MRI-R2: Acquisition of Data Intensive Academic Grid (DIAG)

该奖项是根据2009年《美国复苏和再投资法案》(公法111-5)提供资金的。在NSF MRI-R2计划的支持下，巴尔的摩马里兰大学得以构建数据密集型学术网格(DIAG)，其中包括用于高通量计算分析的100个节点和用于高性能计算分析的5个节点。该资源将通过从高级海洋微生物生态研究和分析社区网络基础设施(CAMERA)和国家生物技术信息中心(CAMERA)等公共数据库中挖掘数据来优化数据集，并将利用为TeraGrid和Open Science Grid等现有网格资源开发的技术。生物信息学社区将使用Ergatis访问DIAG，Ergatis是一个基于网络的管道创建和管理工具，面向生物信息学的虚拟机器，以及使用Nimbus和开放科学网格的虚拟数据工具包等技术的交互式和程序性访问。DIAG网站生产的软件将很容易被Globus Workspace项目、开放科学网格项目和其他涉及大型多机构合作的项目使用，并为位于伊利诺伊州和加利福尼亚州的另外两个科学网格提供溢出能力。DIAG的发展将使下一代生物学家的培训成为可能，方法是在15个不同的课堂环境中为本科生、研究生和研究生带来强大的新分析系统，并将系统时间分配给少数族裔占主导地位的大学，极大地增强这些群体可用的计算和计算基础设施。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). Support from the NSF MRI-R2 program allowed the University of Maryland at Baltimore to build the Data Intensive Academic Grid (DIAG) that includes 100 nodes for high-throughput computational analysis and 5 nodes for high-performance computational analysis. This resource will optimize data sets generated by mining the data from public data repositories like Community Cyberinfrastructure for Advanced Marine Microbial Ecology Research and Analysis (CAMERA) and the National Center for Biotechnology Information and will leverage technologies developed for existing grid resources such as the TeraGrid, and Open Science Grid. The bioinformatics community will access the DIAG using Ergatis, a web based pipeline creation and management tool, bioinformatics oriented Virtual Machines, as well as interactive and programmatic access using technologies such as Nimbus and the Virtual Data Toolkit from the Open Science Grid. The software produced at the DIAG site will be easily utilized by projects such as the Globus Workspaces Project, Open Science Grid and other projects involving large multi-institutional collaborations, as well as providing spillover capacity for two other science grids, located in Illinois and California. The DIAG development will enable training of the next generation of biologists, by bringing a powerful new analysis system to undergraduate, graduate, and post-graduate students in 15 different classroom settings, and the allocation of system time to universities with predominantly under-represented minorities, greatly enhancing the available computation and computing infrastructure for these groups.