benchmark: add calibrate-n script

RafaelGSS · RafaelGSS · commit 8c609be1b19f · 2025-08-12T09:19:56.000-03:00
This script should help identify the best N when creating/updating benchmarks Signed-off-by: RafaelGSS <rafael.nunu@hotmail.com> PR-URL: #59186 Reviewed-By: Vinícius Lourenço Claro Cardoso <contact@viniciusl.com.br> Reviewed-By: James M Snell <jasnell@gmail.com>
diff --git a/benchmark/calibrate-n.js b/benchmark/calibrate-n.js
@@ -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'], '&#10003; Stable') :
+        styleText(['bold', 'red'], '&#10007; 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: ${(avgCV * 100).toFixed(2)}%`);
+
+      const isOverallStable = avgCV < CV_THRESHOLD;
+      const hasVeryUnstableConfigs = tooUnstableConfigs.length > 0;
+
+      // Check if overall CV is below CV_THRESHOLD and no configuration has CV > 10%
+      if (isOverallStable && !hasVeryUnstableConfigs) {
+        console.log(styleText(['bold', 'green'], `  &#10003; Overall CV is below 5% and no configuration has CV above 10%`));
+      } else {
+        if (!isOverallStable) {
+          console.log(styleText(['bold', 'red'], `  &#10007; Overall CV (${(avgCV * 100).toFixed(2)}%) is above 5%`));
+        }
+        if (hasVeryUnstableConfigs) {
+          console.log(styleText(['bold', 'red'], `  &#10007; ${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(`  &rarr; New best n: ${n} with average CV: ${(avgCV * 100).toFixed(2)}%`);
+      } else {
+        console.log(`  &rarr; Current best n remains: ${bestN} with average CV: ${(bestCV * 100).toFixed(2)}%`);
+      }
+    }
+
+    // Check if we've reached acceptable stability based on new criteria
+    // 1. Overall CV should be below CV_THRESHOLD
+    // 2. No configuration should have a CV greater than 10%
+    const avgCV = groupStats.length > 0 ?
+      groupStats.reduce((sum, g) => sum + g.stats.cv, 0) / groupStats.length : Infinity;
+    const hasUnstableConfig = groupStats.some((g) => g.stats.cv > 0.10);
+    const isOverallStable = avgCV < CV_THRESHOLD;
+
+    if (isOverallStable && !hasUnstableConfig) {
+      console.log(`\n&#10003; Found optimal n=${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(`    &#9888;&#65039; 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);
+});
diff --git a/doc/contributing/writing-and-running-benchmarks.md b/doc/contributing/writing-and-running-benchmarks.md
@@ -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)
@@ -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
