OpenFOAM · CUDA · C++ · GPU computing
Testing where OpenFOAM benefits from the GPU.
This work starts with the numerical operations that a CFD solver performs repeatedly: sparse matrix updates, vector algebra, reductions and iterative solves. The aim is to understand which layers are worth moving, and what data needs to stay on the device for that move to matter.
Why this matters
A faster kernel is not automatically a faster solve.
An OpenFOAM solve repeatedly touches matrices and fields. If every accelerated operation is followed by a copy back to the CPU, the transfer can erase the benefit of the kernel.
That makes GPU work here a systems problem. The useful question is not “can this function run in CUDA?” but “which part of the solve can remain on the GPU long enough to reduce total work?”
What is validated
A trustworthy first path from CPU code to CUDA.
The current proof of concept uses matching CPU and CUDA Jacobi implementations for the same Laplacian-style problem, then checks both against an OpenFOAM reference. It establishes numerical parity and keeps host/device movement visible.
Same problem
The CPU and CUDA paths operate on the same numerical setup.
Checked result
The GPU result is compared with the CPU path and the OpenFOAM reference.
Honest boundary
This is a validated solver path, not yet a native GPU port of OpenFOAM.
Integration roadmap
From a solver experiment to native data paths.
CUDA Jacobi solver
A standalone CUDA Jacobi implementation was built for a Laplacian-style problem and checked against a CPU implementation.
Numerical comparison
The CPU and CUDA results were compared with each other and with an OpenFOAM reference solution.
Host/device transfers
The proof of concept measures the data movement around the kernel, so transfer cost is part of the design discussion.
Native OpenFOAM data
The next integration step is to feed native OpenFOAM matrix and field data into the GPU path.
GPU-backed iterative solvers
The solver layer needs device-side implementations that can stay active across repeated iterations.
Sparse matrix and field operations
Sparse storage, vector algebra and reductions are being examined as the layers that determine whether GPU residency is worthwhile.
Next step
Keep the expensive part of the solve on the GPU.
The next useful milestone is a native OpenFOAM path in which matrices, fields and iterative operations remain on the device across as much of the solve as the architecture allows.