{
  "schema_version": "1.0",
  "id": "cpu-sieve-scaling-2026-10-28",
  "title": "Prime-sieve scaling across four workload sizes",
  "published_at": "2026-10-28",
  "evidence_status": "reproducible engineering benchmark",
  "method": "Segmented prime sieve; four upper bounds; eight configurations; three repetitions per configuration.",
  "known_count_checks": {
    "1000000": 78498,
    "5000000": 348513,
    "25000000": 1565927,
    "50000000": 3001134
  },
  "best_configuration": "2 workers, 16 KiB segment size",
  "observed_speedup_vs_best_single_thread": {
    "1000000": 1.545,
    "5000000": 1.856,
    "25000000": 1.879,
    "50000000": 1.873
  },
  "limitations": [
    "Single host and implementation; timing is not portable.",
    "Does not establish performance for AI inference, indexing, or other workloads.",
    "Three repetitions per configuration; no claim of scientific novelty."
  ],
  "artifacts": {
    "report": "https://darkoza.com/science/reports/cpu-sieve-scaling/report.md",
    "csv": "https://darkoza.com/science/reports/cpu-sieve-scaling/results.csv",
    "raw_results": "https://darkoza.com/science/reports/cpu-sieve-scaling/results.json",
    "source": "https://darkoza.com/science/reports/cpu-sieve-scaling/run_scaling_study.py"
  }
}