Engineering simulation workloads; from finite element analysis (FEA) and computational fluid dynamics (CFD) to structural, thermal, or multi-physics modelling, remain among the most computationally demanding tasks in the technical landscape. Traditionally, these simulations have run on-premises, leveraging high-performance computing (HPC) clusters within universities, research centres, and industry R&D labs.
But the landscape is shifting. Cloud HPC platforms are maturing rapidly, offering not just raw compute, but orchestration tools, elastic scaling, and integration with modern development pipelines. This has many institutions and companies re-evaluating: should they stay local, or move engineering simulation to the cloud?
The Case for Staying On-Prem
On-premises infrastructure offers predictable performance and cost, with several key advantages:
- Low latency: Especially important for simulations that involve tightly coupled parallel processing, such as domain-decomposed FEA models or turbulent CFD runs using MPI-based solvers.
- No data egress fees: With simulation outputs often exceeding hundreds of gigabytes, keeping data local avoids the often-significant cloud bandwidth charges.
- Data control and compliance: Engineering designs and prototype models are frequently IP-sensitive. For HE institutions dealing with industrial partnerships or classified projects, keeping compute in-house simplifies data governance.
- Existing investment: Many universities and firms already maintain clusters with InfiniBand interconnects, GPU nodes, and job scheduling infrastructure like SLURM. Migrating to cloud introduces not only cost, but complexity in toolchain adaptation and workflow retraining.
The Case for Cloud-Based Simulation
Cloud platforms such as Azure Batch, AWS ParallelCluster, and Google Cloud’s HPC Toolkit now offer high-core count VMs, low-latency networking (e.g. Elastic Fabric Adapter), and scalable storage; all provisioned on-demand.
Benefits include:
- Elasticity: Scale up to thousands of vCPUs or GPUs during simulation peaks—such as near grant deadlines, thesis crunch time, or product release windows, without maintaining underutilised hardware year-round.
- Faster iteration: Engineering teams can run parametric sweeps or optimisation routines in parallel, reducing turnaround times from days to hours.
- Integrated workflows: Cloud-native tools make it easier to connect simulations with pre/post-processing, version control, and visualisation pipelines, enabling reproducible science and engineering.
This agility is particularly appealing to startups, small engineering firms, or HE departments without dedicated HPC facilities.
Hybrid Models: The Emerging Standard
In practice, many organisations are adopting hybrid HPC models. These allow local infrastructure to handle routine workloads, while “bursting to cloud” when demand spikes or specialist hardware is needed.
For technical staff, this means building flexible job submission pipelines, often using containerised environments (e.g. Singularity, Docker) and portable job scripts. Tools like Rescale, Altair SmartCloud, and CloudyCluster can help bridge local and cloud HPC workflows.
Key Factors for Technical Staff to Consider
- Solver licensing: Some simulation tools (ANSYS, Abaqus, COMSOL) use token or seat-based models that may not translate well to cloud elasticity. Licensing costs can dwarf compute costs if not carefully managed.
- Mesh complexity and data size: Very large models may be more efficiently handled locally due to I/O constraints or slow upload speeds.
- Job scheduling and queue times: A cloud instance might spin up in minutes, while a local HPC queue could delay jobs during term time.
- Skillset and support: Maintaining local infrastructure requires a different expertise than cloud orchestration. Upskilling technical staff may be necessary for migration or hybrid models.
Conclusion: Optimising for Flexibility, Not Ideology
The future of engineering simulation isn’t binary. While on-prem remains powerful for tightly coupled, high-throughput work, cloud HPC offers unprecedented flexibility; especially when budgets, timelines, or physical resources are constrained.
For technical professionals, the priority should be benchmarking workloads, understanding usage patterns, and designing flexible infrastructure that supports scientific excellence while managing costs and complexity. Done right, a well-balanced hybrid strategy can deliver the best of both worlds.