[MITgcm-support] tutorial_global_oce_optim optimisation failed

Andrew McRae andrew.mcrae at physics.ox.ac.uk
Thu Jun 21 06:31:37 EDT 2018 

Okay, thanks, I'll give this a try.

I read your earlier email more closely and realised this was exactly the
problem I had a few weeks later!  I should read more carefully...

"I am still not sure what makes openAD decide if active_var is type(active)
or real." -- abstractly, any variable that is both dependent on the
independent variable xx_hfluxm



*# ifdef ALLOW_HFLUXM_CONTROLc$openad INDEPENDENT(xx_hfluxm)# endif*

and is a dependency of the dependent variable fc




*# ifdef ALLOW_OPENADc$openad DEPENDENT(fc)# endif /* ALLOW_OPENAD */*

should be turned into type(active).

Andrew

On 21 June 2018 at 10:15, Ron Goldman <ron at ocean.org.il> wrote:

> Hi Andrew,
> It compiled, and grdchk returned output that matched the finite
> difference. I recall that optim reduced the norm by little but I don't
> recall if the change in OPENAD_OPTIONS.h was needed for that.
> Ron
>
>
> On 06/21/18 10:22, Andrew McRae wrote:
>
> Hi Ron,
>
> "It worked" = it compiled, or it compiled + everything now seems to work
> (including the optimization)?
>
> Andrew
>
> On 21 June 2018 at 05:57, Ron Goldman <ron at ocean.org.il> wrote:
>
>> Hi Andrew,
>> I've been having the same issue. It worked when I changed the code by
>> dropping the %v %d.
>> Changing tools/OAD_support/ad_template.active_read_xy.F will propagate
>> the changes to externalDummies_cb2m_oad.f.
>> I am still not sure what makes openAD decide if active_var is
>> type(active) or real.
>> Best reagrds,
>> Ron
>>
>>
>> On 06/20/18 20:28, Andrew McRae wrote:
>>
>> Damn.  After doing this, the gradient written into ecco_cost seems to be
>> all 0.0.  Help?
>>
>> Andrew
>>
>> On 19 June 2018 at 15:37, Andrew McRae <andrew.mcrae at physics.ox.ac.uk>
>> wrote:
>>
>>> Okay, I have
>>> 1) copied OPENAD_OPTIONS.h from pkg/openad to the code_oad/ subfolder of
>>> the tutorial, changing it to define ALLOW_OPENAD_ACTIVE_READ_XY
>>>
>>> Good news: the main body of tools/OAD_support/ad_template.active_read_xy.F
>>> (which is wrapped in #ifdef ALLOW_OPENAD_ACTIVE_READ_XY) now appears in
>>> external_Dummies_cb2m_oad.f
>>>
>>> Bad news: this gives a compile error in externalDummies_cb2m_oad.f of
>>> "Error: Unexpected '%' for nonderived-type variable 'active_var'".  This
>>> seems to be because active_var is declared as a REAL(w2f__8) in
>>> externalDummies_cb2m_oad.f, not a type(active).  The lines of code
>>> corresponding to
>>>
>>>       active_var = dummy + active_var
>>>       dummy = active_var(1,1,1,1) + dummy
>>>
>>> don't appear in the post-processed code [optimized out by the OpenAD
>>> toolchain, or something else?], which is probably why active_var doesn't
>>> become an active variable.  Therefore, I....
>>>
>>> 2) change the type of active_var to type(active) in the post-processed
>>> file (yuck).  make adAll continues from where it left off, and mitgcmuv_ad
>>> now compiles :)
>>>
>>> (I tried changing the type of this variable in
>>> pkg/openad/externalDummies.F
>>> <https://github.com/MITgcm/MITgcm/blob/master/pkg/openad/externalDummies.F#L285>,
>>> but this leads to a bork in the OpenAD toolchain)
>>>
>>> I can confirm the cost function changes from iteration to iteration, and
>>> I'll now test if the optimization works.  Hopefully you can find a more
>>> permanent solution to the above.
>>>
>>> Andrew
>>>
>>> On 19 June 2018 at 13:43, Andrew McRae <andrew.mcrae at physics.ox.ac.uk>
>>> wrote:
>>>
>>>> The active_read_xy routine used in OpenAD mode looks suspicious:
>>>> https://github.com/MITgcm/MITgcm/blob/master/pkg/openad/exte
>>>> rnalDummies.F#L269-L296
>>>>
>>>> 1) ALLOW_OPENAD_ACTIVE_READ_XY isn't defined for
>>>> tutorial_global_oce_optim; I guess it should be?
>>>>
>>>> 2) This routine seems to be basically a no-op anyway?  I guess
>>>> active_var_file should be read into active_var, or similar?
>>>>
>>>> Andrew
>>>>
>>>> On 18 June 2018 at 18:04, Andrew McRae <andrew.mcrae at physics.ox.ac.uk>
>>>> wrote:
>>>>
>>>>> Not sure if you've had a chance to look at this yet... the only time I
>>>>> can see tmpfld2d being written to (and not just initialised to 0.0 or 1.0)
>>>>> is in pkg/admtlm/bypassad.F line 96.  Presumably that package isn't
>>>>> switched on here.  I can't see xx_hfluxm being written to at all.
>>>>>
>>>>> A few lines above, active_read_xy is called with xx_hfluxm_dummy as
>>>>> the last argument... should this have been xx_hfluxm, perhaps?
>>>>> (xx_hfluxm_dummy is a single variable, while xx_hfluxm is an array, so this
>>>>> probably won't work as-is...)
>>>>>
>>>>> Andrew
>>>>>
>>>>> On 13 June 2018 at 23:18, Andrew McRae <andrew.mcrae at physics.ox.ac.uk>
>>>>> wrote:
>>>>>
>>>>>> Okay, thank you.  If do you have any advice on debugging this, do
>>>>>> say.  I guess you already got as far as spotting that all the terms on the
>>>>>> RHS of https://github.com/MITgcm/MITgcm/blob/master/pkg/ctrl/ctrl_m
>>>>>> ap_forcing.F#L259 are zero.
>>>>>>
>>>>>> Andrew
>>>>>>
>>>>>> On 13 June 2018 at 21:36, Patrick Heimbach <heimbach at mit.edu> wrote:
>>>>>>
>>>>>>> Andrew,
>>>>>>>
>>>>>>> I have not been able to look into this due to various other
>>>>>>> commitments over the last couple of months.
>>>>>>>
>>>>>>> I'll be grounded for a while in Austin starting next week, and this
>>>>>>> will be near the top of my ToDo list.
>>>>>>>
>>>>>>> Patrick
>>>>>>>
>>>>>>> > On Jun 13, 2018, at 12:56 PM, Andrew McRae <
>>>>>>> andrew.mcrae at physics.ox.ac.uk> wrote:
>>>>>>> >
>>>>>>> > MITgcm built with OpenAD is not making use of the ecco_ctrl files
>>>>>>> for optimcycle >= 1.  The file apparently gets read in, but the contents
>>>>>>> get dropped on the floor somewhere.
>>>>>>> >
>>>>>>> > Andrew
>>>>>>> >
>>>>>>> > On 13 June 2018 at 18:51, Matthew Mazloff <mmazloff at ucsd.edu>
>>>>>>> wrote:
>>>>>>> > Hello
>>>>>>> >
>>>>>>> > Sorry, I lost track. What needs to be debugged? Can you please
>>>>>>> reiterate the problem?
>>>>>>> >
>>>>>>> > Thanks
>>>>>>> > Matt
>>>>>>> >
>>>>>>> >
>>>>>>> >> On Jun 13, 2018, at 10:14 AM, Andrew McRae <
>>>>>>> andrew.mcrae at physics.ox.ac.uk> wrote:
>>>>>>> >>
>>>>>>> >> Hi Patrick,
>>>>>>> >>
>>>>>>> >> Were you able to make any progress with this?  If not, do you
>>>>>>> have any advice on debugging this?  (I'm getting lost in ctrl_unpack as to
>>>>>>> which variable the control vector is even read into)
>>>>>>> >>
>>>>>>> >> Thanks,
>>>>>>> >> Andrew
>>>>>>> >>
>>>>>>> >> On 5 May 2018 at 20:12, Patrick Heimbach <heimbach at mit.edu>
>>>>>>> wrote:
>>>>>>> >> A quick update:
>>>>>>> >>
>>>>>>> >> This tutorial works as advertised (in the manual), but not as
>>>>>>> "hoped".
>>>>>>> >> What I mean is that it has been developed and only ever fully
>>>>>>> tested and used  in optimization mode with TAF-generated code (and that's
>>>>>>> what's documented in the manual).
>>>>>>> >>
>>>>>>> >> Of course, it should not make a difference of whether we use TAF
>>>>>>> vs. OpenAD as long as gradients are correct. But as it turns out, with the
>>>>>>> OpenAD code there appears to be a little glitch. Gradient seems correct,
>>>>>>> and iteration 1 update is properly read in, but then not used (instead it
>>>>>>> is reset to zero). Oh well. I'll need to check where that happens, so stay
>>>>>>> tuned.
>>>>>>> >>
>>>>>>> >> p.
>>>>>>> >>
>>>>>>> >> > On May 4, 2018, at 10:11 AM, Andrew McRae <
>>>>>>> andrew.mcrae at physics.ox.ac.uk> wrote:
>>>>>>> >> >
>>>>>>> >> > And, still no luck(?)
>>>>>>> >> >
>>>>>>> >> > Running for a year (switching the commented and uncommented
>>>>>>> nTimeSteps and lastinterval declarations in data and data.cost), optim.x
>>>>>>> (lsopt+optim, not optim_m1qn3) now gives the output
>>>>>>> >> >
>>>>>>> >> >   cost function............... 0.60514949E+01
>>>>>>> >> >   norm of x................... 0.00000000E+00
>>>>>>> >> >   norm of g................... 0.23235517E+00
>>>>>>> >> >
>>>>>>> >> >   optimization stopped because :
>>>>>>> >> >   ifail =   4    the search direction is not a descent one
>>>>>>> >> >
>>>>>>> >> >
>>>>>>> >> > On 4 May 2018 at 13:58, Andrew McRae <
>>>>>>> andrew.mcrae at physics.ox.ac.uk> wrote:
>>>>>>> >> > On 4 May 2018 at 06:04, Patrick Heimbach <heimbach at mit.edu>
>>>>>>> wrote:
>>>>>>> >> > Hi Matt,
>>>>>>> >> >
>>>>>>> >> > as you indicated, all is still good, and I suspect the same you
>>>>>>> did regarding what might be at issue.
>>>>>>> >> >
>>>>>>> >> > I just downloaded latest MITgcm, re-ran adjoint, and conducted
>>>>>>> 2 iterations (using lsopt).
>>>>>>> >> >
>>>>>>> >> > It still works "out of the box" ... if one realizes that a
>>>>>>> manual is part of that "box", and section 3.18 (old manual prior to
>>>>>>> readthedocs) has some description of this tutorial, thanks to dfer
>>>>>>> (admittedly somewhat out of date, but still mostly relevant). In particular
>>>>>>> it says there that the optimization has been conducted for a 1-year
>>>>>>> simulation.
>>>>>>> >> >
>>>>>>> >> > Okay, thanks.  I interpreted the manual footnote as "running a
>>>>>>> 1-year simulation will reproduce the scientifically-interesting graphs in
>>>>>>> the manual", not as "the default parameters are only useful for verifying
>>>>>>> correctness of the adjoint, but will break the optimisation routine".  I'll
>>>>>>> see if I have more success with the longer run.
>>>>>>> >> >
>>>>>>> >> >
>>>>>>> >> > Since we do not want to conduct 1-year integrations for *any*
>>>>>>> of the tutorials within our regression tests (these tests consist of 90
>>>>>>> forward, 24 adjoint/TAF, 10 adjoint/OpenAD, and 16 tangent-linear/TAF
>>>>>>> configurations, each needing to be compiled and executed) we have shortened
>>>>>>> the number of time steps to 10 (= 10 days) to perform efficient nightly
>>>>>>> regression tests of the adjoint. Not changing the number of time steps
>>>>>>> leads to optimizing in the noise - in fact cost function goes up in that
>>>>>>> case.
>>>>>>> >> >
>>>>>>> >> > That the user's cost function does not change at all suggests a
>>>>>>> more basic problem though (hard to speculate what it might be).
>>>>>>> >> >
>>>>>>> >> > I made a quick test by extending nTimeSteps from 10 to 90 days,
>>>>>>> which leads to cost reduction as desired, namely, for:
>>>>>>> >> >  numiter=1,
>>>>>>> >> >  nfunc=3,
>>>>>>> >> >  fmin=5.74,
>>>>>>> >> > (values in data.optim that comes with tutorial_global_oce_optim)
>>>>>>> >> > I obtain following costs:
>>>>>>> >> > iter. 0: fc =  0.184199260445164D+02
>>>>>>> >> > iter. 1: fc =  0.130860446841901D+02
>>>>>>> >> > iter. 2: fc =  0.979374136987667D+01
>>>>>>> >> >
>>>>>>> >> > I did that test "by hand", i.e. not using the script cycsh also
>>>>>>> provided (see manual). Doing so by hand requires two more lines in
>>>>>>> data.ctrl:
>>>>>>> >> >  &CTRL_PACKNAMES
>>>>>>> >> >  costname='ecco_cost',
>>>>>>> >> >  ctrlname='ecco_ctrl',
>>>>>>> >> >
>>>>>>> >> > Since gradients produced with TAF are extremely similar (10+
>>>>>>> digits?) to those produce with OpenAD (see results/ directory which has
>>>>>>> both TAF and OpenAD reference results), I expect it to work with OpenAD too
>>>>>>> (have not tested it right now).
>>>>>>> >> >
>>>>>>> >> > -Patrick
>>>>>>> >> >
>>>>>>> >> >
>>>>>>> >> >
>>>>>>> >> > > On May 2, 2018, at 12:34 PM, Andrew McRae <
>>>>>>> andrew.mcrae at physics.ox.ac.uk> wrote:
>>>>>>> >> > >
>>>>>>> >> > > Thanks for this.
>>>>>>> >> > >
>>>>>>> >> > > Just as a sanity check, before I involve optim_m1qn3 again,
>>>>>>> the output of my ./testreport -t tutorial_global_oce_optim -oad includes
>>>>>>> >> > >
>>>>>>> >> > > There were 16 decimal places of similarity for "ADM CostFct"
>>>>>>> >> > > There were 16 decimal places of similarity for "ADM Ad Grad"
>>>>>>> >> > > There were 0 decimal places of similarity for "ADM FD Grad"
>>>>>>> >> > >
>>>>>>> >> > > Should I be concerned about this?
>>>>>>> >> > >
>>>>>>> >> > > E.g. lines 2116-2118 of my output_oadm.txt file are
>>>>>>> >> > >
>>>>>>> >> > > (PID.TID 0000.0001)  ADM  ref_cost_function      =
>>>>>>> 6.20023228182329E+00
>>>>>>> >> > > (PID.TID 0000.0001)  ADM  adjoint_gradient       =
>>>>>>> -2.69091500991183E-06
>>>>>>> >> > > (PID.TID 0000.0001)  ADM  finite-diff_grad       =
>>>>>>> 0.00000000000000E+00
>>>>>>> >> > >
>>>>>>> >> > > But at least my cost function value is the same:
>>>>>>> >> > >
>>>>>>> >> > > (PID.TID 0000.0001)   local fc =  0.620023228182329D+01
>>>>>>> >> > > (PID.TID 0000.0001)  global fc =  0.620023228182329D+01
>>>>>>> >> > >
>>>>>>> >> > > Andrew
>>>>>>> >> > >
>>>>>>> >> > > On 2 May 2018 at 10:34, Martin Losch <Martin.Losch at awi.de>
>>>>>>> wrote:
>>>>>>> >> > > Hi Andrew,
>>>>>>> >> > >
>>>>>>> >> > > I won’t be able to help you much with the optim/lsopt code,
>>>>>>> because I would have to get it running again myself. But I do recommend
>>>>>>> using the MITgcm_contrib/mlosch/optim_m1qn3 code. It’s not very
>>>>>>> well documented, but I am attaching a skeleton script to illustrate how to
>>>>>>> use it. Please give it a try and if you find it useful, I can add this
>>>>>>> script to the repository.
>>>>>>> >> > >
>>>>>>> >> > > The two versions of the optimization routine are similar,
>>>>>>> both implement the same optimization algorithm (BFGS), but optim_m1qn3 uses
>>>>>>> a later version of the m1qn3 code, I think it’s easier to compile (only one
>>>>>>> Makefile) and I believe (but there’s debate about this) that it does the
>>>>>>> right thing as opposed to the optim/lsopt variant, which somehow truncates
>>>>>>> the optimization in each iteration. Having said that, I have used both in
>>>>>>> parallel, and the reduction of the cost function (which is really all we
>>>>>>> care about) is sometimes better with the optim_m1qn3 code, sometimes it is
>>>>>>> better with the optim/lsopt code. The optim_m1qn3 code is closer to the
>>>>>>> idea of the original m1qn3 code.
>>>>>>> >> > >
>>>>>>> >> > > Let me know if you can use my attached instructions.
>>>>>>> >> > >
>>>>>>> >> > > Martin
>>>>>>> >> > >
>>>>>>> >> > >
>>>>>>> >> > >
>>>>>>> >> > > > On 1. May 2018, at 00:00, Andrew McRae <
>>>>>>> andrew.mcrae at physics.ox.ac.uk> wrote:
>>>>>>> >> > > >
>>>>>>> >> > > > Right, but the cost function is the same value each time,
>>>>>>> the norm of x is 0 each time, and the norm of g is the same each time.
>>>>>>> This suggests nothing is happening.  It's a bit ridiculous that one of the
>>>>>>> core tutorials simply isn't working out of the box...
>>>>>>> >> > > >
>>>>>>> >> > > > I will have a go at debugging.
>>>>>>> >> > > >
>>>>>>> >> > > > Andrew
>>>>>>> >> > > >
>>>>>>> >> > > > On 30 April 2018 at 22:54, Matthew Mazloff <
>>>>>>> mmazloff at ucsd.edu> wrote:
>>>>>>> >> > > > Well you are correct that its not actually taking a step
>>>>>>> because the dot product of the control is 0:
>>>>>>> >> > > >>> norm of x................... 0.00000000E+00
>>>>>>> >> > > > meaning the controls are all 0 still.
>>>>>>> >> > > >
>>>>>>> >> > > > However the gradients are non-zero
>>>>>>> >> > > >>> norm of g................... 0.12730927E-01
>>>>>>> >> > > > so the linesearch should step and
>>>>>>> >> > > > ecco_ctrl_MIT_CE_000.opt0001
>>>>>>> >> > > > should not be all zero.
>>>>>>> >> > > >
>>>>>>> >> > > > To debug this you could put a print statement in
>>>>>>> optim_writedata.F to see what it is writing…..
>>>>>>> >> > > >
>>>>>>> >> > > > I don’t know enough about this tutorial to be a bigger
>>>>>>> help, sorry
>>>>>>> >> > > >
>>>>>>> >> > > > Matt
>>>>>>> >> > > >
>>>>>>> >> > > >
>>>>>>> >> > > >> On Apr 30, 2018, at 2:50 PM, Andrew McRae <
>>>>>>> andrew.mcrae at physics.ox.ac.uk> wrote:
>>>>>>> >> > > >>
>>>>>>> >> > > >> Yes, I did.
>>>>>>> >> > > >>
>>>>>>> >> > > >> On 30 April 2018 at 22:42, Matthew Mazloff <
>>>>>>> mmazloff at ucsd.edu> wrote:
>>>>>>> >> > > >> This is still iteration 0. You have to update data.optim
>>>>>>> to tell it you are now at iteration 1
>>>>>>> >> > > >>
>>>>>>> >> > > >> Matt
>>>>>>> >> > > >>
>>>>>>> >> > > >>
>>>>>>> >> > > >>> On Apr 30, 2018, at 2:38 PM, Andrew McRae <
>>>>>>> andrew.mcrae at physics.ox.ac.uk> wrote:
>>>>>>> >> > > >>>
>>>>>>> >> > > >>> I tried a few steps of this, but the output of optim.x
>>>>>>> always has
>>>>>>> >> > > >>>
>>>>>>> >> > > >>>   cost function............... 0.62002323E+01
>>>>>>> >> > > >>>   norm of x................... 0.00000000E+00
>>>>>>> >> > > >>>   norm of g................... 0.12730927E-01
>>>>>>> >> > > >>>
>>>>>>> >> > > >>> near the end, with no decrease in the cost function.  So
>>>>>>> I guess it's not actually taking the step?
>>>>>>> >> > > >>>
>>>>>>> >> > > >>> Andrew
>>>>>>> >> > > >>>
>>>>>>> >> > > >>> On 27 April 2018 at 18:04, Andrew McRae <
>>>>>>> andrew.mcrae at physics.ox.ac.uk> wrote:
>>>>>>> >> > > >>> !!!  Okay...
>>>>>>> >> > > >>>
>>>>>>> >> > > >>> Yes, it produced the .opt0001 file.  I'll see how this
>>>>>>> goes.
>>>>>>> >> > > >>>
>>>>>>> >> > > >>> Thanks,
>>>>>>> >> > > >>> Andrew
>>>>>>> >> > > >>>
>>>>>>> >> > > >>> On 27 April 2018 at 17:57, Matthew Mazloff <
>>>>>>> mmazloff at ucsd.edu> wrote:
>>>>>>> >> > > >>> Hello
>>>>>>> >> > > >>>
>>>>>>> >> > > >>> Its been awhile, but I am pretty sure that is the normal
>>>>>>> output. It says “fail", but it did give you a new and
>>>>>>> ecco_ctrl_MIT_CE_000.opt0001 (correct?) and if you unpack and run likely
>>>>>>> the cost will descend.
>>>>>>> >> > > >>>
>>>>>>> >> > > >>> I think it worked correctly. lsopt/optim are just
>>>>>>> confusing…but I think its working. I think all is good!
>>>>>>> >> > > >>>
>>>>>>> >> > > >>> Matt
>>>>>>> >> > > >>>
>>>>>>> >> > > >>>
>>>>>>> >> > > >>>
>>>>>>> >> > > >>>> On Apr 27, 2018, at 8:25 AM, Andrew McRae <
>>>>>>> andrew.mcrae at physics.ox.ac.uk> wrote:
>>>>>>> >> > > >>>>
>>>>>>> >> > > >>>> Just separating this from the other thread, I got the
>>>>>>> bundled MITgcm optim routine built (having made these changes, based on
>>>>>>> this thread from 2010 and this one from 2016).
>>>>>>> >> > > >>>>
>>>>>>> >> > > >>>> I use OpenAD to create the adjoint.
>>>>>>> >> > > >>>>
>>>>>>> >> > > >>>> My steps are:
>>>>>>> >> > > >>>> 1) in the build directory, run ../../../tools/genmake2
>>>>>>> -oad -mods=../code_oad
>>>>>>> >> > > >>>> 2) run make depend and make adAll
>>>>>>> >> > > >>>> 3) copy input_oad/ into a new folder scratch/
>>>>>>> >> > > >>>> 4) within scratch/, run ./prepare_run
>>>>>>> >> > > >>>> 5) copy mitgcmuv_ad from build/ into scratch/, copy
>>>>>>> optim.x into scratch/OPTIM/
>>>>>>> >> > > >>>> 6) run ./mitgcmuv_ad
>>>>>>> >> > > >>>> 7) in scratch/OPTIM, create symlinks to ../data.optim
>>>>>>> and ../data.ctrl
>>>>>>> >> > > >>>> 8) copy the files ecco_cost_MIT_CE_000.opt0000 and
>>>>>>> ecco_ctrl_MIT_CE_000.opt0000 into the OPTIM subdirectory
>>>>>>> >> > > >>>> 9) run ./optim.x within the subdirectory
>>>>>>> >> > > >>>>
>>>>>>> >> > > >>>> The full output is attached, but I assume the
>>>>>>> optimisation failed since the last lines are
>>>>>>> >> > > >>>>
>>>>>>> >> > > >>>>   optimization stopped because :
>>>>>>> >> > > >>>>   ifail =   4    the search direction is not a descent
>>>>>>> one
>>>>>>> >> > > >>>>
>>>>>>> >> > > >>>> Any ideas?  (I guess this isn't something that is tested
>>>>>>> in the daily builds?)
>>>>>>> >> > > >>>>
>>>>>>> >> > > >>>> In the meantime, I'll try the m1qn3 routine as in the
>>>>>>> other thread, which should help distinguish between a problem with the
>>>>>>> optimisation routine or the gradient generated by mitgcmuv_ad.
>>>>>>> >> > > >>>>
>>>>>>> >> > > >>>> Andrew
>>>>>>> >> > > >>>> <out.txt>_______________________________________________
>>>>>>> >> > > >>>> MITgcm-support mailing list
>>>>>>> >> > > >>>> MITgcm-support at mitgcm.org
>>>>>>> >> > > >>>> http://mailman.mitgcm.org/mail
>>>>>>> man/listinfo/mitgcm-support
>>>>>>> >> > > >>>
>>>>>>> >> > > >>>
>>>>>>> >> > > >>>
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