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Cyberinfrastructure From Dreams to Reality

Cyberinfrastructure From Dreams to Reality. Deborah L. Crawford Deputy Assistant Director of NSF for Computer & Information Science & Engineering. Workshop for eInfrastructures Rome, December 9, 2003. Daniel E. Atkins, Chair, University of Michigan

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Cyberinfrastructure From Dreams to Reality

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  1. CyberinfrastructureFrom Dreams to Reality Deborah L. Crawford Deputy Assistant Director of NSF forComputer & Information Science & Engineering Workshop for eInfrastructuresRome, December 9, 2003

  2. Daniel E. Atkins, Chair, University of Michigan Kelvin K. Droegemeier, University of Oklahoma Stuart I. Feldman, IBM Hector Garcia-Molina, Stanford University Michael L. Klein, University of Pennsylvania David G. Messerschmitt, University of California at Berkeley Paul Messina, California Institute of Technology Jeremiah P. Ostriker, Princeton University Margaret H. Wright,New York University http://www.communitytechnology.org/nsf_ci_report/

  3. Setting the Stage In summary then, the opportunity is here to create cyberinfrastructure that enables more ubiquitous, comprehensive knowledge environments that become functionally complete .. in terms of people, data, information, tools, and instruments and that include unprecedented capacity for computational, storage, and communication. They can serve individuals, teams and organizations in ways that revolutionize what they do, how they do it, and who can participate. - The Atkins Report

  4. Desired Characteristics • Science- and engineering-driven • Enabling discovery, learning and innovation • Promising economies of scale and scope • Supporting data-, instrumentation-, compute- and collaboration-intensive applications • High-end to desktop • Heterogeneous • Interoperable-enabled by collection of reusable, common building blocks

  5. Science Gateways CI Commons Distributed Resources Integrated Cyberinfrastructuremeeting the needs of a community of communities • Applications • Environmental Science • High Energy Physics • Proteomics/Genomics • Learning Discovery, Learning & Innovation Science of CI CI Services & Middleware Training & Workforce Development Hardware

  6. Overarching Principles • Enrich the portfolio • Demonstrate transformative power of CI across S&E enterprise • Empower range of CI users – current and emerging • System-wide evaluation and CI-enabling research informs progress • Develop intellectual capital • Catalyze community development and support • Enable training and professional development • Broaden participation in the CI enterprise • Enable integration and interoperability • Develop shared vision, integrating architectures, common investments • Promote collaboration, coordination and communication across fields • Share promising technologies, practices and lessons learned

  7. CI Planning - A Systems Approach • Domain-specific strategic plans • Technology/human capital roadmaps • Gaps and barrier analyses (policy, • funding, ..) S&E Gateways CI-enabling Research Integrative CI “system of systems” Core Activities CI Commons - Compute-centric - Information-intensive - Instrumentation-enabling - Interactive-intensive • System-wide activities • Education, training • (Inter)national networks • Capacity computing • Science of CI

  8. Baselining NSF CI Investments • Core (examples) • Protein Databank • Network for Earthquake Engineering Simulation • International Integrated Microdata Access System • Partnerships, Advanced Computational Infrastructure • Circumarctic Environmental Observatory Network • National Science Digital Library • Pacific Rim GRID Middleware • Priority Areas (examples) • Geosciences Network • international Virtual Data Grid Laboratory • Grid Research and Applications Development • .. and others too numerous to mention (~$400M in FY’04)

  9. CI Building Blocks Partnerships for Advanced Computational Infrastructure (PACI) • Science Gateways (Alpha projects, Expeditions) • Middleware Technologies (NPACKage, ATG, Access Grid in a Box, OSCAR ) • Computational Infrastructure NSF Middleware Initiative (NMI) • Production software releases • GridsCenter Software Suite, etc. Early Adopters • Grid Physics Network (GriPhyN), international Virtual Data Grid Laboratory (iVDGL) • National Virtual Observatory • Network for Earthquake Engineering Simulation (NEES) • Bio-Informatics Research Network (BIRN) Extensible Terascale Facility (TERAGRID) • Science Gateways (value-added of integrated system approach) • Common Teragrid Software Stack (CTSS) • Compute engines, Data, Instruments, Visualization

  10. Extensible Terascale Facility (TERAGRID)A CI Pathfinder • Pathfinder Role • integrated with extant CI capabilities • clear value-added • supporting a new class of S&E applications • Deploy a balanced, distributed system • not a “distributed computer” but rather • a distributed “system” using Grid technologies • computing and data management • visualization and scientific application analysis • remote instrumentation access • Define an open and extensible infrastructure • an “enabling cyberinfrastructure” demonstration • extensible beyond original sites with additional funding • NCSA, SDSC, ANL, Caltech and PSC • ORNL, TACC, Indiana University, Purdue University and Atlanta hub

  11. Resource Providers + 4 New Sites Caltech: Data collection analysis ANL: Visualization LEGEND Visualization Cluster Cluster IA64 IA32 0.4 TF IA-64 IA32 Datawulf 80 TB Storage 1.25 TF IA-64 96 Viz nodes 20 TB Storage IA64 Storage Server Shared Memory IA32 IA32 Disk Storage Backplane Router Extensible Backplane Network LA Hub Chicago Hub 30 Gb/s 40 Gb/s 30 Gb/s 30 Gb/s 30 Gb/s 30 Gb/s 6 TF EV68 71 TB Storage 0.3 TF EV7 shared-memory 150 TB Storage Server 10 TF IA-64 128 large memory nodes 230 TB Disk Storage 3 PB Tape Storage GPFS and data mining 4 TF IA-64 DB2, Oracle Servers 500 TB Disk Storage 6 PB Tape Storage 1.1 TF Power4 EV7 IA64 Sun EV68 IA64 Pwr4 Sun SDSC: Data Intensive NCSA: Compute Intensive PSC: Compute Intensive

  12. Linux Operating Environment Basic and Core Globus Services GSI (Grid Security Infrastructure) GSI-enabled SSH and GSIFTP GRAM (Grid Resource Allocation & Management) GridFTP Information Service Distributed accounting MPICH-G2 Science Portals Advanced and Data Services Replica Management Tools GRAM-2 (GRAM extensions) CAS (Community Authorization Service) Condor-G (as brokering “super scheduler”) SDSC SRB (Storage Resource Broker) APST user middleware, etc. Common Teragrid Software Stack (CTSS)

  13. TERAGRID as a Pathfinder • Science Drivers - Gateways • On-demand computing • Remote visual steering • Data-intensive computing • Systems Integrator/Manager • Common TERAGRID Software Stack • User training & services • TERAGRID Operations Center • Resource Providers • Data resources, compute engines, viz, • user services

  14. Focus on Policy and Social Dynamics • Policy issues must be considered up front • Social engineering will be at least as important as software engineering • Well-defined interfaces will be critical for successful software development • Application communities will need to participate from the beginning Fran Berman, SDSC

  15. CI Building Blocks Partnerships for Advanced Computational Infrastructure (PACI) • Science Gateways (Alpha projects, Expeditions) • Middleware Technologies (NPACKage, ATG, Access Grid in a Box, OSCAR ) • Computational Infrastructure NSF Middleware Initiative (NMI) • Production software releases • GridsCenter Software Suite, etc. Early Adopters • Grid Physics Network (GriPhyN), international Virtual Data Grid Laboratory (iVDGL) • National Virtual Observatory • Network for Earthquake Engineering Simulation (NEES) • Bio-Informatics Research Network (BIRN) Extensible Terascale Facility (TERAGRID) • Science Gateways (value-added of integrated system approach) • Common Teragrid Software Stack (CTSS) • Compute engines, Data, Instruments, Visualization

  16. CI Commons Goals • Commercial-grade software – stable, well-supported and well-documented • User surveys and focus groups inform priority-setting • Development of “Commons roadmap” Unanswered questions • What role does industry play in development and support of products • In what timeframe will software and services be available • How will customer satisfaction be assessed and by whom • What role do standards play – and does an effective standards process exist today

  17. CI CommonsCommunity Development Approach • End-user communities willing and able to modify code • Adds features, repairs defects, improves code • Customizes common building blocks for domain applications • Leads to higher quality code, enhances diversity • Natural way to set priorities Requires • Education, training in community development methodologies • Effective Commons governance plan • Strong, sustained interaction between Commons developers and community code enhancers

  18. Challenging Context • Cyberinfrastructure Ecology • Technological change more rapid than institutional change • Disruptive technology promises unforeseen opportunity • Seamless Integration of New and Old • Balancing upgrades of existing and creation of new resources • Legacy instruments, models, data, methodologies • Broadening Participation • Community-Building • Requires Effective Migration Strategy

  19. On-Demand: Severe Weather Forecasting Several times a week, need multiple hours dedicated access to amulti-Teraflops system. Kelvin Droegemeier, Center for Analysis and Prediction of Storms (CAPS) University of Oklahoma

  20. On Demand: Brain Data Grid Objective: Form a National Scale Testbed for Federating Large Databases Using NIH High Field NMR Centers Stanford U. Of MN NCRR Imaging and Computing Resources UCSD Harvard Cal Tech Surface Web SDSC Cal-(IT)2 Deep Web UCLA Duke Cyberinfrastructure Linking Tele-instrumentation, Data Intensive Computing, and Multi-scale Brain Databases. Wireless “Pad” Web Interface Mark Ellisman, Larry Smarr, UCSD

  21. membrane potential (s) • bath concentration (s) – Inside / Outside • bath diffusion constant • channel diffusion constant • time step-size • number of time steps • channel diameter • channel length • force profile Molecular Dynamics • protein/lipid 3-d struct coord and topology • force field sets • ion-water ratio • ion type/initial positions • simulation time step-size • simulation methodology specifications ** Related by sampling method used for calculation of diffusion constant in MD simulations Brownian Dynamics Hole Profile analysis Hole Analysis • ion trajectory • ion type • temperature • ionic strength • protein dielectric • water dielectric • protein 3-d structure coordinates • technical specifications * • partial charges of titratable residues • protein 3-d structure coordinates • one position in channel • approximate channel direction • technical specifications*** Electrostatics - II • temperature • ionic strength • protein dielectric • water dielectric • protein 3-d struct coord • technical specifications * Electrostatics - I • pH of bath • interaction potentials between titratable groups in protein Molecular Biology Simulation User Web Portal Workflow Manager Data Globus Client TeraGrid Resources Eric Jakobsson, UIUC

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