The SCaLeS Report Opportunities and Needs in Basic Energy Sciences

1. The SCaLeS ReportOpportunities and Needs in Basic Energy Sciences Thom H. Dunning, Jr. Joint Institute for Computational Sciences University of Tennessee • Oak Ridge National Laboratory Oak Ridge, Tennessee

2. Outline of Presentation • Background • Trends: Computing Technologies • Trends: Scientific Applications • Scientific Opportunities • SCaLeS Workshop • SCaLeS Report • Reports, Editors, and Process • Recommendations

3. BackgroundTrends: Computing Technologies • Computers • Microprocessor performance continuing to double every 18-24 months, but … • increasing mismatch with memory subsystem performance • increasing mismatch with communications subsystem performance • Storage • Disk storage capacity doubling every year, but … • data transfer rates increasing only modestly • Communications Fabric • Increasing performance, but … • increasingmismatch with performance of computational nodes • increasing mismatch with needed I/O transfer rates

4. BackgroundTrends: Scientific Applications • Computational Models • Continually refining existing models and creating new models • Multi-physics and multi-scale problems pose challenges • Parallel Computing • Increasing use of parallelism • Most codes scale to 10s of processors, a few to 1-2,000 processors, but almost none to 10,000 processors • Mathematical Techniques • New approaches hold great promise • Linear scaling reducing the growth rate in computational cost with increasing molecule size

5. Scientific Opportunities • Combustion Science • Reacting chemical flows • Autoignition • Molecular Science • Chemical reactivity (combustion, catalysis) • Heavy-element chemistry • Materials Science • Materials design • Multiscale materials modeling • Nanoscience • Self-assembly • Simulation of nano-devices

6. SCaLeS Workshop • Date: June 23-24, 2003 • Location: Arlington, Virginia • Organizer: D. Keyes, Columbia University • Goal: to assess the major opportunities and challenges facing computational science in areas of strategic importance to the Office of Science • Participants: 300+ scientists and engineers from academia, national laboratories, federal agencies and other institutions

7. SCaLeS Report • Editors • David Keyes, Editor-in-Chief • Phil Colella, LBNL (mathematics); Thom Dunning, UT/ORNL (science); Bill Gropp, ANL (computer science) • Topical Editors • Chemistry: R. Harrison, ORNL; T. Windus, PNNL • Combustion: J. Bell, LBNL; L. Rahn, SNL • Materials Science: F. Gygi, LLNL; M. Stocks, ORNL • Nanoscience: P. Cummings, Vanderbilt; L-W. Wang, LBNL • Process • Preliminary topical reports compiled from Workshop notes • Reports iterated with Workshop participants plus others

8. SCaLeS Report (cont’d) • Two Volumes • Volume 1. Summary and recommendations • Available for download: http://www.pnl.gov/scales/ • Volume 2. Detailed discussion of scientific opportunities and challenges • Available early next year

9. Hardware Infrastructure Software Infrastructure COLLABORATORIES COMPUTING SYSTEMS SOFTWARE M A T H E M A T I C S S C I E N T I F I C SIMULATION O P E R A T I N G D A T A G R I D S S Y S T E M Data Analysis & Visualization CODES Programming Environments Scientific Data Management Problem-solving Environments SciDAC: Successful Prototype to Build On

10. SCaLeS ReportRecommendations • Investments in Foundations of Computational Modeling and Simulation • #1. Computational Science • #5. Basic Theory and Mathematical Algorithms • #6. Recruit Computational Scientists • Investments in Hardware and Software Infrastructure • #2. Multidisciplinary Teams • #4. Computing Systems and Scientific Applications Software • #3. Capability and Capacity Computing • #8. New Computer Architectures for Scientific Computing • Investments in Networking and Collaboration Technologies • #7. Network Infrastructure and Software to Support Distributed Computing and Data Resources and Scientific Teams

11. SCaLeS ReportRecommendations: Foundations • Recommendation #1 Major new investments in computational science are needed in all of the mission areas of DOE’s Office of Science, so that the United States is the first, or among the first, to capture the new opportunities presented by the continuing advances in computing power. • Recommendation #5 Additional investments in hardware facilities and software infrastructure should be accompanied by sustained collateral investments in algorithm research and theoretical development. • Recommendation #6 Computational scientists of all types should be proactively recruited with improved reward structures and opportunities as early as possible in the educational process so that the number of trained computational science professionals is sufficient to meet present and future demands.

12. To achieve 1 kcal/mol accuracy: CCSD(T) in 1989 cc-Basis Sets in 1989 Faster mprocessors in 1990s Investments in Computational ScienceAdvances in Molecular Simulations • Bond energies critical for describing many chemical phenomena • Accuracy of calculated bond energies increased dramatically from 1970-2000 • Due to advances in • Theoretical methodology • Computational techniques • Computing technology 100 l l Error (kcal/mol) 10 l l 1 1970 1980 1990 2000

13. SCaLeS ReportRecommendations: Infrastructure • Recommendation #2 Multidisciplinary teams, with carefully selected leadership, should be assembled to provide the broad range of expertise needed to address the intellectual challenges associated with translating advances in science, mathematics and computer science into simulations that can take full advantage of advanced computers. • Recommendation #4 Investment in hardware facilities should be accompanied by sustained collateral investment in the software infrastructure for them. The efficient use of expensive computational facilities and the data they produce depends directly upon multiple layers of systems software and scientific software which, together with the hardware, are the engines of scientific discovery …

14. Developing New Simulation Capabilities Problem with Mathematical Model? Theory (mathematical model) Applied Mathematics Computer Science Problem with Computational Method? Computational Science (scientific codes) Basic Math Algorithms Computer Systems Software Computational Predictions Inadequate Experiment? Performance? NO YES New Tool for Scientific Discovery Adequate

15. SCaLeS ReportRecommendations: Infrastructure • Recommendation #3 Extensive investments in new computational facilities is strongly recommended, … New facilities should strike a balance between capability computing for those “heroic simulations” that cannot be performed in any other way, and capacity computing for “production” simulations that contribute to the steady stream of progress. • Recommendation #8 Federal investments in innovative, high-risk computer architectures that are well suited to scientific and engineering simulations is both appropriate and needed to complement commercial research and development. The commercial computing marketplace is no longer effectively driven by the needs of computational science.

16. Branscomb ReportFrom Desktop to Teraflop Frontier Computers Capability Computing High-end Capacity Computing Supercomputers Increasing Cost per Flop Increasing Capability Mid-range Parallel Computers and Clusters Workgroup Capacity Computing Personal Computers and Workstations Personal Computing

17. Parallel Simulations: Hard vs Soft Scaling “Hard” Scaling – near linear speed-up independent of problem size – uncommon increasing problem size Speed-up “Soft” Scaling – decreasing speed-up with constant problem size – increase problem size to maintain scaling but cost of calculation can increase more rapidly than that gained from increased scalability – common Number of Processors

18. SCaLeS ReportRecommendations: Networks and Collabs • Recommendation #7 Sustained investments must be made in network infrastructure for access and resource sharing, as well as in the software needed to support collaboration among distributed teams of scientists, recognizing that the best possible science teams will be widely separated geographically and that researchers will generally not be co-located with facilities and data.

19. Working Team Distributed Teams and Resources High-speed networks plus grid and collaboratory software are needed to connect researchers with each other and with computing and data resources.

20. Advances in Computer Technology “The rising tide of change shows no respect for the established order. Those who are unwilling or unable to adapt in response to this profound movement not only lose access to the opportunities that the information technology revolution is creating, they risk being rendered obsolete by smarter, more agile, or more daring competitors.” Jack J. Dongarra University of Tennessee