NSF NCAR | NASA GSFC | DOE LANL ANL | NOAA NCEP GFDL | MIT Adoption and field tests of M.I.T General Circulation Model (MITgcm) with ESMF Chris Hill ESMF Community Meeting MIT, July 2005

NSF NCAR | NASA GSFC | DOE LANL ANL | NOAA NCEP GFDL | MIT Outline MITgcm – a very quick overview –algorithmic features –software characteristics Adopting ESMF –strategy –steps Field test applications –MITgcm coupling with everything (including itself!) - interoperating with NCAR, GFDL and UCLA atmosphere models, intermediate complexity coupled system. –high-end parameterization as a coupled problem. Next steps

NSF NCAR | NASA GSFC | DOE LANL ANL | NOAA NCEP GFDL | MIT MITgcm algorithmic characteristics General orthogonal curvilinear coordinate finite-volume dynamical kernel. Flexible, scalable domain decomposition 1CPU  CPU’s. Can apply to wide range of scales, hydrostatic  non-hydrostatic. Pressure-height isomorphism allows kernel to apply to ocean or atmosphere. Many optional packages spanning biogeochemistry, atmospheric physics, boundary layers, sea-ice etc… Adjoints to most parts for assimilation/state-estimation and sensitivity analysis. HYDROSTATIC NON-HYDROSTATIC ~100 km ~10 km ~1 km ~20 m ~100 m ~1000 km and more…. see

NSF NCAR | NASA GSFC | DOE LANL ANL | NOAA NCEP GFDL | MIT MITgcm software characteristics Fortran (what else ) Approx 170K executable statements Generic driver code (superstructure), coupling code, computational kernel code and parallelism, I/O etc… support code (infrastructure) are modularized  aligns with ESMF’s “sandwich” architecture. Target hardware – my laptop to largest supercomputers (Columbia, Blue Genes)  it tries to be portable! OSes - linux, HPUX, Solaris, AIX etc… Parallel - MPI parallelism binding, threads parallelism binding (dormant), platform specific parallelism library support e.g active messages, shmem (dormant). Distributed openly on web. Supported through user+developer mailing list, website. Users all over the world.

NSF NCAR | NASA GSFC | DOE LANL ANL | NOAA NCEP GFDL | MIT Outline MITgcm – a very quick overview –algorithmic features –software characteristics Adopting ESMF –strategy –steps Field test applications –MITgcm coupling with everything (including itself!) - interoperating with NCAR, GFDL and UCLA atmosphere models, intermediate complexity coupled system. –high-end parameterization as a coupled problem. Next steps

NSF NCAR | NASA GSFC | DOE LANL ANL | NOAA NCEP GFDL | MIT Adoption strategy Currently only in-house (i.e. ESMF binding not part of default distribution). Practical consideration as many MITgcm user systems do not have ESMF installed. Set of ESMF experiments maintained in MITgcm CVS source repository that we keep up to date with latest ESMF (with one/two week lag). These experiments use –ESMF component model ( init(), run(), finalize() ) –Clocks, configuration attributes, field communications –Primarily sequential mode component execution (more on this later)

NSF NCAR | NASA GSFC | DOE LANL ANL | NOAA NCEP GFDL | MIT Adoption steps – top level Introduction of internal init(), run(), finalize(). Development of couplers (and stub components to test against) –coupler_init(), coupler_run()coupler_init(), coupler_run() Development of drivers –driver_run(), driver_init()driver_run(), driver_init() Code can be seen under CVS repository at mitgcm.org. “MITgcm_contrib/ESMF”

NSF NCAR | NASA GSFC | DOE LANL ANL | NOAA NCEP GFDL | MIT Outline MITgcm very quick overview –algorithmic features –software characteristics Adopting ESMF –strategy –steps Field test applications –MITgcm coupling with everything (including itself!) - interoperating with NCAR, GFDL and UCLA atmosphere models. –high-end parameterization as a coupled problem. Next steps

NSF NCAR | NASA GSFC | DOE LANL ANL | NOAA NCEP GFDL | MIT Field test: M.I.T. General Circulation Model (MITgcm) to NCAR Community Atmospheric Model (CAM). Versions of CAM and MITgcm were adapted to a.have init(), run(), finalize() interfaces b.accept, encode and decode ESMF_state variables A coupler component that maps MITgcm grid to CAM grid was written Runtime steps MITgcm prepares export state. Export state passes through parent to coupler Coupler returns CAM gridded SST array which is passed as import state to CAM gridded component x64 on 1x16 PE’s 180x90 on 1x16 PE’s Kluzek, Hill Uses ESMF_GridComp, ESMF_CplComp and ESMF_Regrid sets of functions.

NSF NCAR | NASA GSFC | DOE LANL ANL | NOAA NCEP GFDL | MIT Versions of MOM and MITgcm were adapted to components a.work within init(), run(), finalize() interfaces b.accept, encode and decode ESMF_state variables A coupler component that maps MITgcm grid to ALI grid was written MITgcm component is substituted for MOM component with MITgcm-ALI coupler Runtime steps MITgcm prepares export state. Export state passes through parent to coupler Coupler returns ALI gridded SST array which is passed to ALI Field test: M.I.T. General Circulation Model (MITgcm) to GFDL Atmosphere/Land/Ice (ALI). 128x60 on 1x16 PE’s 144x90 on 16x1 PE’s Smithline, Zhou, Hill Uses ESMF_GridComp, ESMF_CplComp and ESMF_Regrid sets of functions.

NSF NCAR | NASA GSFC | DOE LANL ANL | NOAA NCEP GFDL | MIT SI experiment: M.I.T. General Circulation Model (MITgcm) ECCO assimilation ocean and POP to UCLA atmosphere. Uses ESMF_GridComp, ESMF_CplComp and ESMF_Regrid sets of functions. Obs. analysis 3 mo. forecast A 3 mo. forecast B

NSF NCAR | NASA GSFC | DOE LANL ANL | NOAA NCEP GFDL | MIT New app: High-end resolution embedding as a coupled problem. For a climate related ocean simulation domain decomposition is limited on the number of processors it can usefully scale to. For ~1 O model maybe no scaling beyond ~64 cpu’s This limit is because parallelism costs (comm overhead, overlap computations) exceed parallelism benefits Question: Are there other things beside ensembles of runs we can do with a thousand+ processor system? Increasing resolution is hard because explicit scheme timesteps drop with resolution – not good for millenial simulations.

NSF NCAR | NASA GSFC | DOE LANL ANL | NOAA NCEP GFDL | MIT New app: High-end resolution embedding as a coupled problem. What about embedding local sub-models, running concurrently on separate processors but coupled to coarse resolution run

NSF NCAR | NASA GSFC | DOE LANL ANL | NOAA NCEP GFDL | MIT New app: High-end resolution embedding as a coupled problem. What about embedding local sub-models, running concurrently on separate processors but coupled to coarse resolution run

NSF NCAR | NASA GSFC | DOE LANL ANL | NOAA NCEP GFDL | MIT Implementation with ESMF ESMF provides nice tools for developing this embedded system –component model abstraction for managing different pieces –parallel regrid/redist provides great tool for N to M coupling. regrid()/redist() precompute data flows at initialization. At each timestep resolving data transport between ~ components is about 15 lines of user code …… sub-components sub-sub-components Top component

NSF NCAR | NASA GSFC | DOE LANL ANL | NOAA NCEP GFDL | MIT MITgcm with ESMF next steps Continued work in house. Directions –Embedding with dynamic balancing –High-resolution ocean and coupled work ESMF in default MITgcm distribution –Most MITgcm user systems do not have ESMF installed yet. This will take time to change – how long? –Hopeful that in the next year this will evolve.

NSF NCAR | NASA GSFC | DOE LANL ANL | NOAA NCEP GFDL | MIT Summary ESMF implementation functionality has grown significantly over last year –optimized regrid/redist scaling –concurrent components Performance is always within factor of 2 of custom code at infrastructure level, at superstructure (code driver, coupling) ESMF overhead is comparable to our own code.

NSF NCAR | NASA GSFC | DOE LANL ANL | NOAA NCEP GFDL | MIT –coupler_init() –coupler_run() BACK

NSF NCAR | NASA GSFC | DOE LANL ANL | NOAA NCEP GFDL | MIT –driver_init() –driver_run() BACK