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benchmark: add calibrate-n script #59186
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'use strict'; | ||
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const path = require('node:path'); | ||
const { fork } = require('node:child_process'); | ||
const fs = require('node:fs'); | ||
const { styleText } = require('node:util'); | ||
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const DEFAULT_RUNS = 30; // Number of runs for each n value | ||
const CV_THRESHOLD = 0.05; // 5% coefficient of variation threshold | ||
const MAX_N_INCREASE = 6; // Maximum number of times to increase n (10**6) | ||
const INCREASE_FACTOR = 10; // Factor by which to increase n | ||
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const args = process.argv.slice(2); | ||
if (args.length === 0) { | ||
console.log(` | ||
Usage: node calibrate-n.js [options] <benchmark_path> | ||
Options: | ||
--runs=N Number of runs for each n value (default: ${DEFAULT_RUNS}) | ||
--cv-threshold=N Target coefficient of variation threshold (default: ${CV_THRESHOLD}) | ||
--max-increases=N Maximum number of n increases to try (default: ${MAX_N_INCREASE}) | ||
--start-n=N Initial n value to start with (default: autodetect) | ||
--increase=N Factor by which to increase n (default: ${INCREASE_FACTOR}) | ||
Example: | ||
node calibrate-n.js buffers/buffer-compare.js | ||
node calibrate-n.js --runs=10 --cv-threshold=0.02 buffers/buffer-compare.js | ||
`); | ||
process.exit(1); | ||
} | ||
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// Extract options | ||
let benchmarkPath; | ||
let runs = DEFAULT_RUNS; | ||
let cvThreshold = CV_THRESHOLD; | ||
let maxIncreases = MAX_N_INCREASE; | ||
let startN = 10; | ||
let increaseFactor = INCREASE_FACTOR; | ||
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for (const arg of args) { | ||
if (arg.startsWith('--runs=')) { | ||
runs = parseInt(arg.substring(7), 10); | ||
} else if (arg.startsWith('--cv-threshold=')) { | ||
cvThreshold = parseFloat(arg.substring(14)); | ||
} else if (arg.startsWith('--max-increases=')) { | ||
maxIncreases = parseInt(arg.substring(15), 10); | ||
if (isNaN(maxIncreases)) { | ||
console.error(`Error: Invalid value for --max-increases. Using default: ${MAX_N_INCREASE}`); | ||
maxIncreases = MAX_N_INCREASE; | ||
} | ||
} else if (arg.startsWith('--start-n=')) { | ||
startN = parseInt(arg.substring(10), 10); | ||
if (isNaN(startN)) { | ||
console.error(`Error: Invalid value for --start-n. Using default: 10`); | ||
startN = 10; | ||
} | ||
} else if (arg.startsWith('--increase=')) { | ||
increaseFactor = parseInt(arg.substring(11), 10); | ||
if (isNaN(increaseFactor)) { | ||
console.error(`Error: Invalid value for --increase. Using default: ${INCREASE_FACTOR}`); | ||
increaseFactor = INCREASE_FACTOR; | ||
} | ||
} else { | ||
benchmarkPath = arg; | ||
} | ||
} | ||
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if (!benchmarkPath) { | ||
console.error('Error: No benchmark path specified'); | ||
process.exit(1); | ||
} | ||
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const fullBenchmarkPath = path.resolve(benchmarkPath); | ||
if (!fs.existsSync(fullBenchmarkPath)) { | ||
console.error(`Error: Benchmark file not found: ${fullBenchmarkPath}`); | ||
process.exit(1); | ||
} | ||
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function calculateStats(values) { | ||
const mean = values.reduce((sum, val) => sum + val, 0) / values.length; | ||
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const squaredDiffs = values.map((val) => { | ||
const diff = val - mean; | ||
const squared = diff ** 2; | ||
return squared; | ||
}); | ||
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const variance = squaredDiffs.reduce((sum, val) => sum + val, 0) / values.length; | ||
const stdDev = Math.sqrt(variance); | ||
const cv = stdDev / mean; | ||
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return { mean, stdDev, cv, variance }; | ||
} | ||
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function runBenchmark(n) { | ||
return new Promise((resolve, reject) => { | ||
const child = fork( | ||
fullBenchmarkPath, | ||
[`n=${n}`], | ||
{ stdio: ['inherit', 'pipe', 'inherit', 'ipc'] }, | ||
); | ||
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const results = []; | ||
child.on('message', (data) => { | ||
if (data.type === 'report' && data.rate && data.conf) { | ||
results.push({ | ||
rate: data.rate, | ||
conf: data.conf, | ||
}); | ||
} | ||
}); | ||
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child.on('close', (code) => { | ||
if (code !== 0) { | ||
reject(new Error(`Benchmark exited with code ${code}`)); | ||
} else { | ||
resolve(results); | ||
} | ||
}); | ||
}); | ||
} | ||
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async function main(n = startN) { | ||
let increaseCount = 0; | ||
let bestN = n; | ||
let bestCV = Infinity; | ||
let bestGroupStats = null; | ||
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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: ${increaseFactor}x`); | ||
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while (increaseCount < maxIncreases) { | ||
console.log(`\nTesting with n=${n}:`); | ||
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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'); | ||
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const groupedResults = {}; | ||
resultsData.forEach((result) => { | ||
if (!result || !result.conf) return; | ||
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const confKey = JSON.stringify(result.conf); | ||
groupedResults[confKey] ||= { | ||
conf: result.conf, | ||
rates: [], | ||
}; | ||
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groupedResults[confKey].rates.push(result.rate); | ||
}); | ||
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const groupStats = []; | ||
for (const [confKey, group] of Object.entries(groupedResults)) { | ||
console.log(`\nConfiguration: ${JSON.stringify(group.conf)}`); | ||
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const stats = calculateStats(group.rates); | ||
console.log(` CV: ${(stats.cv * 100).toFixed(2)}% (lower values mean more stable results)`); | ||
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const isStable = stats.cv <= cvThreshold; | ||
console.log(` Stability: ${isStable ? | ||
styleText(['bold', 'green'], '✓ Stable') : | ||
styleText(['bold', 'red'], '✗ Unstable')}`); | ||
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groupStats.push({ | ||
confKey, | ||
stats, | ||
isStable, | ||
}); | ||
} | ||
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if (groupStats.length > 0) { | ||
// Check if any configuration has CV > 10% (too unstable) | ||
const tooUnstableConfigs = groupStats.filter((g) => g.stats.cv > 0.10); | ||
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const avgCV = groupStats.reduce((sum, g) => sum + g.stats.cv, 0) / groupStats.length; | ||
console.log(`\nOverall average CV: ${(avgCV * 100).toFixed(2)}%`); | ||
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const isOverallStable = avgCV < CV_THRESHOLD; | ||
const hasVeryUnstableConfigs = tooUnstableConfigs.length > 0; | ||
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// Check if overall CV is below CV_THRESHOLD and no configuration has CV > 10% | ||
if (isOverallStable && !hasVeryUnstableConfigs) { | ||
console.log(styleText(['bold', 'green'], ` ✓ Overall CV is below 5% and no configuration has CV above 10%`)); | ||
} else { | ||
if (!isOverallStable) { | ||
console.log(styleText(['bold', 'red'], ` ✗ Overall CV (${(avgCV * 100).toFixed(2)}%) is above 5%`)); | ||
} | ||
if (hasVeryUnstableConfigs) { | ||
console.log(styleText(['bold', 'red'], ` ✗ ${tooUnstableConfigs.length} configuration(s) have CV above 10%`)); | ||
} | ||
} | ||
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if (avgCV < bestCV || !bestGroupStats) { | ||
bestN = n; | ||
bestCV = avgCV; | ||
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bestGroupStats = []; | ||
for (const group of Object.values(groupedResults)) { | ||
if (group.rates.length >= 3) { | ||
const stats = calculateStats(group.rates); | ||
bestGroupStats.push({ | ||
conf: group.conf, | ||
stats: stats, | ||
isStable: stats.cv <= 0.10, | ||
}); | ||
} | ||
} | ||
console.log(` → New best n: ${n} with average CV: ${(avgCV * 100).toFixed(2)}%`); | ||
} else { | ||
console.log(` → Current best n remains: ${bestN} with average CV: ${(bestCV * 100).toFixed(2)}%`); | ||
} | ||
} | ||
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// Check if we've reached acceptable stability based on new criteria | ||
// 1. Overall CV should be below CV_THRESHOLD | ||
// 2. No configuration should have a CV greater than 10% | ||
const avgCV = groupStats.length > 0 ? | ||
groupStats.reduce((sum, g) => sum + g.stats.cv, 0) / groupStats.length : Infinity; | ||
const hasUnstableConfig = groupStats.some((g) => g.stats.cv > 0.10); | ||
const isOverallStable = avgCV < CV_THRESHOLD; | ||
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if (isOverallStable && !hasUnstableConfig) { | ||
console.log(`\n✓ Found optimal n=${n} (Overall CV=${(avgCV * 100).toFixed(2)}% < 5% and no configuration has CV > 10%)`); | ||
console.log('\nFinal CV for each configuration:'); | ||
groupStats.forEach((g) => { | ||
console.log(` ${JSON.stringify(groupedResults[g.confKey].conf)}: ${(g.stats.cv * 100).toFixed(2)}%`); | ||
}); | ||
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return n; | ||
} | ||
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increaseCount++; | ||
n *= increaseFactor; | ||
} | ||
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if (increaseCount >= maxIncreases) { | ||
const finalAvgCV = bestGroupStats && bestGroupStats.length > 0 ? | ||
bestGroupStats.reduce((sum, g) => sum + g.stats.cv, 0) / bestGroupStats.length : Infinity; | ||
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console.log(`Maximum number of increases (${maxIncreases}) reached without achieving target stability`); | ||
console.log(`Best n found: ${bestN} with average CV=${(finalAvgCV * 100).toFixed(2)}%`); | ||
console.log(`\nCV by configuration at best n:`); | ||
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if (bestGroupStats) { | ||
bestGroupStats.forEach((g) => { | ||
if (g.conf) { | ||
console.log(` ${JSON.stringify(g.conf)}: ${(g.stats.cv * 100).toFixed(2)}%`); | ||
if (g.stats.cv > cvThreshold) { | ||
console.log(` ⚠️ This configuration is above the target threshold of ${cvThreshold * 100}%`); | ||
} | ||
} | ||
}); | ||
} | ||
} | ||
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console.log(` | ||
Recommendation: You might want to try increasing --max-increases to | ||
continue testing with larger n values, or adjust --cv-threshold to | ||
accept the current best result, or investigate if specific configurations | ||
are contributing to instability.`); | ||
return bestN; | ||
} | ||
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main().catch((err) => { | ||
console.error('Error:', err); | ||
process.exit(1); | ||
}); |
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