HTCondor is no longer in active use at Aalto. This page serves as historical reference information that may be useful for others.


HTCondor (formerly known as just Condor) is a computing scheduler developed at University of Wisconsin-Madison. This allows users to run their binaries on Aalto Linux workstations without explicit logging to desktop machines. Condor takes care of choosing the right workstation, setting correct job priority and taking care of cleaning the output. Condor distributes, schedules, executes and returns the result. So handmade farming is not needed.

HTCondor status at Aalto and support

Condor installations are department specific. Here is a list of departments that have HTCondor software installed on their Ubuntu workstations.

Department / school

Support contact



Aalto IT servicedesk *

joint installation, installed on all the Ubuntu workstations


Aalto IT servicedesk *

installed on all the Ubuntu workstations


Matti Harjula and Kenrick Bingham

installed on about 50 newer Ubuntu workstations

The instructions below are common to all the departments if not mentioned otherwise.

* Getting help: your department IT guys have responsibility over the HTCondor installation. Best way to reach them is to drop an email to the Aalto IT servicedesk including info like: your department, Linux workstation name and type of problem.

HTCondor official manuals

The detailed manual can be found from Current version of Condor we have can be checked with condor_q -version.

Before you run with Condor

It is recommended that you compile you binary statically. If you have used shared libs (or you get from someone code that has not been compiled statically), make sure that you set your environment correctly and use getenv = true option in Condor submit script.

No large MPI jobs (over the net) are allowed with Condor. For any large MPI or multithread job, please either run on your local workstation only or on other resources like Triton.

Condor is well suited for short time serial runs (like overnight), or for small (2-4 CPUs) parallel runs that can be run within one machine. Long runs (over 12 hours) are possible, but remember that Condor runs on local workstations, and uses only idle CPU cycles, i.e. some currently unused workstation during the day and all of them during night. Local usage is of higher priority and thus submitted Condor job that hurts local user will be suspended.

Always use should_transfer_files = yes in your Condor submit script. This way you make sure that all IOs will go to local directory assigned to HTCondor on a local worker instead of shared NFS (be it /home or alike).

Run your code with Condor

  • Discover condor pool status with condor_status or with condor_status -available to find out which machines are available for jobs. This step is to make sure that condor pool is available.

  • Compile a statically linked binary.

  • Create a condor submission script, like job.cond below

  • Submit the job to condor pool with condor_submit job.cond

  • Manage your job(s) with condor_q, condor_rm

It may take several minutes for code to start running. Check out condor.log for any useful log information.

Job script examples

CS users should use universe = local

# job_1.cond -- ready to run serial code example

executable = serial.bin
universe = vanilla
output = serial.out
error = serial.err
log = condor.log
should_transfer_files = YES
# job_2.cond -- Condor serial job submission script example

# define job specific vars to be used later in this script
# this should be an absolute path, or path from current working dir

# setting up base directory for input, output, error and log files, executable path is not affected
initialdir = $(DIR)

# Define executable to run, it can be arch specific, or just some generic code
executable = mycode

# memory requirements, if any
#request_memory = 512 MB

# Condor universe. Default Vanilla, others haven't been configured/tested
universe = vanilla

# the file name specified with 'input' should contain any keyboard input the program requires
# note, that command-line arguments are specified by the 'arguments' command below
input = input.txt

# and output files
# note, that input, output, log and error files will/should be in 'initialdir' directory
output = $(cluster).out

# Errors, if any, will go here
error = $(cluster).err

# Always define log file, so that you know what haapened to your job(s)
log = condor.log

# email for job notifications, when it is completed or finished with errors
#notify_user =
#notification = Complete
# Additional environment vars
#environment = "PATH=$ENV(PATH):/home/user/bin"

# replicate your current working environment on the worker node
# useful when you have some specific vars like PATH, LD_LIBRARY_PATH or other defined with 'module'
getenv = true

# code arguments, if any
#arguments = -c cmd_input.conf

# Trasferring your files to a system the job is going to run on
# that is the recommended method, to avoid NFS traffic
should_transfer_files = yes
transfer_input_files = cmd_input.conf,input.txt
when_to_transfer_output = ON_EXIT_OR_EVICT

# Some specific requirements, if any. By default Condor will run job on a machine which has
# the same architecture and operating system family as the machine from which it was submitted.
# Here is we want the worker node would be Ubuntu 12.04 with 4 CPU cores or more
#requirements = (OpSysLongName >= "Ubuntu 12.04") && (TotalCPus >= 4)


Condor commands

  • condor_q -analyze <condor_job_id> # your running/pending jobs diagnostics (for all your jobs at once if job_id is missing)

  • condor_q -global # list all/everyone’s jobs at pool

  • condor_q -version # find out installed condor version

  • condor_status -available # list available computers for your job

  • condor_status -state -total # Condor pool resources in total

  • condor_status HOSTNAME # show status for a specific host ( in this case), where number of slots gives number of CPU cores available

  • condor_status -long vesku # show all details for a specific host

  • condor_status -constraint 'OpSysLongName>="Ubuntu 12.04"' # list Ubuntu 12.04 workstations only

  • condor_rm <condor_job_id> # remove particular job

  • condor_rm -all # remove all user jobs

  • condor_rm -constraint 'JobStatus =!= 2' # remove all user jobs that are not currently running

  • condor_hold <job_id> # hold your Condor job(s) in the queue

  • condor_release <job_id> # release job(s) previously holded in the queue

  • (NOTE: doesn’t work on Ubuntu, so anywhere at Aalto) condor_compile [cc \| f77 \| g++ \| make \| ...] # relink an executable for checkpointing with Standard universe; not installed on Ubuntu 12.04, see Checkpointing section below

  • condor_history # list the completed jobs submitted from the workstation you run this command on

Startup script requirements= can be always tested with condor_status -constraint. Like in the above job_2.cond example:

  • condor_status -constraint '(OpSysLongName>="Ubuntu 12.04") && (TotalCPus >= 4)' -available

More commands and their usage examples you can find at Condor User Manual.

Additional “requirements”/”constraints” options that have been configured on PHYS workstations only: CPUModel, CPUModelName, TotalFreeMemory. The later one in MB, reports currently available free memory according to /proc/meminfo. Can be useful for large memory jobs, see example below.

# ask for machine with more than 4GB of free memory
requirements = (TotalFreeMemory >= 4000)

Checkpointing and condor_compile

HTCondor has no checkpoitning or remote system calls support on Ubuntu (according tomanual pages).

HTCondor config

Machine in considered to be free if: no user activity within 15 min (keyboard or mouse), average load < 30%, and no condor job already running.

Running job will be suspended if: local workstation user became active (on hold) or CPU busy for more than 2 min and job has been running more than 90 sec.

Suspended job will be resumed if: machine has been free for 5 min.

Suspended job is killed if: it has been suspended for 4 hours (Vanilla universe) or hasn’t completed checkpointing within 10 min (Standard universe) or higher priority job is waiting in the queue.

Job will be preempted if: it uses more memory than available for its slot (killed and send back to queue).


Condor has support on running jobs under shared filesystem. Should I use this?

This is a bad idea. Keep using Condor’s default local directory (somewhere on the local harddrive, department specific settings), otherwise, several jobs using NFS constantly (either home or any other remotely mounted) would make it really slow. Use

should_transfer_files  = YES
transfer_input_files   = file1.dat,file2.txt

options instead. Then condor will copy all required (specified) files to its local spool directory and run jobs locally. Only when finished, it will return files back to the original submitting directory. This original submitting directory should not be a NFS mounted directory such as your home directory, as in the Aalto environment those are mounted with Kerberos security, and if the Kerberos ticket has expired because you aren’t working on your workstations, condor will not be able to access this directory and your job results will be lost.

My job is in ‘Idle’ state, while there are resources available

Job may take several minutes to start, if it takes longer, check out job log (defined with log = directive in the submit script) and then run condor_q -analyze <job_id> to see possible reasons. More debugging options at condor_q manual.

I’ve copy/pasted example files from this page, but when try to run they produce some errors

Should be this wiki specific. Noticed (with cat -A filename) that copy/pasted text includes bunch of non-ascii characters.

Got it fixed with perl -pi -e 's/[[:^ascii:]] //g' filename

Additional files/scripts

Files that may be useful with condor:

  • cq – A script that works as condor_qbut also prints the executing host

    use POSIX;
    $now=`date +%s`;
    $str=" -cputime -submitter $user ";
    for $i (0..$#ARGV) {
     $str.=" $ARGV[$i-1]";
    if($ARGV[0] eq "all") {$str=" -global -cputime -currentrun";}
    if($ARGV[0] eq "j") {system("condor_q -global -cputime -currentrun -submitter $user|egrep '(jobs|Schedd)'");exit(0);}
    if($ARGV[0] eq "rm") {$str=`condor_q -submitter $user -format \"%d\\n\" ClusterId|xargs`;print "condor_rm $str";exit(0);}
    foreach(`condor_q -long $str`) {
      if(m/^Iwd\s*=\s*(\S+)/) { $iwd=$1; }
      if(m/^RemoteHost\s*=\s*(\S+)/) { $rh=$1; }
      if(m/ServerTime/) {
        push(@iwds, "$rh\t $iwd");
    foreach(`condor_q $str`) {
      if(/^\s*\d+\.\d/) {
        $_.=" ".$iwd;
      print "$_\n";
    sub runtime() {
      my($now, $st)=@_;
      $str=~s/\t/ /g;
      $str=~s/\s+/ /g;
      split(/ /,$str);
      if($d>0) {$ret="$d+$t";}else{$ret=$t;}
      return $ret;
  • turbomole.cond, run_ridft510_condor.scr– pair of scripts for running TurboMole or AMBER (thanks to Markus Kaukonen)

    # turbomole.cond
    Executable = ./run_ridft510_condor.scr
    Universe = vanilla
    Error = err.$(cluster)
    Output = out.$(cluster)
    Log = log.$(cluster)
    environment = "OMP_NUM_THREADS=1"
    Requirements = Memory > 1000
    should_transfer_files = YES
    when_to_transfer_output = ON_EXIT
    transfer_input_files = run_ridft510_condor.scr, auxbasis, basis, control, coord,
    #Arguments =

    and run_ridft510_condor.scr

    source /etc/profile
    source /etc/bashrc
    source /etc/profile.d/
    export PATH
    export PATH="${AMBERHOME}/exe:${AMBERHOME}/bin:${PATH}"
    export PATH="${HOME}/bin:${PATH}"
    ulimit -s unlimited
    #ulimit -a > mylimits.out
    jobex -ri -c 200 > jobex.out