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1 NCSA 2015 Strategic Planning Process April 21, 2010 José L. Muñoz (Acting) Director, OCI (thanks to Blatecky, Parashar and Pennington) 1.

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Presentation on theme: "1 NCSA 2015 Strategic Planning Process April 21, 2010 José L. Muñoz (Acting) Director, OCI (thanks to Blatecky, Parashar and Pennington) 1."— Presentation transcript:

1 1 NCSA 2015 Strategic Planning Process April 21, 2010 José L. Muñoz (Acting) Director, OCI (thanks to Blatecky, Parashar and Pennington) 1

2 Outline  OCI  CF21  SOFTWARE  HPC 2

3 National Science Foundation National Science Board Director Deputy Director Computer & Information Sci & Eng ($633M) Engineering ($764M) Geo- Sciences ($909M) Mathematical & Physical Sciences ($1380M) Education & Human Resources Office of Budget, Finance & Award Management Biological Sciences ($733M) Office of Cyber- infrastructure ($219M) Office of Polar Programs Office of International Sci & Engr Office of Integrated Activities Social, Behavioral & Economic Sciences ($257M) Budgets presented are FY 2010 Request Office of Information Reserouce Management

4 4 OCI FY09 BUDGET BREAKDOWN $279M Includes ARRA

5 5 OCI BUDGET BREAKDOWN FY10: $219M

6 CyberInfrastructure Framework for 21 st Century Science and Engineering (CF 21) 6

7 7 Five Crises  Computing Technology  Multicore: processor is new transistor  Programming model, fault tolerance, etc  New models: clouds, grids, GPUs,… where appropriate  Data, provenance, and viz  Generating more data than in all of human history: preserve, mine, share?  How do we create “data scientists”?  Software  Complex applications on coupled compute- data-networked environments, tools needed  Modern apps: 10 6 + lines, many groups contribute, take decades

8 8 Five Crises con’t  Organization for Multidisciplinary Computational Science  “Universities must significantly change organizational structures: multidisciplinary & collaborative research are needed [for US] to remain competitive in global science”  “Itself a discipline, computational science advances all science…inadequate/outmoded structures within Federal government and the academy do not effectively support this critical multidisciplinary field”  Education  The CI environment is running away from us!  How do we develop a workforce to work effectively in this world?  How do we help universities transition?

9 What is Needed? An ecosystem, not components… 9 NSF-wide CI Framework for 21 st Century Science & Engineering People, Sustainability, Innovation, Integration

10 Expertise Research and Scholarship Education Learning and Workforce Development Interoperability and operations Cyberscience Discovery Collaboration Education Maintainability, sustainability, and extensibility Cyberinfrastructure Ecosystem Software Applications, middleware Software development and support Cybersecurity: access, authorization, authentication Networking Campus, national, international networks Research and experimental networks End-to-end throughput Cybersecurity Data Databases, Data repositories Collections and Libraries Data Access; storage, navigation management, mining tools, curation Organizations Universities, schools Government labs, agencies Research and Medical Centers Libraries, Museums Virtual Organizations Communities Computational Resources Supercomputers Clouds, Grids, Clusters Visualization Compute services Data Centers Scientific Instruments Large Facilities, MREFCs,telescopes Colliders, shake Tables Sensor Arrays - Ocean, environment, weather, buildings, climate. etc

11 CF21  A goal of Virtual Proximity – as though you are one with your resources  Continue to collapse the barrier of distance and remove geographic location as an issue  ALL resources (including people) are virtually present, accessible and secure Instruments, HPC, Vis, Data, Software, Expertise, VOs, etc 11 End-to-End Integrated Cyberinfrastructure Science, throughput and usefulness becomes the metric

12 12 Driving Forces  Need to support the efficient pursuit of S&E  Multi-domain, multi-disciplinary, multi-location  Leading edge CI network capabilities  Seamless integration  Need to connect Researcher to Resource  Access to major scientific resources and instruments  CI resource availability – at speed and in real-time (HPC, MREFC, Data Center, Vis center, Clouds, etc)  Campus environment including intra-campus  State, regional, national and international network and infrastructure transparency 12

13 CF21: Cyberinfrastructure Framework…  High-end computation, data, visualization, networks for transformative science  Facilities/centers as hubs of innovation  MREFCs and collaborations including large-scale NSF collaborative facilities, international partners  Software, tools, science applications, and VOs critical to science, integrally connected to instruments  Campuses fundamentally linked end-to-end; clouds, loosely coupled campus services, policy to support  People. Comprehensive approach workforce development for 21st century science and engineering 13

14 14 ACCI Task Forces Campus Bridging: Craig Stewart, IU (BIO) Computing: Thomas Zacharia, ORNL/UTK (DOE) Grand Challenge Communities/VOs: Tinsley Oden, Austin (ENG) Education & Workforce: Alex Ramirez, CEOSE Software: David Keyes, Columbia/KAUS T (MPS) Data & Viz: Shenda Baker, Harvey Mudd (MPS); Tony Hey, (CISE)  Completion by end of year  Advising NSF  Conducting Workshop(s)  Recommendations  Input to NSF informs CF21 programs, 2011-12 CI Vision Plan 14

15 CF21 Plan  Existing Task Forces Recommendations and input  Need to establish CF21 group at NSF  CI lead from each Directorate  Creation of the CF21 document is the goal  Early Draft by January 2011  CF21 Colloquium (C 2 ) – in process  Need to have a budget building exercise for CF21 for FY12 15

16 SOFTWARE 16

17 Software is Critical  CI – Unprecedented complexity, challenges  Software is essential to every aspect of CI – “the glue”  Drivers, middleware, runtime, programming systems/tools, applications, …  This software is different …. ?  In its natures, who builds it, how is it built, where it runs, its lifetime, etc.  Software crisis?  Software complexity is impeding the use of CI Science apps have 10 3 to 10 6+ lines, have bugs Developed over decades – long lifecycles (~35 years)  Software/systems design/engineering issues Emergent rather than by design  Quality of science in question

18 Software Grand Challenge  SW as the modality for CF21 and Computational Science in the 21st Century  Sustainable SW as a CI resource  What SW to sustain?  How to sustain it?  Fundamental Grand Challenge: Robust, Sustainable and Manageable Software at CI-Scale  Repeatability, Reliability, Performance, Usability, Energy efficiency, ….  Sustainability, manageability, etc., are NOT add- ons – it has to be integrated into the design

19 Many complex aspects….  Building the right software – application involvement, understanding requirements  scales, types of software, target user communities  Building software right – teams, reward structures, processes, metrics, verification/testing  Protecting investments – active management, sustainability, leverage/reuse, ownership, business models  Building trust – user community must be able to depend on the availability of a robust and reliable software infrastructure!

20 Scientific Software Elements (SSE): 1– 2 PIs $0.2 – 0.5M, 3 years Scientific Software Integration (SSI): Focused Groups ~$1 per year, 3 – 5 years Scientific Software Innovation Institutes (S2I2): Large Multidisciplinary Groups $6–8M per year, 5 (+) years Planning Activities FY 11 and beyond only Software Infrastructure for Sustained Innovations (SI 2 ) - Mechanisms  Create a software ecosystem that scales from individual or small groups of software innovators to large hubs of software excellence  3 interlocking levels of funding Focus on innovation Focus on sustainability

21 Sustained Long-Term Investment in Software  Transform innovations into sustainable software that is an integral part of a comprehensive cyberinfrastructure  robust, efficient, resilient, repeatable, manageable, sustainable, community-based, etc.  Catalyze and nurture multidisciplinary software as a symbiotic “process” with ongoing evolution  Domain and computational scientists, software technologists  Address all aspects, layers and phases of software  Systematic approaches  Theory validated by empirical trials  Tools that embody and support processes  Metrics, validation mechanisms, governance structures  Amortised over large (global) user communities  Support for maintenance and user support

22 Sustained Long-Term Investment in Software  Significant multiscale, long-term program  Envisions $200-300M over a decade  Connected institutes, teams, investigators  Integrated into CF21 framework 22 Many individuals w/short term grant Numerous teams of scientists and computational and computer scientists with longer term grants 3-6 centers, 5+5 years, for critical mass, sustainability

23 Sensor Nets Experiments/Instruments Data Archives (DataNet) X Infrastructure (XD) Visualization/Analytics

24 Software Infrastructure for Sustained Innovation (SI 2 ): Details  Letters of Intent (Required) – May 10, 2010  Title, Team, Synopsis (science/engr. drivers, target user community, specific software elements)  Full Proposals – June 14, 2010  SSE: ~2 PIs + 2 GAs, 3 years  SSI: ~3-4 PIs, 3-4 GAs, 1-2 senior personnel, 3-5 years  No S2I2 in FY 10  Note:  Proposal preparation instructions, supplementary document requirements  Additional review criteria  Please do read the solicitation!  Email questions to si2queries@nsf.gov

25 High Performance Computing 25

26 HPC Task Force General Questions  Access to advanced computing resources  2011-2015 time frame  Applications development and support  Development, maintenance and support  Computer science and engineering  Innovations to advance development and use  Integration of research and education  Pre-college through post-graduate  Training  Preparation of the scientific workforce

27 Gathering Community Input On:  How do we best address sustainability, user requirements in HPC?  Revisions to current acquisition model?  What is the proper balance between production and experimental systems?  How to build on TG and XD for integration, advanced services, in the future: CF21  Alternate models of computing:  Clouds, grids, etc  Commercial providers? Pay per service?  Exascale and beyond  Already jointly sponsoring workshops with DOE on advanced software for exascale  How to advance the applications community for this?  Partnerships with DOE, other agencies moving forward

28 Workshop #1, Dec 2010  Workshop held in Arlington, VA  Position papers and report available at:  http://www.nics.tennessee.edu/workshop http://www.nics.tennessee.edu/workshop  Resultant set of recommendations being used to help shape programs and longer term strategy  Follow on workshops on Applications and Software  Two major recommendations

29 Recommendations  By 2015–2016, academic researchers should have access to a rich mix of HPC systems that:  deliver sustained performance of 20–100 petaflops on a broad range of science and engineering codes;  are integrated into a comprehensive, national cyberinfrastructure environment; and  are supported at national, regional, and/or campus levels.

30 Recommendations  To sustain and promote the stability of resources, NSF should direct the evolution of its supercomputing program in a sustainable way, allowing researchers and HPC centers to select the best value in computational and data platforms and enabling centers to offer continuous service to the community.  NSF should:  Commit to stable and sustained funding for HPC centers to allow them to recruit and develop the expertise needed to maximize the potential offered by NSF’s hardware investments. Rigorous review and oversight processes can be developed and implemented to provide assurance that centers meet NSF expectations for performance.  Encourage HPC centers to build long-term relationships with vendors, thus providing researchers with the benefits of a planned road map for several generations of chip technology upgrades and with continuity in architecture and software environments. Results-oriented acquisition strategies can be applied to ensure that vendor performance meets center and NSF needs.

31 End 31


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