optimisation package for MITgcm based on m1qn3 with proper reverse communication (code and documentation can be found here: https://who.rocq.inria.fr/Jean-Charles.Gilbert/modulopt/optimization-routines/m1qn3/m1qn3.html)
These are some basic instructions to an optimization of MITgcm/verification/tutorial_global_oce_optim with optim_m1qn3. Some tweaking is definitely possible and not described here.
cd MITgcm/verification
This is just to compile and run the model for testing. I use TAF (just because I have it and OpenAD is a pain to compile on a Mac), but that should not make any difference
./testreport -t tutorial_global_oce_optim -adm -j 4 -ncad
This is the result:
G D M C A F
e p a R o d D
n n k u s G G
2 d e n t r r
Y Y Y Y 15>16< 7 pass tutorial_global_oce_optim (e=0, w=2)
here is the cost function value that I get
(PID.TID 0000.0001) local fc = 0.620023228182336D+01
(PID.TID 0000.0001) global fc = 0.620023228182336D+01
now I download and compile optim_m1qn3
cd ../../
git clone https://github.com/mjlosch/optim_m1qn3.git
cd optim_m1qn3/src
Edit Makefile to adjust to your platform and compiler. It is important that you use the same compiler and compiler flags here as for compiling the MITgcm. This is important to ensure that the binary files written by the MITgcm are read correctly. For me this involves choosing the correct CPP command and setting SUFF=for (because I use MacOS and this is case-insensitive in my case), but you may want to check tutorial_global_oce_optim/build/Makefile to see, what compilers and options you actually used. Pay attention to the values of FC and FFLAGS; the optimization flags in FOPTIM are usually not important.
The file src/Makefile.ARCHER in this repository can be used to compile optim_m1qn3 on ARCHER, the UK national supercomputer.
make depend
make
Then I get executable optim.x
cd ../../MITgcm/verification/tutorial_global_oce_optim/run
cp ../../../../optim_m1qn3/src/optim.x .
Then:
- turn off the gradient check (in data.pkg:
useGrdchk = .FALSE.) - tweak the namelist files data.ctrl and data.optim to the compiler needs (I need a
/to terminate a namelist) - I would replace fmin with dfminFrac = 0.1 (expected reduction of 10%) in
data.optim&OPTIMto be independent of the absolute value of the cost function. (Note: When you run./mitgcmuv_adwith code prior to Apr 12, 2019 with this you need to comment outdfminFrac, because older versions ofmitgcmuv_addid not know about this namelist parameter.) - set
numiter=100,nfunc=10, or some other large value.nfunc*numiteris the number of simulations that are allowed in total. This number should be much larger than numiter, because you may need more than one function call (= run of mitgcmuv_ad) per iteration, see m1qn3 docs for details - add an empty namelist &M1QN3 to data.optim
&M1QN3
/
Ready to run:
./optim.x > opt0.txt
I get a lot of output in opt0.txt, which is easier to read with less -S opt0.out (to truncate long lines); essentially I have the same value for the cost function.
==================================================
Large Scale Optimization with off-line capability.
==================================================
OPTIM_READPARMS: Control options have been read.
OPTIM_READPARMS: Minimization options have been read.
OPTIM_READDATA: Reading cost function and gradient of cost function
for optimization cycle: 0
OPTIM_READDATA: opened file ecco_cost_MIT_CE_000.opt0000
OPTIM_READDATA: nvartype 1
OPTIM_READDATA: nvarlength 2315
OPTIM_READDATA: yctrlid MIT_CE_000
OPTIM_READDATA: filenopt 0
OPTIM_READDATA: fileff 6.2002322818233591
OPTIM_READDATA: fileiG 1
OPTIM_READDATA: filejG 1
OPTIM_READDATA: filensx 2
OPTIM_READDATA: filensy 2
[...]
OPTIM_READDATA: end of optim_readdata
OPTIM_READPARMS: Iteration number = 0
number of control variables = 2315
cost function value in ecco_ctrl = 6.2002322818233591
expected cost function minimum = 5.5802090536410232
expected cost function decrease = 0.62002322818233591
Data will be read from the following file: ecco_ctrl_MIT_CE_000.opt0000
OPTIM_SUB: Calling m1qn3_optim for iteration: 0
OPTIM_SUB: with nn, REAL_BYTE = 2315 4
OPTIM_SUB: read model state
OPTIM_READDATA: Reading control vector
for optimization cycle: 0
OPTIM_READDATA: opened file ecco_ctrl_MIT_CE_000.opt0000
OPTIM_READDATA: nvartype 1
OPTIM_READDATA: nvarlength 2315
OPTIM_READDATA: yctrlid MIT_CE_000
OPTIM_READDATA: filenopt 0
OPTIM_READDATA: fileff 6.2002322818233591
OPTIM_READDATA: fileiG 1
OPTIM_READDATA: filejG 1
OPTIM_READDATA: filensx 2
OPTIM_READDATA: filensy 2
OPTIM_READDATA: end of optim_readdata
OPTIM_READDATA: Reading cost function and gradient of cost function
for optimization cycle: 0
OPTIM_READDATA: opened file ecco_cost_MIT_CE_000.opt0000
OPTIM_READDATA: nvartype 1
OPTIM_READDATA: nvarlength 2315
OPTIM_READDATA: yctrlid MIT_CE_000
OPTIM_READDATA: filenopt 0
OPTIM_READDATA: fileff 6.2002322818233591
OPTIM_READDATA: fileiG 1
OPTIM_READDATA: filejG 1
OPTIM_READDATA: filensx 2
OPTIM_READDATA: filensy 2
OPTIM_READDATA: end of optim_readdata
OPTIM_SUB after reading ecco_ctrl and ecco_cost:
OPTIM_SUB nn = 2315
OPTIM_SUB objf = 6.2002322818233591
OPTIM_SUB xx(1) = 0.0000000000000000
OPTIM_SUB adxx(1) = -6.7879882408306003E-005
OPTIM_SUB: cold start, optimcycle = 0
OPTIM_SUB: call m1qn3_offline ........
OPTIM_SUB: ...........................
OPTIM_SUB: returned from m1qn3_offline
OPTIM_SUB: nn = 2315
OPTIM_SUB: xx(1) = 0.51934864251896229 0.73000729032300782
OPTIM_SUB: adxx(1) = -6.7879882408306003E-005 -9.5413379312958568E-005
OPTIM_SUB: omode = -1
OPTIM_SUB: niter = 1
OPTIM_SUB: nsim = 10000
OPTIM_SUB: reverse = 1
OPTIM_SUB: mean(xx) = 0.16365068483545642
OPTIM_SUB: max(xx) = 4.6525355815045790
OPTIM_SUB: min(xx) = -9.3326211896764324
OPTIM_SUB: std(xx) = 15.613450260548481
OPTIM_STORE_M1QN3: saving the state of m1qn3 in OPWARM.opt0001
OPTIM_SUB: writing 2315 sized control to file ecco_ctrl
OPTIM_WRITEDATA: Writing new control vector to file(s)
for optimization cycle: 1
OPTIM_WRITEDATA: nvartype 1
OPTIM_WRITEDATA: nvarlength 2315
OPTIM_WRITEDATA: yctrlid MIT_CE_000
OPTIM_WRITEDATA: nopt 1
OPTIM_WRITEDATA: ff -9999.0000000000000
OPTIM_WRITEDATA: iG 1
OPTIM_WRITEDATA: jG 1
OPTIM_WRITEDATA: nsx 2
OPTIM_WRITEDATA: nsy 2
OPTIM_WRITEDATA: end of optim_writedata, icvoffset 2315
======================================
Large Scale Optimization run finished.
======================================
ff = -9999 is an intentional dummy value.
Now you can organize the rest in a loop. In bash, it could look like this:
first comment out dfminFrac, if mitgcmuv_ad does not know about it (see above); dfminFrac is really only needed in the zeroth iteration.
# tabula rasa:
rm ecco_c* OPWARM.* m1qn3_output.txt
#
myiter=0
cat > data.optim <<EOF #
# ********************************
# Off-line optimization parameters
# ********************************
&OPTIM
optimcycle=${myiter},
numiter=10,
nfunc=10,
dfminFrac = 0.1,
iprint=10,
nupdate=8,
/
&M1QN3
/
EOF
while (( $myiter < 20 ))
do
# formatter iteration count
it=`echo $myiter | awk '{printf "%03i",$1}'`
echo "iteration ${myiter}"
# increment counter in data.optim
sed -i .${it} "s/.*optimcycle.*/ optimcycle=${myiter},/" data.optim
./mitgcmuv_ad > output${it}.txt
./optim.x > opt${it}.txt
m1qn3out=`grep "m1qn3: output mode" m1qn3_output.txt`
if test "x${m1qn3out}" != x; then
echo "m1qn3 has finished"
break
fi
# increase counter for next iteration
((myiter++))
done
grep "fc " output001.txt
(PID.TID 0000.0001) early fc = 0.000000000000000D+00
(PID.TID 0000.0001) local fc = 0.132325873958879D+02
(PID.TID 0000.0001) global fc = 0.132325873958879D+02
grep "global fc " output???.txt
output000.txt:(PID.TID 0000.0001) global fc = 0.620023228182336D+01
output001.txt:(PID.TID 0000.0001) global fc = 0.132325873958879D+02
output002.txt:(PID.TID 0000.0001) global fc = 0.615567811433600D+01
output003.txt:(PID.TID 0000.0001) global fc = 0.615556878869932D+01
output004.txt:(PID.TID 0000.0001) global fc = 0.615547009131471D+01
As you can see, the improvement is not very good after the initial steps, and the optimization will not be successful in the end, i.e. satisfy the tolerance set by epsg. This is likely because the number of timesteps in data is small (20) for this test. Try longer intergations (e.g. 1 year) and wait longer. Check out m1qn3_output.txt, which records the output of m1qn3. The python script plotfc.py greps the cost function values out of m1qn3_ouput.txt and plots them and the number of simulations per iteration; the latter typically increases with decreasing cost function.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent