NSF NCAR | NASA GSFC | DOE LANL ANL | NOAA NCEP GFDL | MIT Adoption and field tests of M.I.T General...

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


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


MITgcm algorithmic characteristics• General orthogonal curvilinear coordinate finite-volume dynamical kernel. • Flexible, scalable domain decomposition 1CPU 2000+ 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 http://mitgcm.org


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.


Outline

• MITgcm – a very quick overview– algorithmic features– software characteristics



interoperating with NCAR, GFDL and UCLA atmosphere models, intermediate complexity coupled system.

– high-end parameterization as a coupled problem.

• Next steps


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)


Adoption steps – top level• Introduction of internal init(), run(), finalize().

• Development of couplers (and stub components to test against)– coupler_init(), coupler_run()

• Development of drivers – driver_run(), driver_init()

• Code can be seen under CVS repository at mitgcm.org. “MITgcm_contrib/ESMF”


Outline• MITgcm very quick overview

– algorithmic features– software characteristics



interoperating with NCAR, GFDL and UCLA atmosphere models.

– high-end parameterization as a coupled problem.• Next steps


Field test: M.I.T. General Circulation Model (MITgcm) to NCAR Community Atmospheric Model (CAM).

Versions of CAM and MITgcm were adapted toa. have init(), run(), finalize() interfacesb. accept, encode and decode ESMF_state variables

A coupler component that maps MITgcm grid to CAM grid was written

Runtime stepsRuntime stepsMITgcm prepares export state.

Export statepasses throughparent to coupler

Coupler returnsCAM gridded SST array which ispassed as importstate to CAMgriddedcomponent.

1

2

3

128x64on 1x16 PE’s

180x90on 1x16 PE’s

Kluzek,Hill

•Uses ESMF_GridComp, ESMF_CplComp and ESMF_Regrid sets of functions.


Versions of MOM and MITgcm were adapted to componentsa. work within init(), run(), finalize() interfacesb. accept, encode and decode ESMF_state variables

A coupler component that maps MITgcm grid to ALI grid was writtenMITgcm component is substituted for MOM component with MITgcm-ALI coupler

Runtime stepsRuntime stepsMITgcm prepares export state.

Export statepasses throughparent to coupler

Coupler returnsALI gridded SST array which ispassed to ALI.

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2

3

Field test: M.I.T. General Circulation Model (MITgcm) to GFDL Atmosphere/Land/Ice (ALI).

128x60on 1x16 PE’s

144x90on 16x1 PE’s

Smithline, Zhou, Hill



SI experiment: M.I.T. General Circulation Model (MITgcm) ECCO assimilation ocean and POP to UCLA

atmosphere.


Obs. analysis

3 mo.forecast A

3 mo.forecast B


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 ~1O model maybe no scaling beyond ~64 cpu’s

This limit is because parallelism costs (comm overhead, overlap computations) exceed parallelism benefits.

0 1 23 4 5 6 7

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.


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.

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65666768

319


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 ~300-400 components is about 15 lines of user code.

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64656667

316317318319

……sub-components

sub-sub-components

Top component


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.


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.


– coupler_init()

– coupler_run()

BACK


– driver_init()

– driver_run()

BACK

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