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What is Cyberinfrastructure? The Computer Science Perspective

What is Cyberinfrastructure? The Computer Science Perspective. Dr. Chaitan Baru Project Director, The Geosciences Network (GEON) Director, Science R&D, San Diego Supercomputer Center.  Application of IT to problems in science and engineering…and in other areas

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What is Cyberinfrastructure? The Computer Science Perspective

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  1. What is Cyberinfrastructure?The Computer Science Perspective Dr. Chaitan Baru Project Director, The Geosciences Network (GEON) Director, Science R&D, San Diego Supercomputer Center

  2.  Application of IT to problems in science and engineering…and in other areas  “Comprehensive infrastructure”, i.e. hardware, software, and expertise (people) Cyberinfrastructure: A Definition “The comprehensive infrastructure needed to capitalize on dramatic advances in information technology has been termed cyberinfrastructure.” From “NSF’S Cyberinfrastructure Vision for 21st Century Discovery,” NSF Cyberinfrastructure Council, September 26th, 2005, Ver.4.0, pg 4.

  3. Cyberinfrastructure: What do we mean? • Technologies to bring remote resources together

  4. Evolution of the Computational Infrastructure Investments in the USSource: Dr. Deborah CrawfordChair, NSF CyberInfrastructureWorking Group (CIWG) Cyberinfrastructure TCS, DTF, ETF Terascale GRID Term Coined ~ Metacomputing • NPACI: National Partnership for Advanced Computational Infrastructure • NCSA: National Computatioal Science Alliance PACI Telescience: Access to Remote Resources NSF Networking Mosaic - Web Browser SDSC (San Diego Supercomputer Center); NCSA (National Center for Supercomputing Applications); PSC (Pittsburgh Supercomputer Center), CTC (Cornell Theory Center) Prior Computing Investments Supercomputer Centers | | | | | | 1985 1990 1995 2000 2005 2010 A timeline from the Computational Infrastructure Division of the US National Science Foundation

  5. Domain-specific Cybertools (software) Shared Cybertools (software) Distributed Resources (computation, communicationstorage, etc.) Integrated Cyberinfrastructure System: Meeting the needs of multiple communitiesSource: Dr. Deborah Crawford, Chair, NSF CyberInfrastructure Working Group • Applications • Environmental Science • High Energy Physics • Biomedical Informatics • Geoscience DevelopmentTools & Libraries Education and Training Discovery & Innovation Grid Services & Middleware Hardware

  6. Examples of NSF Cyberinfrastructure Projects • GriPhyN: Grid Physics Network • Sharing high-energy physics data from single, large data sources • NVO: National Virtual Observatory • Providing online access to digital sky surveys • Integrating heterogeneous sky surveys • BIRN: Biomedical Informatics Research Network (NIH) • Sharing human and mouse structural and functional brain imaging data between independent, remote research groups • NEES: Network for Earthquake Engineering Simulations • Sharing of experimental data • Central, persistent repository for data from shake-table and tsunami wave tank experiments • GEON: Geosciences Network • Integrating existing 4D multi-disciplinary data products • Extreme heterogeneity in data: discipline, scale, resolution, accuracy • SEEK: Science Environment for Ecological Knowledge • IT infrastructure to support ecological modeling • Access to distributed ecological data collections All require (on-demand) access to large computers, for modeling, data analysis, visualization and data assimilation

  7. Guiding Principles for CI Projects • Use IT state-of-the-art, and develop advanced IT where needed • to support the “day-to-day” conduct of science (e-science) • Employ open-architecture and standards-based approach, based on community standards • E.g.use of Web services and/or Grid services based approach to accessing distributed resources •  The “two-tier” approach • Use best practices, including commercial tools, • while developing advanced technology in open source, and doing CS research • An equal partnership • IT works in close conjunction with science, to develop best practices, data sharing frameworks, useful and usable capabilities and tools • Create the “science infrastructure” • Integrated online databases with advanced search engines • Online science models • Robust tools and applications, etc. • Leverage other intersecting projects • Much commonality in the technologies, regardless of science disciplines • Constantly work towards eliminating (or, at least, minimizing) the “NIH” syndrome • And, importantly, try not to reinvent what industry already knows how to do…

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