Preruns

Preruns are preliminary executions of a process, program, or workflow that occur before a main run. They are intended to prepare the environment, validate prerequisites, or initialize resources so that the primary execution can proceed more smoothly. Preruns may establish caches, load configurations, create needed directories, or seed data, and their effects are often not counted as part of the main results.

In software development and data processing, preruns are commonly used to reduce warm-up time and ensure that the system is ready for the actual work. For example, a prerun might populate a cache, perform dependency checks, or precompile components. This helps to minimize variability caused by cold starts and can improve the reliability of subsequent runs in environments where startup costs are nontrivial.

In benchmarking and performance testing, preruns are sometimes treated as warm-up iterations. They help stabilize metrics by allowing just-in-time compilation, caching, and resource allocation to occur before measurements begin. However, preruns can also distort results if their effects linger in the measured period, so best practices vary: some tests separate preruns from the measured run and document their role clearly.

In machine learning and experimental workflows, preruns can include tasks such as loading data sets, initializing models, or allocating hardware resources. These steps can prevent repeated overhead during the main experiment and help ensure reproducibility.

Implementation considerations for preruns include configurability, isolation from the main run, and clear documentation of which steps are performed, why, and how their effects are accounted in results. They are most effective when used to stabilize environments while avoiding unintended side effects on the primary measurements.