Workflow Orchestration for Simulation
Orchestration tools run multi-code simulation pipelines reliably at scale, managing dependencies, parallelism, failures, and the data passed between steps.
From scripts to pipelines
A serious analysis chains many codes: equilibrium, transport, heating, neutronics, stability. Doing this by hand does not scale and is not reproducible. Workflow orchestration formalizes the chain as a pipeline with explicit steps and dependencies, run by an engine that handles execution, data flow, and errors.
What an orchestrator provides
- A description of the steps and their input-output dependencies
- Scheduling across compute resources, including parallel scans
- Automatic handling of failures, retries, and restarts
- Capture of every input, output, and version for provenance
Directed acyclic graphs
Many workflows are expressed as a directed acyclic graph, where nodes are computations and edges are data dependencies. The engine runs nodes as soon as their inputs are ready, parallelizing independent branches. This structure makes large parameter scans, thousands of related runs, tractable and organized.
Reproducibility by construction
Because the orchestrator records the exact inputs, code versions, and environment for each step, a completed workflow can be re-run to reproduce its results, or partially re-run after a change while reusing unaffected results. This turns reproducibility from a manual burden into a property of the system.
Scaling and portability
Good orchestration separates the description of what to compute from where it runs, so the same workflow can execute on a laptop, a cluster, or a supercomputer. This portability lets a pipeline developed at small scale be deployed at large scale without rewriting the science.
Kronos runs its analyses through orchestrated pipelines so that a large set of related computations can be executed, tracked, and regenerated consistently.