1. 4.3.1 Pioneering Applications: Priority Research Directions Criteria for Consideration What will Pioneering Apps do to address the barriers & gaps in associated Priority Research Directions (PRD’s)? (1)Demonstrated need for Exascale (2)Significant Scientific Impact in: basic physics, environment, engineering, life sciences, materials (3)Realistic Productive Pathway (over 10 years) to Exploitation of Exascale What new software capabilities will result? What new methods and tools will be developed? How will this realistically impact the research advances targeted by pioneering applications that may benefit from exascale systems? What’s the timescale in which that impact may be felt? Summary of Barriers & Gaps Potential Impact on Software Potential impact on user community (usability, capability, etc.)

2. 4.3.1 PIONEERING APPLICATIONS New capability 1 Integrated Plasma core-edge simulations Single hadron physics Regional decadal climate Global coupled climate processes Multi-hadron physics Electroweak symmetry breaking Whole system burning plasma simulations 2010201120122013201420152016201720182019 Science Milestones 1 PF10 PF100 PF1 EF Pioneering Applications with demonstrated need for Exascale to have significant scientific impact on associated priority research directions (PRD’s) with a productive pathway to exploitation of computing at the extreme scale

3. PIONEERING APPS Technology drivers – Advanced architectures with greater capability but with formidable software development challenges Alternative R&D strategies – Choosing architectural platform(s) capable of addressing PRD’s of Pioneering Apps on path to exploiting Exascale Recommended research agenda – Effective collaborative alliance between Pioneering Apps, CS, and Applied Math with an associated strong V&V effort Crosscutting considerations – Identifying possible common areas of software development need among the Pioneering Apps – Addressing common need to attract, train, and assimilate young talent into this general research arena

4. 4.3.1 Pioneering Applications: High Energy Physics Key challenges Achieving the highest possible sustained applications performance for the lowest cost Exploiting architectures with imbalanced node performance and inter-node communications Developing multi-layered algorithms and implementations to exploit on-chip (heterogeneous) capabilities, fast memory, and massive system parallelism Tolerance to and recovery from system faults at all levels over long runtimes Generic software components required: Performance analysis tools Highly parallel, high bandwidth I/O Efficient compilers for multi-layered parallel algorithms targeting heterogeneous architectures Automatic recovery from hardware/system errors Robust global file system and metadata standards Stress testing and verification of exascale hardware and system software Development of new stochastic and linear solver algorithms Reliable fault-tolerant massively parallel systems Global data sharing and interoperability Summary of research direction Potential impact on software component Potential impact on usability, capability, and breadth of community The applications community will develop: Multi-layer, multi-scale algorithms and implementations Optimised single-core/single-chip complex linear algebra routines Mixed precision arithmetic for fast memory access and off-chip communications Algorithms that tolerate hardware without error detection/correction Verification of all components (algorithms, software and hardware) Data management and standards for shared data use

5. Technology drivers – massive parallelism – heterogeneous microprocessor architectures Alternative R&D strategies – optimisation of computationally demanding kernels – algorithms targeting all levels of hardware parallelism Recommended research agenda – co-design of hardware and software Cross-cutting considerations – automated fault tolerance at all levels – multi-level algorithm implementation and optimisation 4.3.1 Pioneering Applications: High Energy Physics

6. 6 Industrial challenges in the Oil & Gas industry: Depth Imaging roadmap Algorithmic complexity Vs. corresponding computing power 3-18 Hz 3-35 Hz 3-55 Hz RTM 9.5 PF 900 TF 56 TF 10 15 flops 0,1 1 10 1000 100 1995 2000 2005 201020152020 0,5 Algorithm complexity Visco elastic FWI petro-elastic inversion elastic FWI visco elastic modeling isotropic/anisotropic FWI elastic modeling/RTM isotropic/anisotropic RTM isotropic/anisotropic modeling Paraxial isotropic/anisotropic imaging Asymptotic approximation imaging Substained performance for different frequency content over a 8 day processing duration courtesy HPC Power PAU (TF)

7. High Performance Computing as key-enabler 1980 19902000201020202030 Capacity: # of Overnight Loads cases run Available Computational Capacity [Flop/s] CFD-based LOADS & HQ Aero Optimisation & CFD-CSM Full MDO Real time CFD based in flight simulation x10 6 1 Zeta (10 21 ) 1 Peta (10 15 ) 1 Tera (10 12 ) 1 Giga (10 9 ) 1 Exa (10 18 ) 10 2 10 3 10 4 10 5 10 6 LES CFD-based noise simulation RANS Low Speed RANS High Speed HS Design Data Set Unsteady RANS “ Smart” use of HPC power: Algorithms Data mining knowledge Capability achieved during one night batch Courtesy AIRBUS France

8. Computational Challenges and Needs for Academic and Industrial Applications Communities BACKUP IESP/Application Subgroup 20032010 2015 20072006 Consecutive thermal fatigue event Computations enable to better understand the wall thermal loading in an injection. Knowing the root causes of the event  define a new design to avoid this problem. Part of a fuel assembly 3 grid assemblies Computation with an L.E.S. approach for turbulent modelling Refined mesh near the wall. 9 fuel assemblies No experimental approach up to now Will enable the study of side effects implied by the flow around neighbour fuel assemblies. Better understanding of vibration phenomena and wear-out of the rods. The whole vessel reactor 10 6 cells 3.10 13 operations 10 8 cells 10 16 operations 10 10 cells 5.10 18 operations 10 9 cells 3.10 17 operations 10 7 cells 6.10 14 operations Fujistu VPP 5000 1 of 4 vector processors 2 month length computation Cluster, IBM Power5 400 processors 9 days # 1 Gb of storage 2 Gb of memory IBM Blue Gene/L 20 Tflops during 1 month 600 Tflops during 1 month # 15 Gb of storage 25 Gb of memory # 10 Tb of storage 25 Tb of memory # 1 Tb of storage 2,5 Tb of memory # 200 Gb of storage 250 Gb of memory Power of the computerPre-processing not parallelized Mesh generation … ibid. … Scalability / Solver … ibid. … Visualisation 10 Pflops during 1 month Computations with smaller and smaller scales in larger and larger geometries  a better understanding of physical phenomena  a more effective help for decision making  A better optimisation of the production (margin benefits)

9. From sequences to structures : HPC Roadmap 2015 and beyond20112009 1 family 5.10 3 cpu/~week 1 family 5.10 4 cpu/~week 1 family ~ 10 4 *KP cpu/~week CSP : proteins structurally characterized ~ 10 4 # 25 Gb of storage 500 Gb of memory # 5*CSP Tb of storage 5*CSP Tb of memory # 5 Tb of storage 5 Tb of memory Computations using more and more sophisticated bio-informatical and physical modelling approaches  Identification of protein structure and function Identify all protein sequences using public resources and metagenomics data, and systematic modelling of proteins belonging to the family (Modeller software). Improving the prediction of protein structure by coupling new bio-informatics algorithm and massive molecular dynamics simulation approaches. Systematic identification of biological partners of proteins. Grand Challenge GENCI/CCRT Proteins 69 (2007) 415

10. PIONEERING APPS: Fusion Energy Sciences Criteria for Consideration (1) Demonstrated need for Exascale -- FES applications currently utilize LCF’s at ORNL and ANL, demonstrating scalability of key physics with increased computing capability (2) Significant Scientific Impact: (identified at DOE Grand Challenges Workshop) -- high physics fidelity integration of multi-physics, multi-scale FES dynamics -- burning plasmas/ITER physics simulation capability (3) Productive Pathway (over 10 years) to Exploitation of Exascale -- ability to carry out confinement simulations (including turbulence-driven transport) demonstrates ability to include higher physics fidelity components with increased computational capability -- needed for both of the areas identified in (2) as priority research directions

11. PIONEERING APPS: Fusion Energy Sciences Summary of Barriers & Gaps (1) high physics fidelity integration of multi-physics, multi-scale FES dynamics -- FES applications for macroscopic stability, turbulent transport, edge physics (where atomic processes important), etc. have demonstrated at various levels of efficiency the capability of using existing LCF’s -- Associated Barrier & Gap: need to integrate/couple improved versions of such large scale simulations to produce an experimentally validated integrated simulation capability for scenario modeling of the whole device (2) burning plasmas/ITER physics simulation capability -- As FES enters new era of burning plasma experiments on the reactor scale, require capabilities for addressing the larger spatial and longer energy-confinement time -- Associated Barrier & Gap: scales spanning the small gyroradius of the ions to the radial dimension of the plasmas will need to be addressed an order of magnitude greater spatial resolution is needed to account for the larger plasmas of interest major increase expected in the plasma energy confinement time (~1 second in the ITER device) together with the longer pulse of the discharges in these superconducting systems will demand simulations of unprecedented aggregate floating point operations

12. PIONEERING APPS: Fusion Energy Sciences Potential Impact on Software (1) What new software capabilities will result? -- For each science driver and each exascale-appropriate application the approach for developing new software capabilities will involve: Inventory current codes with respect to mathematical formulations, data structures, current scalability of algorithms and solvers (e.g. Poisson solves) with associated identification of bottlenecks to scaling, current libraries used, and “complexity” with respect to memory, flops, and communication Inventory current capabilities for workflows, frameworks, V&V, uncertainty quantification, etc. with respect to: tight vs. loose code coupling schemes for integration; mgt. of large data sets from experiments & simulations; etc. Inventory expected software developmental tasks for the path to exascale (concurrency, memory access, etc.) Inventory and carry out work-force assessment needs with respect to computer scientists, applied mathematician, and FES applications scientists. (2) What new methods and tools will be developed? -- Outcome from above inventory/assessment exercises should lead to development of corresponding exascale relevant tools and capabilities.

13. PIONEERING APPS: Fusion Energy Sciences Potential impact on user community (usability, capability, etc.) (1)How will this realistically impact the research advances targeted by FES that may benefit from exascale systems? -- The FES PRD’s for (1) high physics fidelity integrated simulations and for addressing (2) burning plasmas/ITER challenges will potentially be able to demonstrate how the application of exascale computing capability can enable the accelerated delivery of much needed modeling tools. (2) What’s the timescale in which that impact may be felt? -- As illustrated on Pioneering Apps Roadmap (earlier slide): 10 to 20 PF (2012) integrated plasma core-edge coupled simulations 1 EF (2018) whole-system burning plasma simulations applicable to ITER

14. PIONEERING APPS: Fusion Energy Sciences Technology drivers – Advanced architectures with greater capability but with formidable software development challenges (e.g., scalable algorithms and solvers; workflows & frameworks; etc.) Alternative R&D strategies – Choosing architectural platform(s) capable of addressing PRD’s of FES on path to exploiting Exascale Recommended research agenda – Effective collaborative alliance between FES, CS, and Applied Math (e.g., SciDAC activities) with an associated strong V&V effort Crosscutting considerations – Identifying possible common areas of software development needs with Pioneering Apps (climate, etc.) – Critical need in FES to attract, train, and assimilate young talent into this field – in common with general computational science research arena