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Climate-Weather modeling studies Using a Prototype Global Cloud-System Resolving Model

Explore the effect of clouds in climate-weather models using a prototype global cloud-system resolving model. This project focuses on scientific goals, team description, science lessons, parallel programming, I/O patterns, performance, tools, scalability, and roadmap.

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Climate-Weather modeling studies Using a Prototype Global Cloud-System Resolving Model

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  1. Climate-Weather modeling studies Using a Prototype Global Cloud-System Resolving Model Zhi Liang (GFDL/DRC)

  2. A) Project Overview • The project • Climate-Weather modeling studies Using a Prototype • Global Cloud-System Resolving Model • Science goals Study the effect of clouds in climate-weather models • The participants, description of team • Chris Kerr (GFDL/UCAR, PI) • V. Balaji (GFDL/Princeton University, PI) • Zhi Liang (GFDL/DRC) • Sponsor • GFDL/NOAA

  3. B) Science Lesson • What does the application do, and how? • Study the effect of clouds in climate-weather models: • Role of clouds is critical in global climate models • First generation experiments: atmospheric model • Second generation experiments: coupled atmosphere, ocean, … models • Experiments will focus on 2008 "Year of Tropical Convection” research program: • 12 km HIRAM hydrostatic model • 3.5 km HIRAM non-hydrostatic model

  4. C) Parallel Programming Model • Hybrid model of MPI and OpenMP. • Languages: Fortran 90 and C. • Runtime libraries: netcdf, mpich • Platforms: Cray XT6, Blue Gene/P, Blue Gene/Q, SGI Altix Ice • Status: Runs OK • Future Plan: Performance analysis on Blue Gene/Q.

  5. E) I/O Patterns and Strategy • Input I/O and output I/O patterns Distribute I/O. Use netcdf4. • Approximate sizes of inputs and outputs Output about 600G for 1 month model time. • Checkpoint / Restart capabilities Reproduce and intermediate restart. • Future plans for I/O Improve the I/O scaling.

  6. G) Performance • What tools do you use now to explore performance Hardware performance counters, tau, Craypat • What do you believe is your current bottleneck to better performance/scaling? Under investigation. • Current status and future plans for improving performance Improve the MPI, OpenMP and I/O scaling.

  7. H) Tools • How do you debug your code? Print statement totalview ddt

  8. I) Status and Scalability • How does your application scale now? • OK. • Where do you want to be in a year? • Better scaling. • What are your top 5 pains? (be specific) • I/O scaling: large output. • MPI and OpenMP scaling. • What did you change to achieve current scalability?   • Non-blocking communication, distributed IO.

  9. Scalability

  10. J) Roadmap • What do you hope to learn / discover? Tools for OpenMP performance analysis. • What improvements will you need to make: MPI, OpenMP and I/O scaling, load balance. • What are your plans? Performance analysis.

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