Alan Blatecky Office of Cyberinfrastructure

1. OCI: Opportunities & Challenges Alan Blatecky Office of Cyberinfrastructure

2. OCI Role OCI Technology Push Science Pull Capabilities increase as refinements are implemented Development modifications made as required Spiral Development

3. Advanced Computational Infrastructure (ACI) Vision: Support a comprehensive portfolio of advanced computing infrastructure, programs and other resources to facilitate cutting-edge foundational research in Computational and Data Enabled Science and Engineering (CDS&E) and its applications to all disciplines.

4. Advanced Computational Infrastructure • Invest in diverse and innovative national scale shared resources, outreach and education complementing campus and other investments • Leverage and invest in collaborative flexible “fabrics” dynamically connecting scientific communities with computational resources and services at all scales (campus, regional, national, international) CIPRES – Cyberinfrastructure for Phylogenic Research XSEDE

5. Blue Waters/UIUC National resource offering large allocations for a small number of diverse and significant research projects across the U.S. Highly Scalable Heterogeneous System to enable investigations of computationally challenging problems that require sustained PetaFlops (1015) performance and/or large data and large memory

6. Stampede/UT at Austin Stampede will accommodate larger simulations (both in fidelity and number of ensemble members) producing more accurate forecasts, and permit more research groups during critical response efforts Phylogenic Trees: Stampede will allow us to approach the full tree for all green plant species (~500,000) on Earth to gain insights into the origins of drought resistance or nitrogen efficiency in plants, which could then be bred into future food crops Hurricane Ike tracking predictions using the WRF program and 30 ensemble members (Courtesy F. Zhang, PSU and Y. Weng) Expands the range of data intensive computationally-challenging science and engineering applications that can be tackled with current national resources Introduces new heterogeneous architecture based on Intel MIC to science and engineering research communities

7. XSEDE Enable campus, regional and national resources and communities to interact transparently Flexibly add diverse, distributed, heterogeneous sets of digital resources (computers, data, instruments) that change over time Provide ACI support (management and user), education and outreach to science community Develop computational science and education expertise and capabilities across a broad set of disciplines

8. Computational usage first 9 months of FY12 Number of Allocations 8,695 distinct users 32 NSF Divisions 1,833 Publications

9. Demand and allocation of Service Units 1 Billion SU gap

10. ACI Challenges for the next decade Technology diversity, pace of change and sustainability Increase collaboration and interaction among local, national, and international cyberinfrastructures Broadening ACI capabilities to all science and education including a balance between “Deep” and “Wide” Rapidly growing requirements for CDS&E tools, capabilities, and expertise Data, computation and software are three sides of the same coin – inextricably linked and co-dependent Allocation and prioritization of resources

11. Science and Society Transformed by Data • Modern science • Data- and compute-intensive • Integrative, multiscale • Multi-disciplinary Collaborations for Complexity • Individuals, groups, teams, communities • Sea of Data – Big Data • Age of Observation • Distributed, central repositories, sensor- driven, diverse, etc

12. Building a National Data Infrastructure • The data infrastructure will be complex and involve a range of modalities • Data centers, clouds, distributed systems, replication • Partnerships between campuses, government, business • Leveraging and building on the myriad of data efforts and projects underway • New focus on curation, interoperability, sharing of data, common approaches and data policies • New sustainability models for data stewardship will emerge, driven by the needs of individual communities • Data resources will have to allocated • More data being generated than can be stored • What should be kept, what can be discarded? And, on what basis?

13. Data challenges Increase in volume of simulation-based data will strain and break existing usage models Need significant investments in data analytics, tools and applications development Storage solutions and models already a critical problem New sustainability models for data stewardship need to be developed CDS&E workforce expertise is becoming ever more critical; from algorithm development to data creators, technicians, managers, and scientists

14. Role of Software in Science • Software essential for the bulk of science • About half the papers in recent issues of Science were software-intensive projects • Research becoming dependent upon advances in software • Significant software development being conducted across NSF: NEON, OOI, NEES, NCN, iPlant, etc • Wide range of software types; system, apps, modeling, gateways, analysis, algorithms, middleware, libraries • Development, production and maintenance are people intensive • Software life-times are long compared to hardware • Under-appreciated value

15. Software Challenges • Robust software for data-driven science • Documentation and sustainability • Managing increasing complexity • Disruptive architectures • Governance of software communities • Software assurance, reproducibility, trust in models, simulation & data • Education; using modern software in education, educating people how to use and create software, software engineering • Interaction with consumer trends, such as app store models • Policies for citation, stewardship, attribution and authorship for use of open software

16. CC-NIE: Data Driven Networking Infrastructure for the Campus and Researcher • network infrastructure improvements at the campus level • network upgrades within a campus network to support a wide range of science data flows • re-architecting a campus network to support large science data flows • campus network upgrades addressing sustainable infrastructure through improvements in energy efficient networking. • campus network upgrades addressing the growing needs in mobile networking. • Network connection upgrade for the campus connection to a regional optical exchange or point-of-presence that connects to Internet2 or National Lambda Rail.

17. CC-NIE: Network Integration and Applied Innovation End-to-end network CI through integration of existing and new technologies and applied innovation Applying network research results, prototypes, and emerging innovations to enable (identified) research and education Leverage new and existing investments in network infrastructure, services, and tools by combining or extending capabilities to work as part of the CI environment used by scientific applications and users

18. International Research Network Backbone Concept: Notational only Architecture, aggregation nodes, locations, bandwidth, connectivity to be determined Aggregation nodes: Primary connection to International Backbone; every country and economy has the opportunity to be an aggregation node or connect to one Shared High Bandwidth network (multi 10/100 Gig) interconnecting Aggregation nodes Sep 2012

19. Education, Learning, Workforce Development, CDS&E At the end of the day, cyberinfrastructure is all about people; enabling them to do what they have not been able to do before

20. Conclusion OCI Transforming Science a Bit at a Time