Fluent’s native GPU solver can speed up many pressure‑based cases. It doesn’t cover every physics model or mesh yet, and VRAM matters. This guide shows how to run Fluent on a computing service like Compute without guesswork, how to confirm the GPU path is active, and what to check before scaling.
What this covers
- Picking a CUDA‑ready template
- Wiring up your license (FlexNet) safely
- Starting Fluent in batch with a journal file (recommended) or using the GUI
- Enabling the GPU solver and checking that it’s actually in use
- A small validation + self‑benchmark protocol you can copy
- Limits, VRAM tips, and troubleshooting
Fluent evolves quickly. Treat this page as a practical checklist. Always confirm model coverage against your installed version’s release notes.
1) Choose a CUDA‑ready template
Usually, your job runs inside a container image you pick.
- Fast start: use a general CUDA template such as Ubuntu 24.04 LTS (CUDA 12.6).
- Your own image: if your lab has a Fluent‑ready base, point the template to that image.
You do not need Docker‑in‑Docker. The host driver is usually provided by your computing provider.
2) Wire your license (FlexNet)
Set the right env var in your template’s Environment → Variables and connect over VPN or an SSH tunnel (see the licensing guide).
# Example (ports are examples; use your pinned values)
ANSYSLMD_LICENSE_FILE=1055@licenses.my-org.edu
If tunneling, point to 1055@localhost and keep the vendor port you forwarded.
3) Install or mount Fluent
Bring Fluent yourself—Hivenet doesn’t distribute it.
- Installer inside the template: mount your installer archive and run the Linux installer in the container. Keep the image private.
- Shared volume: if your org provides a network share with Fluent, mount it read‑only on the instance.
Keep licenses and installers out of public images. Mount them at runtime.
4) Start Fluent (batch recommended)
Batch runs are reproducible and easy to automate. Prepare a journal (run.jou) and a matched case/data (.cas.h5/.dat.h5). Start Fluent from the container shell:
# 3D, headless, run a journal
fluent 3d -g -i run.jou
3dor2dper your model.-gis headless (no GUI).-i run.jouexecutes your journal.
Prefer the GUI? Launch fluent 3d (no -g) inside the container and use your remote desktop workflow. For heavy meshes, batch is still better.
5) Enable the GPU solver
Use your Fluent version’s documented toggle to enable GPU acceleration. In recent releases there is a checkbox in General → GPU acceleration and a matching TUI command. Keep it simple for the first run:
- Single GPU
- Single precision (default for GPU path)
- Pressure‑based solver
Confirm it’s active: in the Fluent console/log, you should see messages that a GPU device was initialized and solver kernels are offloaded. If logs show CPU‑only paths, the feature or model you selected may not be GPU‑covered yet.
6) Validate before you scale
Numerics
- Start with single precision if your case allows; compare against a small CPU double‑precision baseline for a few iterations/time steps.
- Check residuals, forces/coefficients, and a domain‑specific metric (e.g., pressure drop) match within your acceptance bands.
VRAM & mesh
- Watch
nvidia-smifor memory use. - If you hit OOM, coarsen the mesh (within validation rules), reduce outputs, or try a GPU with more VRAM.
Self‑benchmark
- Same case, same stopping criteria, same physics.
- Record wall‑clock, iterations/sec, and cost per converged case.
Cost math
cost_per_converged_case = price_per_hour × wall_hours
7) Common limits (plan around them)
- Physics coverage is expanding, but not universal. Check your release notes for combustion, multiphase, acoustics, radiation, etc.
- Double‑precision: the GPU path favors single precision. If your case must run in FP64, expect lower gains or stay on CPU.
- Multi‑GPU exists for some cases but start with single GPU to validate numerics and memory, then scale.
- I/O can dominate. Reduce write frequency, compress logs, and stage data on local NVMe.
8) Minimal journal skeleton (adapt to your model)
This is a pattern, not a drop‑in file—replace with the right commands for your physics and version.
/file/read-case-data case.cas.h5
; Enable GPU acceleration via TUI for your version
; (Use the documented command or toggle in General → GPU acceleration)
/solve/initialize/initialize-flow
/solve/iterate 1000
/file/write-case-data out.cas.h5
/exit yes
Keep a Methods note with: Fluent version, case name, physics models, GPU enabled flag, and stopping criteria.
9) Troubleshooting
“GPU device not found / not initialized”
Confirm nvidia-smi works inside the container. The template must have CUDA user‑space and the host must pass through the driver (Compute does that). If you use your own image, match CUDA to your driver where possible.
“Feature not available in GPU mode”
Switch off the unsupported model, or run the case on CPU for that study.
Out of memory (OOM)
Reduce mesh size or outputs; or select a profile with larger VRAM.
License errors
Check your ANSYSLMD_LICENSE_FILE and your VPN/SSH tunnel. See the licensing guide.
Slower than CPU
Not all cases benefit. Profile with a small case first; consider CPU if the physics/mesh don’t map well to the GPU path.
Methods snippet (copy‑paste)
hardware:
gpu: "<model> (<VRAM> GB)"
driver: "<NVIDIA driver version>"
cuda: "<CUDA version>"
cpu: "<model / cores>"
software:
fluent: "<version> (GPU solver enabled)"
os_image: "Ubuntu 24.04 LTS (CUDA 12.6)"
licenses:
ansys: "ANSYSLMD_LICENSE_FILE=1055@licenses.my-org.edu"
run:
mode: "batch"
journal: "run.jou"
notes: "pressure-based, single precision, single GPU"
outputs:
wall_hours: "<hh:mm>"
iter_per_sec: "<value>"
converged_criteria: "<residuals/metric>"
Related reading
Try Compute today
Start a GPU instance with a CUDA-ready template (e.g., Ubuntu 24.04 LTS / CUDA 12.6) or your own GROMACS image. Enjoy flexible per-second billing with custom templates and the ability to start, stop, and resume your sessions at any time. Unsure about FP64 requirements? Contact support to help you select the ideal hardware profile for your computational needs.
