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What is Cyberinfrastructure? The Computer Science Perspective Dr. Chaitan Baru Project Director, The Geosciences Network (GEON) Director, Science R&D, San Diego Supercomputer Center SACNAS, Sept 29-Oct 1, 2005, Denver, CO 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. Application of IT to problems in science and engineering…and in other areas “Comprehensive infrastructure”, i.e. hardware, software, and expertise (people) SACNAS, Sept 29-Oct 1, 2005, Denver, CO Cyberinfrastructure: What do we mean? • Technologies to bring remote resources together A broad, systemic, strategic conceptualization Components of Cyberinfrastructure (Web Services)-enabled science & engineering High-performance computing for modeling, simulation, data processing/mining Humans Individual & Group Interfaces & Visualization Instruments for observation and characterization. Global Connectivity Facilities for activation, manipulation and construction Collaboration Services http://www.communitytechnology. org/nsf_ci_report/ Physical World Knowledge management institutions for collection building and curation of data, information, literature, digital objects Source: Dan Atkins Implies global (international) system for collaboration SACNAS, Sept 29-Oct 1, 2005, Denver, CO Evolution of the Computational Infrastructure Investments in the US Source: Dr. Deborah Crawford Chair, NSF CyberInfrastructure Working Group (CIWG) Cyberinfrastructure Terascale GRID Term Coined ~ Metacomputing Telescience: Access to Remote Resources PACI Prior Computing Investments Supercomputer Centers | 1985 | | 1990 1995 • NPACI: National Partnership for Advanced Computational Infrastructure • NCSA: National Computatioal Science Alliance NSF Networking Mosaic - Web Browser TCS, DTF, ETF SDSC (San Diego Supercomputer Center); NCSA (National Center for Supercomputing Applications); PSC (Pittsburgh Supercomputer Center), CTC (Cornell Theory Center) | | | 2000 2005 2010 A timeline from the Computational Infrastructure Division of the US National Science Foundation SACNAS, Sept 29-Oct 1, 2005, Denver, CO Integrated Cyberinfrastructure System: Meeting the needs of multiple communities Applications Education and Training Discovery & Innovation Source: Dr. Deborah Crawford, Chair, NSF CyberInfrastructure Working Group • Environmental Science • High Energy Physics • Biomedical Informatics • Geoscience Development Tools & Libraries Domainspecific Cybertools (software) Shared Cybertools (software) Grid Services & Middleware Hardware SACNAS, Sept 29-Oct 1, 2005, Denver, CO Distributed Resources (computation, communication storage, etc.) Examples of NSF Cyberinfrastructure Projects • GriPhyN: Grid Physics Network • • NVO: National Virtual Observatory • • • Sharing of experimental data Central, persistent repository for data from shake-table and tsunami wave tank experiments GEON: Geosciences Network • • • Sharing human and mouse structural and functional brain imaging data between independent, remote research groups NEES: Network for Earthquake Engineering Simulations • • • Providing online access to digital sky surveys Integrating heterogeneous sky surveys BIRN: Biomedical Informatics Research Network (NIH) • • Sharing high-energy physics data from single, large data sources 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 SACNAS, Sept 29-Oct 1, 2005, Denver, CO Guiding Principles for CI Projects • Use IT state-of-the-art, and develop advanced IT where needed • • Employ open-architecture and standards-based approach, based on community standards • • IT works in close conjunction with science, to develop best practices, data sharing frameworks, useful and usable capabilities and tools Create the “science infrastructure” • • • • Use best practices, including commercial tools, while developing advanced technology in open source, and doing CS research An equal partnership • • E.g.use of Web services and/or Grid services based approach to accessing distributed resources The “two-tier” approach • • • to support the “day-to-day” conduct of science (e-science) 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… SACNAS, Sept 29-Oct 1, 2005, Denver, CO