CRI: A Western New York Computational and Data Science Grid

CRI：纽约西部计算和数据科学网格

• 批准号: 0454114
• 负责人: Russ Miller
• 金额: --
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-15 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0454114&HistoricalAwards=false
• 关键词: CRI; Western; New; York; Computational

AbstractProgram: NSF 04-588 CISE Computing Research InfrastructureTitle: CRI: Developing the Lemur Toolkit into a Community Resource Proposal: CNS 0454114PI: Russ MillerInstitution: SUNY at Buffalo This project builds on the Western New York Bio-informatics and Life Sciences initiative headquartered in Buffalo, NY. Partners include SUNY-Buffalo, Niagara University, and SUNY-Geneseo. Collaborators include the Roswell Park Cancer Institute, the Center for Computational Research at SUNY-Buffalo, and the Hauptman-Woodward Medical Research Institute The project will focus on the design, analysis and implementation of a computational and data grid serving the research, teaching, education and outreach needs of computer science, technology, bioinformatics, and life sciences in western New York. Research will include enabling technologies for lightweight, efficient monitoring of a the grid, methods to allow subsets of processors to seamlessly join and leave the grid, data collection and mining to support predictive scheduling, improve backfill usage, and providing a ubiquitous interface to users. Applications supported include bioinformatics, structural biology and life sciences. Broader impacts include outreach activities to the K-16 programs, a certificate program in high-performance computing, and diverse recruiting.

摘要程序：NSF 04-588 CISE计算研究项目结构标题：CRI：将Lemur Toolkit开发为社区资源提案：CNS 0454114 PI：Russ Miller机构：纽约州立大学布法罗分校该项目建立在总部位于纽约州布法罗的纽约西部生物信息学和生命科学计划的基础上。合作伙伴包括纽约州立大学布法罗分校、尼亚加拉大学和纽约州立大学Geneseo。合作者包括Roswell Park癌症研究所，纽约州立大学布法罗分校计算研究中心和Hauptman-Woodward医学研究所。该项目将重点关注计算和数据网格的设计，分析和实施，以满足纽约西部计算机科学，技术，生物信息学和生命科学的研究，教学，教育和推广需求。研究将包括使能技术的轻量级，有效的监测网格，方法，让处理器的子集无缝加入和离开网格，数据收集和挖掘，以支持预测调度，提高回填使用，并提供一个无处不在的界面给用户。支持的应用包括生物信息学、结构生物学和生命科学。更广泛的影响包括K-16计划的推广活动，高性能计算的证书计划和多样化的招聘。