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292 changes: 292 additions & 0 deletions benchmark/calibrate-n.js
Original file line number Diff line number Diff line change
@@ -0,0 +1,292 @@
'use strict';

const path = require('node:path');
const { fork } = require('node:child_process');
const fs = require('node:fs');
const { styleText } = require('node:util');

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

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);
}

// Extract options
let benchmarkPath;
let runs = DEFAULT_RUNS;
let cvThreshold = CV_THRESHOLD;
let maxIncreases = MAX_N_INCREASE;
let startN = 10;
let increaseFactor = INCREASE_FACTOR;

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;
}
}

if (!benchmarkPath) {
console.error('Error: No benchmark path specified');
process.exit(1);
}

const fullBenchmarkPath = path.resolve(benchmarkPath);
if (!fs.existsSync(fullBenchmarkPath)) {
console.error(`Error: Benchmark file not found: ${fullBenchmarkPath}`);
process.exit(1);
}

function calculateStats(values) {
const mean = values.reduce((sum, val) => sum + val, 0) / values.length;

const squaredDiffs = values.map((val) => {
const diff = val - mean;
const squared = diff ** 2;
return squared;
});

const variance = squaredDiffs.reduce((sum, val) => sum + val, 0) / values.length;
const stdDev = Math.sqrt(variance);
const cv = stdDev / mean;

return { mean, stdDev, cv, variance };
}

function runBenchmark(n) {
return new Promise((resolve, reject) => {
const child = fork(
fullBenchmarkPath,
[`n=${n}`],
{ stdio: ['inherit', 'pipe', 'inherit', 'ipc'] },
);

const results = [];
child.on('message', (data) => {
if (data.type === 'report' && data.rate && data.conf) {
results.push({
rate: data.rate,
conf: data.conf,
});
}
});

child.on('close', (code) => {
if (code !== 0) {
reject(new Error(`Benchmark exited with code ${code}`));
} else {
resolve(results);
}
});
});
}

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)}`);

const stats = calculateStats(group.rates);
console.log(` CV: ${(stats.cv * 100).toFixed(2)}% (lower values mean more stable results)`);

const isStable = stats.cv <= cvThreshold;
console.log(` Stability: ${isStable ?
styleText(['bold', 'green'], '✓ Stable') :
styleText(['bold', 'red'], '✗ Unstable')}`);

groupStats.push({
confKey,
stats,
isStable,
});
}

if (groupStats.length > 0) {
// Check if any configuration has CV > 10% (too unstable)
const tooUnstableConfigs = groupStats.filter((g) => g.stats.cv > 0.10);

const avgCV = groupStats.reduce((sum, g) => sum + g.stats.cv, 0) / groupStats.length;
console.log(`\nOverall average CV: ${(avgCV * 100).toFixed(2)}%`);

const isOverallStable = avgCV < CV_THRESHOLD;
const hasVeryUnstableConfigs = tooUnstableConfigs.length > 0;

// 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%`));
}
}

if (avgCV < bestCV || !bestGroupStats) {
bestN = n;
bestCV = avgCV;

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)}%`);
}
}

// 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;

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)}%`);
});

return n;
}

increaseCount++;
n *= increaseFactor;
}

if (increaseCount >= maxIncreases) {
const finalAvgCV = bestGroupStats && bestGroupStats.length > 0 ?
bestGroupStats.reduce((sum, g) => sum + g.stats.cv, 0) / bestGroupStats.length : Infinity;

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:`);

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}%`);
}
}
});
}
}

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;
}

main().catch((err) => {
console.error('Error:', err);
process.exit(1);
});
41 changes: 41 additions & 0 deletions doc/contributing/writing-and-running-benchmarks.md
Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@
* [Benchmark analysis requirements](#benchmark-analysis-requirements)
* [Running benchmarks](#running-benchmarks)
* [Running individual benchmarks](#running-individual-benchmarks)
* [Calibrating the number of iterations with calibrate-n.js](#calibrating-the-number-of-iterations-with-calibrate-njs)
* [Running all benchmarks](#running-all-benchmarks)
* [Specifying CPU Cores for Benchmarks with run.js](#specifying-cpu-cores-for-benchmarks-with-runjs)
* [Filtering benchmarks](#filtering-benchmarks)
Expand Down Expand Up @@ -142,6 +143,46 @@ buffers/buffer-tostring.js n=10000000 len=1024 arg=true: 3498295.68561504
buffers/buffer-tostring.js n=10000000 len=1024 arg=false: 3783071.1678948295
```

### Calibrating the number of iterations with calibrate-n.js

Before running benchmarks, it's often useful to determine the optimal number of iterations (`n`)
that provides statistically stable results. The `calibrate-n.js` tool helps find this value by
running a benchmark multiple times with increasing `n` values until the coefficient of variation (CV)
falls below a target threshold.

```console
$ node benchmark/calibrate-n.js benchmark/buffers/buffer-compare.js

--------------------------------------------------------
Benchmark: buffers/buffer-compare.js
--------------------------------------------------------
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
4. Stop when we have stable results (overall CV < 5% and all configs CV < 10%) or max increases reached

Configuration:
- Starting n: 10 iterations
- Runs per n value: 30
- Target CV threshold: 5% (lower CV = more stable results)
- Max increases: 6
- Increase factor: 10x
```

The tool accepts several options:

* `--runs=N`: Number of runs for each n value (default: 30)
* `--cv-threshold=N`: Target coefficient of variation threshold (default: 0.05)
* `--max-increases=N`: Maximum number of n increases to try (default: 6)
* `--start-n=N`: Initial n value to start with (default: 10)
* `--increase=N`: Factor by which to increase n (default: 10)

Once you've determined a stable `n` value, you can use it when running your benchmarks.

### Running all benchmarks

Similar to running individual benchmarks, a group of benchmarks can be executed
Expand Down
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