iLand cluster

This page describes a possible set up (and step by step instructions) for running multiple iLand simulations on a compute cluster. Here we assume a cluster infrastructure based on Linux.

iLand software


  • get the iLand C++ source code and copy to the server (we assume a folder iland)
  • create a build folder within the iland folder
  • build the console version of iLand on the command prompt (see below). Prerequsites are a viable compiler (e.g., gcc, icc) and the Qt packages (see for example http://doc.qt.io/qt-5/linux.html). Details of the setup process will probably differ and depend on the specific details of the cluster.

cd plugins
rm -r *
qmake ../../src/plugins/plugins.pro
cd ..

cd ilandc
rm -r *
qmake ../../src/ilandc/ilandc.pro
cd ..


  • on success, iLand can be run:

wrammer@l33 build$ ilandc/ilandc
iLand console (1.04 - #1306)
This is the console version of iLand, the individual based
landscape and disturbance forest model.
More at: http://iland-model.org
(c) Werner Rammer, Rupert Seidl, 2009-2017
compiled: Intel 64 bit Qt 5.8.0

ilandc.exe <xml-project-file> <...other options>
you specify a number key=value pairs, and *after* loading of the project
the 'key' settings are set to 'value'. E.g.: ilandc project.xml 100 output.stand.enabled=false output.stand.landscape=false
See also http://iland-model.org/iLand+console </xm


setup of the simulation project

An iLand project is typically a folder that contains the main project file and all necessary files (e.g., climate data, data for the initialization of the vegetation, ...). Usually, the project folder can be simply copied from the desktop computer to the cluster.
A typical simulation project will consist of multiple simulations, for example a setup of a simulation study on a single landscape could include:

  • 5 different climate scenarios
  • 4 different management scenarios
  • simulations with and without natural disturbances
  • replications of each factor combination (to include effects of stochasticity), e.g. 20 replicates

In this example, a total of 5 x 4 x 2 x 20 = 800 simulations are required. One practical way to define the whole simulation job is to create a table with one row for each simulation. Here is an example for such a 'master' file:



The "id" is a unique Id for a simulation, and the columns "climate", "disturbance", and "management" define the detailed settings for each run. The details depend of course on the specific simulation study.

The batch process

iLand does not use any parallel computing across computer nodes (e.g., mpc); instead, every iLand simulation runs on a single computer node independently. The main advantage of a cluster environment is thus that many iLand simulations can be run at the same time (e.g., with different climate and management scenarios). In a typical environment the steps for a single simulation run with id "x" are:

  • copy the required data (i.e. the project folder) to the compute node (note: if storage is fast you could also run all simulations from a project somewhere on your storage; in this case just make sure that you store your *output* on the compute node or in a unique location - otherwise parallel simulations might interfere and overwrite each other!)
  • run the simulation on the node (with settings for the run "x")
  • copy the output of the simulation back to a common storage space (e.g., in a folder 'output_x')

The above mentioned steps can be specified in a batch script (e.g. bash script on Linux systems). The 'hard' part is to translate the abstract settings for a given simulation id to something that iLand understands. In most cases this boils down to 'build' the command line arguments for the "ilandc" executable. Here is a (shortened example) - note that the details differ from the more generic example of a 'master' file above.

# the run id that should be executed is in the environment variable SLURM_ARRAY_TASK_ID
# other relevant env. variables: 
# TARGET: the path to a local directory on the node
# HOME: a path to the directory for the project data and the output data

# extract the run id from the master file:
ID=`grep "^$SLURM_ARRAY_TASK_ID," $HOME/cent4csink/project/master.csv | cut -d "," -f 1`
echo "processing job $ID..."

if [ -d "$HOME/cent4csink/results/data${ID}" ]; then
  echo "directory already exists, quitting"
  exit 0

echo "copy data to target directory $TARGET"

rm -r $TARGET/*
mkdir $TARGET
cp -r $HOME/cent4csink/project/* $TARGET


LINE=`grep "^$SLURM_ARRAY_TASK_ID," $HOME/cent4csink/project/master.csv`
echo $LINE
# extract the variables from the master file
VARSNAPSHOT=`echo $LINE | cut -d "," -f 5`
VARCLIMTABLE=`echo $LINE | cut -d "," -f 8`
VARMGMT=`echo $LINE | cut -d "," -f 6`
VAREVENTS=`echo $LINE | cut -d "," -f 9`

echo "snapshot: $VARSNAPSHOT, climate table: $VARCLIMTABLE abe-on: $VARMGMT events: $VAREVENTS"

# copy snapshot and climate database to the project folder
# this steps just avoid copying *all* climate data files to every node even if the data is not needed
# to do this, parts of the project data are stored outside of the project folder
CMD="cp $HOME/cent4csink/project_data/$VARSNAPSHOT.* init"
echo $CMD
CMD="cp $HOME/cent4csink/project_data/$VARCLIMTABLE database"
echo $CMD

# build the command for ilandc: 
EXECMD="$HOME/iland/build/ilandc/ilandc project.xml $YEARS user.abe_on=$VARMGMT system.database.climate=$VARCLIMTABLE model.initialization.file=init/${VARSNAPSHOT}.sqlite"
EXECMD2=" model.world.timeEventsFile=../wind_scenarios/${VAREVENTS}.txt"
echo "$EXECMD" > output/cmd.txt
echo "*** starting the simulation  ***"

# now run the simulation!

echo "*** finished! ***"
echo "copy back the results to data${ID}"
mkdir $HOME/cent4csink/results/data${ID}

cp $TARGET/output/* $HOME/cent4csink/results/data${ID}
cp $TARGET/log/log.txt $HOME/cent4csink/results/data${ID}/
echo "done."


Execution of the batch job

The details depend on the cluster management software that is used (e.g. SLURM or SGE). The use case for iLand (a single job repeated many times with slightly different paramters) is often covered with a term like 'Job arrays' (e.g. here).
The process is:

  • tell the cluster management to run the job with an array of run ids (e.g. 1:800).
  • use the provided tool of the management software to check progress (the cluster usually provides also different log files that help debugging)
  • when the job is finished, the outputs of all simulations are available in a central location (in the example: $HOME/cent4csink/results/*)

Created by werner. Last Modification: Friday 08 of October, 2021 13:51:13 GMT by werner.