1. CCSM Performance, Successes and Challenges Tony Craig NCAR RIST Meeting March 12-14, 2002 Boulder, Colorado, USA

2. OUTLINE Overview of CCSM, platforms Individual component performance Coupling, load balance, coupled performance Challenges in the environment Summary

3. CCSM Overview CCSM = Community Climate System Model (NCAR) Designed to evaluate and understand earth’s climate, both historical and future. Multiple executables (5) –Atmosphere (CAM), MPI/OpenMP –Ocean (POP), MPI –Land (CLM2), MPI/OpenMP –Sea Ice (CSIM4), MPI –Coupler (CPL5), OpenMP

4. CCSM Platforms Currently support –IBM Power3, Power4 –SGI Origin –CPQ (not quite) Future? –Linux –Vector Platforms

5. CCSM Performance Summary T42 resolution atm and land, 26 vertical levels in atm (128x64x26) 1 degree resolution ocean and ice, 40 vertical levels in ocean (320x384x40) On 100 processors of IBM winterhawk II (4 processors/node, 375 Mhz clock, TBMX network): –Model runs about 4 simulated years / day –Requires about a month to run a century

6. Component Timings

7. Component Scaling

8. Coupling Multiple executables, running concurrently on unique processors sets. All communication is root-to-root, via coupler. Coupling frequency is 1 hour for atm, land, and ice models, 1 day for ocean. 1 send and 1 receive for each component during each coupling interval. Scientific requirement that the land and ice models compute fluxes for the atmosphere “first”. Atm Ocean Land Ice Time Processors

9. Figure 2: CCSM Load Balancing 40 ocean 32 atm 16 ice 12 land 04 cpl 104 total 9.43.0 6.215.0 8.640.4 53.2 10.0 55 32 Timings in seconds per day 5 processors

10. Other Coupling Options: single executable, sequential Single executable, components running sequentially on all processors –No idle time –Components must scale well to large number of processors Atm Ocean Land Ice Time Processors

11. Other Coupling Options: combined sequential/concurrent on overlapping processors Single or multiple executable, components running sequentially and/or concurrently on overlapping processor sets. –Optimal compromise between scaling and load balance. –Requires sophisticated coupler and improvements to hardware and machine software capabilities. LandIce Time Processors Ocean Atm

12. Challenges in the Environment Batch environment; startup and control of multiple executables Tools –Debuggers are challenged on multiple executable, MPI/OpenMP parallel models –Timing and profiling tools inadequate –Compilers and libraries can be slow, buggy, and constantly changing Machines not well balanced; chip speed vs interconnect vs memory access and cache.

13. Performance Summary Successes –Single component scaling –MPI/OpenMP parallelization of components –Load balancing multiple executables Challenges –Scaling up to 1000s of processors for our standard problem –Load balancing multiple executables –Optimizing relative to cache, memory, network, chip, and parallelism –RISC vs Vector architectures –Environment