[ase-users] Parallelization over images with arbitrary calculator

[Benedikt Ziebarth]

Hi there,

I am looking for a way to start a NEB calculation with image 
parallelization using  siesta.
In my calculations 6 CPUs are distributed over 3 images, therefore each  
image should run with 2 CPUs.
Since the siesta calculator does not directly support the 
parallelization, I was trying a work around by starting the neb-python 
script using

mpirun -np 3 neb.py

and  in  "run_siesta.py"  I was changing the way how  siesta is started

from "siesta" to  "mpirun -np 2 siesta"

the neb.py starts up correctly but when siesta is initialized, it just 
crashed  with the error message  "OOB: Connection to HNP lost" and no 
further information or error message. The siesta output is empty.

If I dont change "siesta" to "mpirun -np 2 siesta" it runs fine.


Cheers and thanks for any help in advance
Benedikt Ziebarth




import mpi4py
from ase import *
import ase.io as io
from ase.calculators.siesta import Siesta
import time
from ase.optimize import MDMin
import os
from ase.neb import NEB
from ase.parallel import rank, size
from ase.io.trajectory import PickleTrajectory
import time



initial = io.read('init.traj')
final = io.read('final.traj')

numimages=3
print size
print rank
assert numimages == size


images = [initial]
calc=['z']*numimages
for i in range(numimages):
   print calc[i]
   calc[i]=Siesta(label='IMAGE_%d'%i,\
               xc='PBE',\
               meshcutoff=200 * 13.6,\
               basis='dzp',\
               kpts=[1,1,4])
   calc[i].set_fdf('Diag.ParallelOverK',True)
for i in range(numimages):
     image = initial.copy()
     if i == rank:
         image.set_calculator(calc[i])

     images.append(image)
images.append(final)
time.sleep(rank*1) #needed to avoid some copy errors of the pseudo 
potential files
neb = NEB(images, parallel=True)
neb.interpolate()
qn = MDMin(neb)

time.sleep(rank*1) #needed to avoid some copy errors of the pseudo 
potential files
traj = PickleTrajectory('neb%d.traj' % rank, 'w', images[1 + rank], 
master=True)
qn.attach(traj)
qn.run(fmax=0.05)