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Rich Loft Director, Technology Development Computational and Information Systems Laboratory

An Inconvenient Question: Are We Going to Get the Algorithms and Computing Technology We Need to Make Critical Climate Predictions in Time?. Rich Loft Director, Technology Development Computational and Information Systems Laboratory National Center for Atmospheric Research loft@ucar.edu.

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Rich Loft Director, Technology Development Computational and Information Systems Laboratory

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  1. An Inconvenient Question: Are We Going to Get the Algorithms and Computing Technology We Need to Make Critical Climate Predictions in Time? Rich Loft Director, Technology Development Computational and Information Systems Laboratory National Center for Atmospheric Research loft@ucar.edu

  2. Main Points • Nature of the climate system makes it a grand challenge computing problem. • We are at a critical juncture: we need regional climate prediction capabilities! • Computer clock/thread speeds are stalled: massive parallelism is the future of supercomputing. • Our best algorithms, parallelization strategies and architectures are inadequate to the task. • We need model acceleration improvements in all three areas if we are to meet the challenge.

  3. Options for Application Acceleration • Scalability • Eliminate bottlenecks • Find more parallelism • Load balancing algorithms • Algorithmic Acceleration • Bigger Timesteps • Semi-Lagrangian Transport • Implicit or semi-implicit time integration – solvers • Fewer Points • Adaptive Mesh Refinement methods • Hardware Acceleration • More Threads • CMP, GP-GPU’s • Faster threads • device innovations (high-K) • Smarter threads • Architecture - old tricks, new tricks… magic tricks • Vector units, GPU’s, FPGA’s

  4. A Very Grand Challenge:Coupled Models of the Earth System ~150 km air column water column Viner (2002) Typical Model Computation: - 15 minute time steps - 1 peta-flop per model year There are 3.5 million timesteps in a century

  5. Multicomponent Earth System Model Coupler Land Atmosphere Ocean Sea Ice C/N Cycle Dyn. Veg. Land Use Ecosystem & BGC Gas chem. Prognostic Aerosols Upper Atm. Ice Sheets • Software Challenges: • Increasing Complexity • Validation and Verification • Understanding the Output Key concept: A flexible coupling framework is critical!

  6. Climate Change Credit: Caspar Amman NCAR

  7. IPCC AR4 - 2007 • IPCC AR4: “Warming of the climate system is un-equivocal” … • …and it is “very likely” caused by human activities. • Most of the observed changes over the past 50 years are now simulated by climate models adding confidence to future projections. • Model Resolutions: O(100 km)

  8. 2007 Climate Change Research Epochs Assess regional impacts Simulate adaptation strategies Simulate geoengineering solns Reproduce historical trends Investigate climate change Run IPCC Scenarios Before IPCCAR4 After Curiosity Driven Policy Driven

  9. Where we want to go:The Exascale Earth System Model Vision Coupled Ocean-Land-Atmosphere Model ~1 km x ~1 km (cloud-resolving) 100 levels, whole atmosphere Unstructured, adaptive grids ~100 m 10 levels Landscape-resolving ~10 km x ~10 km (eddy-resolving) 100 levels Unstructured, adaptive grids Requirement: Computing power enhancement by as much as a factor of 1010-1012 ESSL - The Earth & Sun Systems Laboratory YIKES!

  10. Compute Factors for ultra-high resolution Earth System Model (courtesy of John Drake, ORNL)

  11. Why run-length:global thermohaline circulation timescale: 3,000 years

  12. Why resolution: Atmospheric convective (cloud) scales in the : O(1 km)

  13. Ocean component of CCSM (Collins et al, 2006) Eddy-resolving POP (Maltrud & McClean,2005) Why High Resolution in the Ocean? 1˚ 0.1˚

  14. High Resolution and the Land Surface

  15. Performance Improvements are not coming fast enough! …suggests 1010 to 1012 improvement will take 40 years

  16. ITRS Roadmap: feature size dropping 14%/year By 2050 reaches the size of an atom – oops!

  17. National Security Agency - The power consumption of today's advanced computing systems is rapidly becoming the limiting factor with respect to improved/increased computational ability." 

  18. Chip Level Trends: Stagnant Clock Speed • Chip density is continuing increase ~2x every 2 years • Clock speed is not • Number of cores are doubling instead • There is little or no additional hidden parallelism (ILP) • Parallelism must be exploited by software Source: Intel, Microsoft (Sutter) and Stanford (Olukotun, Hammond)

  19. Moore’s Law -> More’s Law: Speed-up through increasing parallelism How long can we double the number of cores per chip?

  20. NCAR and University Colorado Partner to Experiment with Blue Gene/L • Characteristics: • 2048 Processors/5.7 TF • PPC 440 (750 MHz) • Two processors/node • 512 MB memory per node • 6 TB file system Dr. Henry Tufoand myself with “frost”(2005)

  21. Status and immediate plans for high resolution Earth System Modeling

  22. Current high resolution CCSM runs • 0.25 ATM,LND + 0.1 OCN,ICE [ATLAS/LLNL] • 3280 processors • 0.42 simulated years/day (SYPD) • 187K CPU hours/year • 0.50 ATM,LND + 0.1 OCN,ICE [FRANKLIN/NERSC] • Current • 5416 processors • 1.31 SYPD • 99K CPU hours/year • “Efficiency Goal • 4932 processors • 1.80 SYPD • 66K CPU hours/year

  23. 168 sec. 120 sec. 52 sec. ATM [np=1664] CPL [np=384] LND [np=16] ICE [np=1800] 21 sec. 91 sec. 5416 processors Current 0.5 CCSM “fuel efficient” configuration [franklin] OCN [np=3600]

  24. 168 sec. 120 sec. 52 sec. ATM [np=1664] OCN [np=3600] CPL [np=384] LND [np=16] ICE [np=1800] 21 sec. 91 sec. 5416 processors Efficiency issues in current 0.5 CCSM configuration Use Space Filling Curves (SFC) in POP, reduce processor count by 13%.

  25. Load Balancing: Partitioning with Space Filling Curves Partition for 3 processors

  26. Space-filling Curve Partitioning for Ocean Model running on 8 Processors Static Load Balancing… Key concept: no need to compute over land!

  27. Ocean Model 1/10 Degree performance Key concept: You need routine access to > 1k procs to discover true scaling behaviour!

  28. 168 sec. 120 sec. 52 sec. ATM [np=1664] OCN [np=3600] CPL [np=384] LND [np=16] ICE [np=1800] 21 sec. 91 sec. 5416 processors Efficiency issues in Current CCSM 0.5 configuration Use wSFC in CICE, reduce Execution time by 2x.

  29. Large domains @ low latitudes Small domains @ high latitudes Static, Weighted Load Balancing Example:Sea Ice Model CICE4 @ 1° on 20 processors Courtesy of John Dennis

  30. 168 sec. 120 sec. 52 sec. ATM [np=1664] OCN [np=3600] CPL [np=384] LND [np=16] ICE [np=1800] 21 sec. 91 sec. 5416 processors Efficiency issues in current 0.5 CCSM configuration: Coupler Unresolved scalability issues in Coupler – Options: Better interconnect,Nested grids, PGAS language paradigm

  31. 168 sec. 120 sec. 52 sec. ATM [np=1664] OCN [np=3600] CPL [np=384] LND [np=16] ICE [np=1800] 21 sec. 91 sec. 5416 processors Efficiency issues in current 0.5 CCSM configuration: atmospheric component Scalability limitation in 0.5° fv-CAM[MPI] – shift to hybrid OpenMP/MPI version

  32. 62 sec. 62 sec. 31 sec. ATM [np=5200] CPL [np=384] LND [np=40] ICE [np=8120] 21 sec. 10 sec. 19460 processors Projected 0.5 CCSM “capability” configuration: 3.8 years/day OCN [np=6100] Action: Run hybrid atmospheric model

  33. 62 sec. 62 sec. 31 sec. ATM [np=5200] CPL [np=384] LND [np=40] ICE [np=8120] 21 sec. 10 sec. 14260 processors Projected 0.5 CCSM “capability” configuration - version 2: 3.8 years/day OCN [np=6100] Action: Thread ice model

  34. Ne=16 Cube Sphere Showing degree of non-uniformity Scalable Geometry Choice: Cube-Sphere • Sphere is decomposed into 6 identical regions using a central projection (Sadourny, 1972) with equiangular grid (Rancic et al., 1996). • Avoids pole problems, quasi-uniform. • Non-orthogonal curvilinear coordinate system with identical metric terms

  35. Scalable Numerical Method:High-Order Methods • Algorithmic Advantages of High Order Methods • h-p element-based method on quadrilaterals (Ne x Ne) • Exponential convergence in polynomial degree (N) • Computational Advantages of High Order Methods • Naturally cache-blocked N x N computations • Nearest-neighbor communication between elements (explicit) • Well suited to parallel µprocessor systems

  36. HOMME: Computational Mesh • Elements: • A quadrilateral “patch” of N x N gridpoints • Gauss-Lobatto Grid • Typically N={4-8} • Cube • Ne = Elements on an edge • 6 x Ne x Ne elements total

  37. Partitioning a cube-sphere on 8 processors

  38. Partitioning a cubed-sphere on 8 processors

  39. Aqua-Planet CAM/HOMME Dycore Full CAM Physics/HOMME Dycore Parallel I/O library used for physics aerosol input and input data ( work COULD NOT have been done without Parallel IO) Work underway to couple to other CCSM components 5 years/day

  40. 60 sec. 60 sec. 47 sec. HOMME ATM [np=24000] CPL [np=3840] LND [np=320] ICE [np=16240] 8 sec. 5 sec. 30000 processors Projected 0.25 CCSM “capability” configuration - version 2: 4.0 years/day OCN [np=6000] Action: insert scalable atmospheric dycore

  41. Using a bigger parallel machine can’t be the only answer • Progress in the Top 500 list is not fast enough • Amdahl’s Law is formidable opponent • Dynamical timestep goes like N-1 • Merciless effect of Courant limit • The cost of dynamics relative to physics increases as N • e.g. if dynamics takes 20% at 25 km it will take 86% of the time at 1 km • Traditional parallelization of horizontal leaves N2 per thread cost (vertical x horizontal) • Must inevitably slow down with stalled thread speeds

  42. Options for Application Acceleration • Scalability • Eliminate bottlenecks • Find more parallelism • Load balancing algorithms • Algorithmic Acceleration • Bigger Timesteps • Semi-Lagrangian Transport • Implicit or semi-implicit time integration – solvers • Fewer Points • Adaptive Mesh Refinement methods • Hardware Acceleration • More Threads • CMP, GP-GPU’s • Faster threads • device innovations (high-K) • Smarter threads • Architecture - old tricks, new tricks… magic tricks • Vector units, GPU’s, FPGA’s

  43. Accelerator Research • Graphics Cards – Nvidia 9800/Cuda • Measured 109x on WRF microphysics on 9800GX2 • FPGA – Xilinx (data flow model) • 21.7x simulated on sw-radiation code • IBM Cell Processor - 8 cores • Intel Larrabee

  44. DG+NH+AMR • Curvilinear elements • Overhead of parallel AMR at each time-step: less than 1% Idea based on Fischer, Kruse, Loth (02) Courtesy of Amik St. Cyr

  45. SLIM ocean model • Louvain la Neuve University • DG, implicit, AMR unstructured To be coupled to prototype unstructured ATM model (Courtesy of J-F Remacle LNU)

  46. NCAR Summer Internships in Parallel Computational Science (SIParCS)2007-2008 • Open to: • Upper division undergrads • Graduate students • In Disciplines such as: • CS, Software Engineering • Applied Math, Statistics • ES Science • Support: • Travel, Housing, Per diem • 10 weeks salary • Number of interns selected: • 7 in 2007 • 11 in 2008 http://www.cisl.ucar.edu/siparcs

  47. Meanwhile - the clock is ticking

  48. Contributors: D. Bailey (NCAR) F. Bryan (NCAR) T. Craig (NCAR) A. St. Cyr (NCAR) J. Dennis (NCAR) J. Edwards (IBM) B. Fox-Kemper (MIT,CU) E. Hunke (LANL) B. Kadlec (CU) D. Ivanova (LLNL) E. Jedlicka (ANL) E. Jessup (CU) R. Jacob (ANL) P. Jones (LANL) S. Peacock (NCAR) K. Lindsay (NCAR) W. Lipscomb (LANL) R. Loy (ANL) J. Michalakes (NCAR) A. Mirin (LLNL) M. Maltrud (LANL) J. McClean (LLNL) R. Nair (NCAR) M. Norman (NCSU) T. Qian (NCAR) M. Taylor (SNL) H. Tufo (NCAR) M. Vertenstein (NCAR) P. Worley (ORNL) M. Zhang (SUNYSB) Funding: DOE-BER CCPP Program Grant DE-FC03-97ER62402 DE-PS02-07ER07-06 DE-FC02-07ER64340 B&R KP1206000 DOE-ASCR B&R KJ0101030 NSF Cooperative Grant NSF01 NSF PetaApps Award Computer Time: Blue Gene/L time: NSF MRI Grant NCAR University of Colorado IBM (SUR) program BGW Consortium Days IBM research (Watson) LLNL Stony Brook & BNL CRAY XT3/4 time: ORNL Sandia The Size of the Interdisciplinary/Interagency Team Working on Climate Scalability

  49. Thanks! Any Questions?

  50. Q. If you had a petascale computerwhat would you do with it? A. Use it as a prototype of an exascale computer.

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