Auszug |
---|
General computational fluid dynamics solver (cell-centered FVM). GPUs are supported. |
...
Info |
---|
To obtain and checkout a product license please read Ansys Suite first. |
Documentation and Tutorials
...
Codeblock | ||||
---|---|---|---|---|
| ||||
#!/bin/bash #SBATCH -t 00:10:00 #SBATCH --nodes=2 #SBATCH --ntasks-per-node=4096 #SBATCH -L ansys #SBATCH -p mediumstandard96:test #SBATCH --mail-type=ALL #SBATCH --output="cavity.log.%j" #SBATCH --job-name=cavity_on_cpu module load ansys/2019r22023r2 srun hostname -s > hostfile echo "Running on nodes: ${SLURM_JOB_NODELIST}" fluent 2d -g -t${SLURM_NTASKS} -ssh -mpi=intel -pib -cnf=hostfile << EOFluentInput >cavity.out.$SLURM_JOB_ID ; this is an Ansys journal file aka text user interface (TUI) file file/read-case initial_run.cas.h5 parallel/partition/method/cartesian-axes 2 file/auto-save/append-file-name time-step 6 file/auto-save/case-frequency if-case-is-modified file/auto-save/data-frequency 10 file/auto-save/retain-most-recent-files yes solve/initialize/initialize-flow solve/iterate 100 exit yes EOFluentInput echo '#################### Fluent finished ############' |
...
Codeblock | ||||
---|---|---|---|---|
| ||||
#!/bin/bash #SBATCH -t 00:59:00 #SBATCH --nodes=1 #SBATCH --partition=gpu-a100:shared ### on GPU-cluster of NHR@ZIB #SBATCH --ntasks-per-node=1 #SBATCH --gres=gpu:1 # number of GPUs per node - ignored if exclusive partition with 4 GPUs #SBATCH --gpu-bind=single:1 # bind each process to its own GPU (single:<tasks_per_gpu>) #SBATCH -L ansys #SBATCH --output="slurm-log.%j" module add gcc openmpi/gcc.11 ansys/2023r2_mlx_openmpiCUDAaware # external OpenMPI is CUDA-aware hostlist=$(srun hostname -s | sort | uniq -c | awk '{printf $2":"$1","}') echo "Running on nodes: $hostlist" cat <<EOF >tui_input.jou file/read-cas nozzle_gpu_supported.cas.h5 solve/initialize/hyb-initialization solve/iterate 1000100 yes file/write-case-data outputfile1 file/export cgns outputfile2 full-domain yes yes pressure temperature x-velocity y-velocity mach-number quit exit EOF fluent 3ddp -g -cnf=$hostlist -t${SLURM_NTASKS} -gpu -nm -i tui_input.jou \ -mpi=openmpi -pib -mpiopt="--report-bindings --rank-by core" >/dev/null 2>&1 echo '#################### Fluent finished ############' |
...
Codeblock | ||||
---|---|---|---|---|
| ||||
#!/bin/bash #SBATCH -t 00:10:00 #SBATCH --nodes=2 #SBATCH --ntasks-per-node=4 #SBATCH -L ansys #SBATCH -p gpu-a100 ### on emmy GPU-pcluster is simply called gpuof NHR@ZIB #SBATCH --output="slurm.log.%j" #SBATCH --job-name=cavity_on_gpu module add gcc openmpi/gcc.11 # external OpenMPI is CUDA aware module add ansys/2023r2_mlx_openmpiCUDAaware hostlist=$(srun hostname -s | sort | uniq -c | awk '{printf $2":"$1","}') echo "Running on nodes: $hostlist" cat <<EOF >fluent.jou ; this is an Ansys journal file aka text user interface (TUI) file parallel/gpgpu/show file/read-case initial_run.cas.h5 solve/set/flux-type yes solve/iterate 100 file/write-case-data outputfile ok exit EOF fluent 2d -g -t${SLURM_NTASKS} -gpgpu=4 -mpi=openmpi -pib -cnf=$hostlist -i fluent.jou >/dev/null 2>&1 echo '#################### Fluent finished ############' |
...
Info |
---|
Ansys only supports certain GPU vendors/models: |
Info |
---|
The number of CPU-cores (e.g. ntasks-per-node=Integer*GPUnr) per node must be an integer multiple of the GPUs (e.g. gpgpu=GPUnr) per node. |
...
Unfortunately, the case setup is most convenient with the Fluent GUI only. Therefore, we recommend doing all necessary GUI interactions on your local machine beforehand. As soon as the case setup is complete (geometry, materials, boundaries, solver method, etc.), save it as a *.cas file. After copying the *.cas file to the working directory of the supercomputer, this prepared case (incl. the geometry) just needs to be read [file/read-case], initialized [solve/initialize/initialize-flow], and finally executed [solve/iterate]. Above, you will find examples of *.jou (TUI) files in the job scripts.
Iff If you cannot set up your case input files *.cas by other means you may start a Fluent GUI as a last resort on our compute nodes.
But be warned: to keep fast/small OS images on the compute node there is a minimal set of graphic drivers/libs only; X-window interactions involve high latency.
...