Engineering compute is valuable when it shortens a design loop or increases the number of alternatives that can be evaluated. The fastest individual solve is not always the highest business throughput: a scheduler running many independent cases may finish a design-of-experiments campaign sooner.
Classify the solver and campaign
Determine whether the code is memory-bound, sparse-linear-algebra dominated, license-limited, GPU-enabled or strongly coupled across MPI ranks. Separate one very large transient solve from parameter sweeps, optimisation and post-processing. Record mesh size, cells/elements per rank, memory per rank, checkpoint rate and licence tokens.
Platform choices
X1140-C AxiKeystone handles large single-node CPU/memory jobs, preprocessing and compilation. X2160 AxiVector provides two 128-core nodes and direct 100GbE RDMA for codes that demonstrate MPI scaling. X3140 AxiLattice provides three scheduled nodes for shared CFD/FEA, parameter studies and departmental throughput.
Parallel CFD and FEA
OpenFOAM decomposes a domain for parallel execution; partition quality and inter-rank boundary area directly affect communication. PETSc provides scalable solver components and GPU paths for supported operations. Bind ranks to NUMA locality, test decomposition strategies and preserve a serial or validated reference result.
Do not infer accuracy from speed. Mesh independence, solver tolerance, convergence and physical validation remain engineering decisions.
Surrogate and reduced-order models
Use simulation campaigns to create a governed training set of geometry, boundary conditions and outputs. Train surrogates to screen designs or provide rapid estimates, while retaining a confidence/validity domain and routing critical candidates back to the full solver. Keep model predictions distinguishable from physics-based results in reports.
Production workflow
Submit jobs from versioned templates, stage input to NVMe, checkpoint long runs and return approved outputs to project storage. Scheduler integration should reflect licence tokens as a resource. Use CI to compile and regression-test custom solvers. Capture residual histories, solver versions and environment manifests with results.
Acceptance plan
- Choose small, medium and production-size reference cases.
- Validate numerical equivalence and one-node performance.
- Measure two- and three-node scaling where supported.
- Run a representative parameter campaign to test scheduler/storage throughput.
- Verify checkpoint restart, node failure procedures and licence controls.
Primary technical references
- OpenFOAM: running applications in parallel
- PETSc getting started
- PETSc GPU roadmap
- Slurm MPI Users Guide
- NVIDIA GPUDirect RDMA documentation
References are provided for software architecture and implementation planning. Validate the versions, licences, support matrix and regulated-use requirements applicable to the final deployment.
From technical concept to production system
Apply this technology through an Axiotech engineering work package
Axiotech can connect the compute, AI or analytics platform to the machine controls, data contracts, validation evidence and lifecycle-support model required for industrial use.
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