What's the most cost-effective way to run scenario planning using reactor simulation software on HPC infrastructure?

The most cost-effective approach is usually to treat scenario planning as an embarrassingly parallel workflow and optimize for throughput, queue efficiency, and licensing cost, rather than trying to run one huge simulation as fast as possible.

Best-practice approach

1) Decompose the problem into many independent runs

For scenario planning, you typically have:

• different fuel/load conditions
• parameter sweeps
• uncertainty samples
• alternative operating strategies

Run these as independent jobs or job arrays on HPC. This gives the best use of:

• spare cores
• idle nodes
• spot/preemptible capacity
• simulation licenses

This is almost always cheaper than large tightly coupled parallel jobs unless the code scales very well.

2) Use a workflow manager

Use something like:

• SLURM job arrays
• Snakemake
• Nextflow
• FireWorks
• AiiDA or similar HPC workflow tooling

This reduces:

• scheduler overhead
• manual reruns
• wasted compute from failures
• human time

For cost-effectiveness, automation matters a lot.

3) Match the simulation granularity to the hardware

If the reactor code scales poorly beyond a certain core count, don’t over-allocate cores.

• Find the sweet spot where runtime improvement per extra core stops paying off.
• Run many medium-sized jobs instead of a few oversized ones.

This improves:

• node utilization
• queue wait times
• cost per successful scenario

4) Use parallelism at the outer loop first

If you have:

• 100 scenarios
• each scenario can use 8–16 cores efficiently

Then prefer:

• 100 independent runs × 8 cores over
• 1 run × 800 cores

unless the solver is proven to scale strongly.

5) Minimize I/O and checkpoint intelligently

HPC cost can be wasted on:

• huge output files
• slow shared filesystems
• unnecessary restart writes

Do this instead:

• write only needed outputs
• use local scratch during runs
• checkpoint only at meaningful intervals
• postprocess in batches

6) Reuse models and preprocessing

If each scenario starts from the same reactor model:

• preprocess geometry/mesh once
• cache invariant inputs
• generate scenario-specific input files programmatically

This saves both compute and engineering time.

7) Exploit cheaper resources where possible

If your HPC environment allows it:

• use spot/preemptible instances for non-urgent runs
• run on off-peak/low-priority queues
• use departmental or shared clusters before commercial cloud

For long scenario sweeps, spot/preemptible can be very cost-effective if your workflow supports restarts.

8) Right-size licensing

Many reactor simulation tools are limited by:

• floating licenses
• named-user licenses
• token counts

If licenses are the bottleneck:

• parallelize only up to available license capacity
• schedule arrays to avoid idle licensed cores
• consider license-aware workflow throttling

Often the cheapest CPU is the one you can actually license.

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A practical cost-effective pattern

A common setup looks like this:

1. Prepare a parameterized reactor model.
2. Generate 50–500 scenario input decks automatically.
3. Submit a SLURM job array.
4. Each task:
   • uses the optimal core count for that code
   • runs independently
   • writes minimal output to local scratch
   • stages final results back to shared storage
5. Aggregate results with a postprocessing step.

This is usually the most economical and scalable pattern.

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What usually isn’t cost-effective

Avoid:

• running everything on maximum core counts “just because”
• manual submission of hundreds of runs
• writing large intermediate files to network storage
• using expensive HPC resources for poorly parallelized codes
• paying for commercial cloud nodes without checkpoint/restart

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Rule of thumb

If your goal is scenario planning, the best cost/performance usually comes from:

Many independent medium-sized jobs + workflow automation + license-aware scheduling + minimal I/O + right-sized core allocation.

If you want, I can also help you design:

• a SLURM job-array template
• a cost model for comparing cluster vs cloud
• a workflow architecture for a specific reactor simulation code