Function main

benchmark/calibrate-n.js:123–287

(n = startN)

}

async function main(n = startN) {
  let increaseCount = 0;
  let bestN = n;
  let bestCV = Infinity;
  let bestGroupStats = null;

  console.log(`
--------------------------------------------------------
Benchmark: ${benchmarkPath}
--------------------------------------------------------
What we are trying to find: The optimal number of iterations (n)
that produces consistent benchmark results without wasting time.

How it works:
1. Run the benchmark multiple times with a specific n value
2. Group results by configuration
3. If overall CV is above 5% or any configuration has CV above 10%, increase n and try again

Configuration:
- Starting n: ${n.toLocaleString()} iterations
- Runs per n value: ${runs}
- Target CV threshold: ${cvThreshold * 100}% (lower CV = more stable results)
- Max increases: ${maxIncreases}
- Increase factor: ${increaseFactor}x`);

  while (increaseCount < maxIncreases) {
    console.log(`\nTesting with n=${n}:`);

    const resultsData = [];
    for (let i = 0; i < runs; i++) {
      const results = await runBenchmark(n);
      // Each run might return multiple results (one per configuration)
      if (Array.isArray(results) && results.length > 0) {
        resultsData.push(...results);
      } else if (results) {
        resultsData.push(results);
      }
      process.stdout.write('.');
    }
    process.stdout.write('\n');

    const groupedResults = {};
    resultsData.forEach((result) => {
      if (!result || !result.conf) return;

      const confKey = JSON.stringify(result.conf);
      groupedResults[confKey] ||= {
        conf: result.conf,
        rates: [],
      };

      groupedResults[confKey].rates.push(result.rate);
    });

    const groupStats = [];
    for (const [confKey, group] of Object.entries(groupedResults)) {
      console.log(`\nConfiguration: ${JSON.stringify(group.conf)}`);