SACNAS, Sept 29-Oct 1, 2005, Denver, CO 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 21 st Century Discovery,” NSF Cyberinfrastructure Council, September 26 th, 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 togetherTechnologies to bring remote resources together

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) Supercomputer Centers PACI Terascale | | | | | | NPACI : National Partnership for Advanced Computational Infrastructure NCSA : National Computatioal Science Alliance SDSC (San Diego Supercomputer Center); NCSA (National Center for Supercomputing Applications); PSC (Pittsburgh Supercomputer Center), CTC (Cornell Theory Center) TCS, DTF, ETF Cyberinfrastructure Prior Computing Investments NSF Networking Mosaic - Web Browser GRID Term Coined ~ Metacomputing A timeline from the Computational Infrastructure Division of the US National Science Foundation Telescience: Access to Remote Resources

SACNAS, Sept 29-Oct 1, 2005, Denver, CO Hardware Integrated Cyberinfrastructure System: Meeting the needs of multiple communities Source: Dr. Deborah Crawford, Chair, NSF CyberInfrastructure Working Group Grid Services & Middleware Development Tools & Libraries Applications Environmental Science High Energy Physics Biomedical Informatics Geoscience Domain- specific Cybertools (software) Shared Cybertools (software) Distributed Resources (computation, communication storage, etc.) Education and Training Discovery & Innovation

SACNAS, Sept 29-Oct 1, 2005, Denver, CO 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

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 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…