Manage Random Streams in Parallel Simulation Studies

Define the reproducibility target

A seed is only one part of a simulation record. Preserve the random-number generator, software version, scenario definitions, replicate identifiers and assignment of streams. Decide whether rerunning one replicate should reproduce exactly the same dataset regardless of which worker executes it.

NumPy’s parallel random-generation documentation describes spawning streams through SeedSequence and related generator interfaces. Use a supported stream-management method rather than setting every worker to the same seed and assuming that parallel execution creates independent randomness.

Assign streams before dispatch

Create stable scenario and replicate identifiers. Associate each with a reproducible child stream or an explicitly documented generation scheme. Do not let completion order determine the random stream, because slower workers or retries can then change which data belong to which replicate.

Keep data-generation randomness separate from stochastic model-fitting randomness when their roles need independent control. If methods are compared on the same simulated dataset, generate that dataset once per replicate and supply it consistently to each method. This pairing can reduce noise in method comparisons without making different replicates identical.

Inspect a hypothetical worker failure

Suppose four workers each generate 100 datasets. If all four reset the same generator to the same seed before their loop, they may produce four copies of the same sequence of 100 datasets rather than 400 distinct simulation replicates. A nominal replicate count of 400 would then exaggerate the amount of independent Monte Carlo information.

A useful diagnostic records a small non-sensitive fingerprint of each generated synthetic dataset and its stream identifier. Unexpected exact repetition across workers warrants investigation. Different fingerprints do not prove ideal independence, but repeated identical datasets can expose a concrete implementation error.

Test restarts and scheduling

Rerun a selected replicate alone and compare it with the corresponding parallel result. Change worker count and scheduling order while keeping the intended replicate-to-stream mapping fixed. Check whether a failed replicate can be retried without consuming another replicate’s stream or overwriting its output.

Document any reproducibility limits across library or generator versions. Do not promise that a seed will reproduce identical results under arbitrary software changes. The final simulation archive should show which stream generated each dataset and which methods used it, making a failed fit, surprising result or numerical discrepancy recoverable without rerunning the entire experiment blindly.

Source verification: 6 November 2026. Examples are hypothetical.