CRI: IAD Acquisition of a Cluster and High Performance Storage for data-intensive applications in Materials Science, Power Systems and Systems Biology

CRI：IAD 收购集群和高性能存储，用于材料科学、电力系统和系统生物学中的数据密集型应用

• 批准号: 0751157
• 负责人: Srinivas Aluru
• 金额: $ 71.9万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-03-15 至 2011-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0751157&HistoricalAwards=false
• 关键词: CRI; IAD; Acquisition; Cluster; Performance

This project supports the acquisition of a high performance cluster and storage system for data-intensive applications in materials science, electrical power systems and systems biology. In materials science, the investigators will develop methods to analyze data from the LEAP atom probe tomography, and find ways to understand material properties from the arrangement of its constitute atoms. This new instrumentation is increasingly being adopted by materials scientists, and the project will provide an archive for such data and develop analysis tools. In power systems, the researchers will investigate improving the security, reliability and efficiency of national energy infrastructure by utilizing high-performance computing for complex system analysis in energy control centers. In systems biology, the investigators will collectively analyze large-scale gene expression data from experiments conducted worldwide to build robust gene networks. These research efforts will lead to significant advances in the penetration of high performance computing technology in materials science, power systems research, and molecular biology. The project will bring together multiple departments and research centers at Iowa State University and further enrich interdisciplinary culture and training opportunities.A number of students will be trained in computation-centric interdisciplinary research.

该项目支持为材料科学、电力系统和系统生物学中的数据密集型应用购买高性能集群和存储系统。在材料科学方面，研究人员将开发分析LEAP原子探针断层扫描数据的方法，并找到从组成原子的排列中了解材料特性的方法。这种新的仪器越来越多地被材料科学家采用，该项目将为这些数据提供档案并开发分析工具。在电力系统方面，研究人员将通过利用高性能计算进行能源控制中心的复杂系统分析，研究如何提高国家能源基础设施的安全性、可靠性和效率。在系统生物学中，研究人员将共同分析来自全球实验的大规模基因表达数据，以建立强大的基因网络。这些研究工作将导致高性能计算技术在材料科学、电力系统研究和分子生物学中的渗透取得重大进展。该项目将汇集爱荷华州州立大学的多个部门和研究中心，进一步丰富跨学科文化和培训机会。