HPC Workflow Orchestration

Coordinating many computational steps across large clusters reliably, reproducibly, and without manual babysitting.

The coordination problem

A serious simulation campaign is not one program but a graph of steps: mesh generation, transport solves, coupling, post-processing, and analysis, often thousands of runs with dependencies between them, executed across a cluster with limited resources. Orchestration is the software that schedules these steps, tracks their state, retries failures, and records what happened, so a human is not manually launching and watching jobs.

What an orchestrator handles

• Dependency graphs: run step B only after step A succeeds
• Resource scheduling: place jobs on nodes without oversubscribing
• Fault tolerance: retry transient failures, quarantine persistent ones
• Data movement: stage inputs to compute nodes and collect outputs
• Provenance: log every step's inputs, version, and result

Why reproducibility is built in, not bolted on

If an orchestrator records exactly which code version, inputs, and parameters produced each output, the whole campaign becomes reproducible by construction. Trying to reconstruct that history afterward is error-prone; capturing it as the work runs is reliable. This is why orchestration and the reproducible-science pipeline are the same effort seen from two angles.

def run_dag(nodes, deps, execute):
    done = set()
    while len(done) < len(nodes):
        ready = [n for n in nodes if n not in done
                 and all(d in done for d in deps[n])]
        for n in ready:
            execute(n)          # logs version, inputs, result
            done.add(n)

Scaling honestly

More nodes do not always mean faster results; communication, data movement, and load imbalance impose limits (see Amdahl's argument in parallel computing). Good orchestration measures where time actually goes and does not assume linear speedup.

Kronos use

Large neutronics, plasma, and materials campaigns for the breeder and burner run through orchestrated workflows so that results are reproducible and the compute is used efficiently, which is essential when high-fidelity runs are the bottleneck.