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General computational fluid dynamics solver (cell-centered FVM)

General Information

The ANSYS software package is developed and distributed by ANSYS, Inc..

This documentation describes the specifics of installation and usage of ANSYS at HLRN. Introductory courses for ANSYS as well as courses for special topics are offered by ANSYS Inc. and their regional offices, e.g. in Germany. It is recommended to take at least an introductory course (see the CAD-FEM GmbH homepage). Good (free) starting points for self-study are https://students.cadfem.net/de/ansys-gratis-lernen.html and https://courses.ansys.com

Details of the HLRN Installation of ANSYS

The ANSYS versions currently installed are

VersionModule FileRemarks
ANSYS 2020 R2ansys/2020r2
ANSYS 2019 R2ansys/2019r2default
ANSYS 19.2ansys/19.2
ANSYS 19.0ansys/19.0

The module name is ansys. Other versions may be installed. Inspect the output of :            module avail ansys

Usage and Licensing at HLRN

The use of Ansys is restricted to members of the ansys user group. You can ask to become a group member at support[at]hlrn.de
Please note the license conditions: Our academic licenses are restricted to students, PhD students, teachers and trainers of public institutions. They cannot be used in projects that are financed by industrial partners.

Licences

Important: Always add
#SBATCH -L ansys
to your job script.

The flag "#SBATCH -L ansys" ensures that the scheduler starts jobs only, when licenses are available.
You can check the availability yourself: scontrol show lic

  • aa_r is a "ANSYS Academic Research License" with 4 inclusive tasks. Research jobs with more than 4 tasks cost additional "aa_r_hpc" licenses.
  • aa_t_a is a "ANSYS Academic Teaching License" with a maximum of 4 tasks. These may be used only for student projects, student instruction and student demonstrations. Eligible users are allowed to activate these, by adding the flag

    Activation of the aa_t_a license
    -lpf $ANSYSLIC_DIR/prodord/license.preferences_for_students_and_teaching.xml

    to the Ansys executable.

Example Jobscripts

The underlying test case described here can be downloaded here: NaturalConvection_SimulationFiles.zip

Job for 2 nodes with 40 tasks (on 40 cpu-cores) per node
#!/bin/bash
#SBATCH -t 00:10:00
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=40
#SBATCH -L ansys
#SBATCH -p medium
#SBATCH --mail-type=ALL
#SBATCH --output="cavity.log.%j"
#SBATCH --job-name=cavity

module load ansys/2019r2
srun hostname -s > hostfile
echo "Running on nodes: ${SLURM_JOB_NODELIST}"

fluent 2d -g -t${SLURM_NTASKS}  -ssh  -mpi=intel -cnf=hostfile << EOFluentInput >cavity.out.$SLURM_JOB_ID
      file/read-case initial_run.cas.h5
      parallel/partition/method/cartesian-axes 2
      solve/initialize/initialize-flow
      solve/iterate 100
      exit
      yes
EOFluentInput

echo '#################### Fluent finished ############'


Job for 2 nodes running with 4 GPUs per node and 4 host tasks per node
#!/bin/bash
#SBATCH -t 00:10:00
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=4
#SBATCH --gpus-per-node=4
#SBATCH -L ansys
#SBATCH -p gpu
#SBATCH --output="slurm.log.%j"
echo "Running on nodes: ${SLURM_JOB_NODELIST}"
srun hostname -s > hostfile

module load ansys/2020r2

cat <<EOF > fluent.jou
; this is a Ansys journal file aka text user interface (TUI) file
parallel/gpgpu/show
file/read-cas initial_run.cas.h5
solve/iterate 10
file/write-case-data outputfile
ok
exit
EOF

fluent 2d -g -t${SLURM_NTASKS} -gpgpu=${SLURM_GPUS_PER_NODE} -mpi=intel -cnf=hostfile -i fluent.jou  >/dev/null 2>&1
echo '#################### Fluent finished ############'

Your job can be offloaded if parallel/gpgpu/show denotes the selected devices with a "(*)".
Your job was offloaded successfully if the actual call of you solver prints "AMG on GPGPU".
In this case, your .trn output file contains device_list and amgx_and_runtime, respectively.

Ansys only supports certain GPU vendors/models:
https://www.ansys.com/it-solutions/platform-support/previous-releases
Look here for the PDF called "Graphics Cards Tested" of your version... (most Nividia, some AMD)

-gpgpu    must be an integer multiple of the tasks per node (ntasks-per-node)
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