parallel construction of module networks

模块网络的并行构建

• DOI: 10.1145/3458817.3476207
• 发表时间: 2021
• 期刊: Storage and Analysis (SC
• 作者: Srivastava, Ankit;Chockalingam, Sriram P.;Aluru, Maneesha;Aluru, Srinivas
• 通讯作者: Aluru, Srinivas

Halo Environment for Population III Star Formation

III族恒星形成的光环环境

• DOI: 10.3847/2515-5172/ab9e78
• 发表时间: 2020
• 期刊: Research Notes of the AAS
• 作者: Grace, Justin;O’Shea, Brian W.;Wise, John H.
• 通讯作者: Wise, John H.

Decoding defect statistics from diffractograms via machine learning

• DOI: 10.1038/s41524-021-00539-z
• 发表时间: 2021-05-17
• 期刊: NPJ COMPUTATIONAL MATERIALS
• 影响因子: 9.7
• 作者: Kunka, Cody;Shanker, Apaar;Dingreville, Remi
• 通讯作者: Dingreville, Remi

Cartesian message passing neural networks for directional properties: Fast and transferable atomic multipoles

• DOI: 10.1063/5.0050444
• 发表时间: 2021-06-14
• 期刊: JOURNAL OF CHEMICAL PHYSICS
• 影响因子: 4.4
• 作者: Glick, Zachary L.;Koutsoukas, Alexios;Sherrill, C. David
• 通讯作者: Sherrill, C. David

Mechanism of action of HBV capsid assembly modulators predicted from binding to early assembly intermediates

通过与早期组装中间体的结合预测 HBV 衣壳组装调节剂的作用机制

• DOI: 10.1101/2020.03.23.002527
• 发表时间: 2020
• 期刊: NA
• 作者: Pavlova, Anna;Bassit, Leda;Cox, Bryan;Korablyov, Maksym;Chipot, Chris;Verma, Kiran;Russell, Olivia;Schinazi, Raymond;Gumbart, James C
• 通讯作者: Gumbart, James C