1. 1 OASCR Genomes to Life OBER Genomes to Life a partnership between Biology and Computing http://www.doegenomestolife.org/ Gary Johnson John Houghton Office of Science

2. 2 OASCR Genomes to Life OBER

3. 3 Office of Advanced Scientific Computing Research: Mathematical, Information and Computational Sciences a brief overview http://www.sc.doe.gov/production/octr/mics/index.html

4. 4 OASCR Genomes to Life OBER operate supercomputers, a high performance network, and related facilities. MICS Mission Discover, develop, and deploy the computational and networking advances that enable researchers in the scientific disciplines to analyze, model, simulate, and predict complex physical, chemical, and biological phenomena important to the Department of Energy (DOE). support a broad research portfolio in advanced scientific computing – applied mathematics, computer science, networking and collaboratory software

5. 5 OASCR Genomes to Life OBER Program Strategy Basic Research …simulation…distributed teams, of complex systems remote access to facilities Energy Sciences Network (ESnet) Advanced Computing Research Facilities National Energy Research Scientific Computing Center (NERSC) Materials Chemical Combustion Accelerator HEP Nuclear Fusion Climate Astrophysics Applied Mathematics Computer Science Scientific Application Pilots Collaboratory Tools Collaboratory Pilots BES, BER, FES, HEP, NP Integrated Software Infrastructure Centers Teams-mathematicians, computer scientists, application scientists, and software engineers High Performance Computing and Network Facilities for Science Research to enable… Grid enabling research Topical Computing Networking Nanoscience Computational Biology

6. 6 OASCR Genomes to Life OBER Budget Request FY2003- $166,625,000 Base Research Comp. Bio. SciDAC Facilities SBIR/STTR Enhancements over FY2002 Computational Biology +$5.6M SciDAC +$5.3M Facilities +$1.3M

7. 7 OASCR Genomes to Life OBER unscalable scalable Problem Size (increasing with number of processors) Time to Solution 200 150 50 0 100 10100 1000 1 Ax=bF(u,x,y,z)=0F(u,u’,u’’,…,x,y,z,t)=0 Linear Solvers Nonlinear Solvers PDE Solvers From the “simple”… …to the complex! Ax= Bx Eigensolvers Algorithms must be scalable. Ideally, as the problem size grows and the number of processors grows, the solution time does not ! Combustion ~60 coupled, nonsymmetric, nonlinear time-dependent PDEs on 10M mesh points. Time steps range from 10 -12 (for chemical reaction rates) to 10 -2 (for the speed of flame front) Current simulations use 44 amino acids. Protein Folding Actual protein ~300 amino acids. Run times using current techniques? Greater than life of the universe! Applied Mathematical Sciences

8. 8 OASCR Genomes to Life OBER AMS Base Research Program Objectives Advance our understanding of science and technology by supporting research in basic applied mathematics and in computational research that facilitates the use of the latest high-performance computer systems. Applied Mathematics Research: Linear Algebra Fluid Dynamics Differential Eqs. Optimization Robust High-Performance Numerical Libraries Adaptive Mesh Refinement (AMR) Sustained Teraflop/s simulations Level Set / Fast Marching Methods Investment in Education Computational Sciences Graduate Fellowship Ultrascalable Algorithms (up to millions of PEs) Mathematical Microscopy These opportunities will be explored through Genomes to Life (with BER) Comp. Nanoscience (with BES) Fusion Energy (FESAC-ASCAC workshop) Ongoing Projects Growth Opportunities Accomplishments Grid Generation Predictability Analysis & Uncertainty Quantification Automated Reasoning Advanced Numerical Algorithms: PETSc Aztec TAO ADIFOR / ADIC Hypre CHOMBO SuperLU PICO

9. 9 OASCR Genomes to Life OBER Computer Science Research Challenge – HPC for Science is (still after fifteen years!) –Hard to use –Inefficient –Fragile –An unimportant vendor market Vision –A comprehensive, integrated software environment which enables the effective application of high performance systems to critical DOE problems Goal– Radical Improvement in –Application Performance –Ease of Use –Time to Solution Node and System Hardware Arch User Space Runtime Support OS Kernel OS Bypass Scientific Applications System Admin Software Development Chkpt/RstrtMath LibsDebuggers Viz/DataScheduler PSEsRes. MgtFramewrks Compilers Perf ToolsFile Sys Runtme Tls HPC System Elements

10. 10 OASCR Genomes to Life OBER Computer Science Technical Elements 25% 19% 18% 15% 23%

11. 11 OASCR Genomes to Life OBER Major Accomplishments PVM – the first widely successful model for parallel computing MPI – the lingua franca of today’s parallel computing MPICH – the open source version of MPI that is the basis for all vendor adaptations Global Arrays – the distributed shared memory programming model that is at the core of NWChem, the motivating application for SciDAC CTSS – the first interactive operating system for high performance computers SUNMOS/Puma/Cougar – the most successful high performance parallel operating system OSCAR – a partnership with industry, the most widely used open source toolkit for management of Linux clusters

12. 12 OASCR Genomes to Life OBER National Collaboratories The nature of how large scale science is done is changing –Distributed data, computing, people, instruments –Instruments integrated with large-scale computing –Human resources are seldom collocated with the resources needed for their science Additional drivers –Large and international collaborations –Management of unique national user facilities –Large multi-laboratory science and engineering projects Why?

13. 13 OASCR Genomes to Life OBER Scientist An End-to-End Problem for Applications Many different types of objects need to be connected to and coordinated by the networks

14. 14 OASCR Genomes to Life OBER Staff – Ed Oliver, Associate Director for Advanced Scientific Computing Research – Dan Hitchcock, Senior Scientific Advisor – Linda Twenty, Senior Budget & Financial Specialist – Walt Polansky, Acting Director MICS – Gary Johnson, ACRTs, Computational Biology – Fred Johnson, Computer Science – William (Buff) Miner, NERSC & Scientific Applications – Thomas Ndousse-Fetter, Network Research – Kimberly Rasar, Senior Info. Tech. (SciDAC) – Chuck Romine, Applied Mathematics – Mary Anne Scott, Collaboratories – George Seweryniak, Esnet – John van Rosendale, Computer Science- Visualization and Data Management – Vacancies- (2) – Jane Hiegel – Susan Kilroy Phone- 301-903-5800 Fax- 301-903-7774 http://www.sc.doe.gov/production/octr/mics/index.html

15. 15 OASCR Genomes to Life OBER OASCR Advisory Committee Committee Chair: Margaret Wright, NYU Subcommittee Chairs: –Biology: Juan Meza, LBNL –Computing Infrastructure: Jill Dahlberg, General Atomics Members in common with BERAC: Warren Washington, NCAR Next Meeting: 2-3 May 2002 Crowne Plaza Hotel 14th and K Streets Washington, DC

16. 16 OASCR Genomes to Life OBER Genomes to Life Program History Phased program startup –FY 2002: OBER –FY 2003: OASCR Precursor activity –FN 01-21: Advanced Modeling and Simulation of Biological Systems –9 Awards, $3M Current solicitations –FN 02-13: Genomes to Life Program planning –5 workshops –Goal 4 roadmap –Update to GTL roadmap

17. 17 OASCR Genomes to Life OBER GTL Planning Activities 7-8 AugustGTL Computing Workshop 6-7 SeptemberSystems Biology & GTL Workshop 22-23 JanuaryComputing Infrastructure Workshop 6-7 MarchComputer Science for GTL Workshop 18-19 MarchMathematics for GTL Workshop 19 AprilDraft Goal 4 Roadmap FutureNew Edition of the GTL Roadmap

18. 18 OASCR Genomes to Life OBER GTL Goal 4 Roadmap

19. 19 OASCR Genomes to Life OBER Genomes to Life Goals Goal 1 Identify and Characterize the Molecular Machines of Life – the Multiprotein Complexes that Execute Cellular Functions and Govern Cell Form Goal 2 Characterize Gene Regulatory Networks Goal 3 Characterize the Functional Repertoire of Complex Microbial Communities in their Natural Environments at the Molecular Level Goal 4 Develop the Computational Methods and Capabilities to Advance Understanding of Complex Biological Systems and Predict their Behavior

20. 20 OASCR Genomes to Life OBER Three Computing Domains Bioinformatics/Data-Intensive Applications Biophysics/Compute-Intensive Applications Biosystems/Complex Systems Modeling

21. 21 OASCR Genomes to Life OBER Biology & Computing Perspectives

22. 22 OASCR Genomes to Life OBER Domain Challenges Bioinformatics –Heterogeneous, large and growing data sets –Legacy systems that don’t interoperate and don’t scale Biophysics –Already bumping up against computational resources More computation, better algorithms, new theory Biosystems –Too much data not to have models –Data-poor and biology-poor –Parts list short, but complex systems

23. 23 OASCR Genomes to Life OBER Initial Thoughts on Computational Infrastructure