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Created October 31, 2025 12:34
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simple-benchmark-analysis.ipynb
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"source" : [ "# Simple Benchmark Analysis\n", "This notebook demonstrates how you can analyze and plot benchmark results from a single benchmark run.\n", "Several projects exist in the `examples` folder, but this notebook assumes we are working on the\n", "JVM part of the `kotlin-multiplatform` project. But the same approach can be used for the other projects.\n", "\n", "First, you need to run the benchmark. This can be done by running this command from the root of the project:\n", "\n", "```shell\n", "./gradlew :examples:kotlin-multiplatform:jvmBenchmark\n", "```\n", "\n", "Once it is completed, run this notebook, and it will automatically find the latest result." ],
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"source" : "%use serialization, dataframe, kandy",
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"source" : [ "import java.nio.file.Files\n", "import java.nio.file.attribute.BasicFileAttributes\n", "import kotlin.io.path.exists\n", "import kotlin.io.path.forEachDirectoryEntry\n", "import kotlin.io.path.isDirectory\n", "import kotlin.io.path.listDirectoryEntries\n", "import kotlin.io.path.readText\n", "\n", "// Find latest result file, based on the their timestamp.\n", "//val runsDir = notebook.workingDir.resolve(\"...\")\n", "val outputFile = notebook.workingDir.resolve(\"../main.json\")\n", "val benchmarkData = outputFile.readText()\n", "benchmarkData" ],
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"text/markdown" : "```json\n[\n {\n \"jmhVersion\": \"1.37\",\n \"benchmark\": \"test.CompareHypothesis.invSqrtBaseline\",\n \"mode\": \"thrpt\",\n \"threads\": 1,\n \"forks\": 1,\n \"jvm\": \"/Users/filipp.zhinkin/.gradle/jdks/amazon_com_inc_-8-aarch64-os_x/amazon-corretto-8.jdk/Contents/Home/jre/bin/java\",\n \"jvmArgs\": [\n \"-Dfile.encoding=UTF-8\",\n \"-Duser.country=US\",\n \"-Duser.language=en\",\n \"-Duser.variant\"\n ],\n \"jdkVersion\": \"1.8.0_422\",\n \"vmName\": \"OpenJDK 64-Bit Server VM\",\n \"vmVersion\": \"25.422-b05\",\n \"warmupIterations\": 3,\n \"warmupTime\": \"5 s\",\n \"warmupBatchSize\": 1,\n \"measurementIterations\": 3,\n \"measurementTime\": \"5 s\",\n \"measurementBatchSize\": 1,\n \"primaryMetric\": {\n \"score\": 8.203977554632516E8,\n \"scoreError\": 1.1337770231189007E7,\n \"scoreConfidence\": [\n 8.090599852320626E8,\n 8.317355256944406E8\n ],\n \"scorePercentiles\": {\n \"0.0\": 8.197133288452934E8,\n \"50.0\": 8.205531948987743E8,\n \"90.0\": 8.209267426456873E8,\n \"95.0\": 8.209267426456873E8,\n \"99.0\": 8.209267426456873E8,\n \"99.9\": 8.209267426456873E8,\n \"99.99\": 8.209267426456873E8,\n \"99.999\": 8.209267426456873E8,\n \"99.9999\": 8.209267426456873E8,\n \"100.0\": 8.209267426456873E8\n },\n \"scoreUnit\": \"ops/s\",\n \"rawData\": [\n [\n 8.197133288452934E8,\n 8.209267426456873E8,\n 8.205531948987743E8\n ]\n ]\n },\n \"secondaryMetrics\": {}\n },\n {\n \"jmhVersion\": \"1.37\",\n \"benchmark\": \"test.CompareHypothesis.invSqrtOptimized\",\n \"mode\": \"thrpt\",\n \"threads\": 1,\n \"forks\": 1,\n \"jvm\": \"/Users/filipp.zhinkin/.gradle/jdks/amazon_com_inc_-8-aarch64-os_x/amazon-corretto-8.jdk/Contents/Home/jre/bin/java\",\n \"jvmArgs\": [\n \"-Dfile.encoding=UTF-8\",\n \"-Duser.country=US\",\n \"-Duser.language=en\",\n \"-Duser.variant\"\n ],\n \"jdkVersion\": \"1.8.0_422\",\n \"vmName\": \"OpenJDK 64-Bit Server VM\",\n \"vmVersion\": \"25.422-b05\",\n \"warmupIterations\": 3,\n \"warmupTime\": \"5 s\",\n \"warmupBatchSize\": 1,\n \"measurementIterations\": 3,\n \"measurementTime\": \"5 s\",\n \"measurementBatchSize\": 1,\n \"primaryMetric\": {\n \"score\": 6.561712309456968E8,\n \"scoreError\": 1.06182674607018E7,\n \"scoreConfidence\": [\n 6.45552963484995E8,\n 6.667894984063987E8\n ],\n \"scorePercentiles\": {\n \"0.0\": 6.55560459019124E8,\n \"50.0\": 6.562337813814477E8,\n \"90.0\": 6.567194524365189E8,\n \"95.0\": 6.567194524365189E8,\n \"99.0\": 6.567194524365189E8,\n \"99.9\": 6.567194524365189E8,\n \"99.99\": 6.567194524365189E8,\n \"99.999\": 6.567194524365189E8,\n \"99.9999\": 6.567194524365189E8,\n \"100.0\": 6.567194524365189E8\n },\n \"scoreUnit\": \"ops/s\",\n \"rawData\": [\n [\n 6.567194524365189E8,\n 6.562337813814477E8,\n 6.55560459019124E8\n ]\n ]\n },\n \"secondaryMetrics\": {}\n }\n]\n```"
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"source" : [ "// Helper class for tracking the information we need to use.\n", "data class Benchmark(val name: String, val params: String, val score: Double, val error: Double, val unit: String)\n", "\n", "// Split benchmark results into groups. Generally, each group consist of all tests from one test file,\n", "// except when it is an parameterized test. In this case, each test (with all its variants) are put\n", "// in its own group.\n", "val benchmarkGroups = benchmarkData\n", " .groupBy {\n", " it.benchmark.substringBeforeLast(\".\")\n", " }\n", " .mapValues { group ->\n", " val benchmarks = group.value.map { benchmark ->\n", " // Parameters are specific to each test. `deserializeJson()` will generate the appropriate data classes,\n", " // but for generic handling of parameters we would need to fallback to reading the JSON. In this case\n", " // we just handle them through the typed API.\n", " val name = benchmark.benchmark\n", " Benchmark(\n", " name,\n", " \"\",\n", " benchmark.primaryMetric.score,\n", " benchmark.primaryMetric.scoreError,\n", " benchmark.primaryMetric.scoreUnit\n", " )\n", " }\n", " benchmarks.toDataFrame()\n", " }\n", "\n", "// Un-commont this to see the benchmark data as DataFrames\n", "// benchmarkGroups.forEach {\n", "// DISPLAY(it.value)\n", "// }" ],
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"source" : [ "// Prepare the data frames for plotting by:\n", "// - Add calculated columns for errorMin / errorMax\n", "// - Tests with parameters use the parameter values as the label\n", "// - Tests without paramaters use the test name as the label\n", "val plotData = benchmarkGroups.mapValues {\n", " it.value\n", " .add(\"errorMin\") { it.getValue<Double>(\"score\") - it.getValue<Double>(\"error\") }\n", " .add(\"errorMax\") { it.getValue<Double>(\"score\") + it.getValue<Double>(\"error\") }\n", " .insert(\"label\") {\n", " // Re-format the benchmark labels to make them look \"nicer\"\n", " if (!it.getValue<String>(\"params\").isBlank()) {\n", " it.getValue<String>(\"params\").replace(\",\", \"\\n\")\n", " } else {\n", " it.getValue<String>(\"name\").substringAfterLast(\".\").removeSuffix(\"Benchmark\")\n", " }\n", " }.at(0)\n", " .remove(\"name\", \"params\")\n", "}" ],
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"source" : [ "import org.jetbrains.letsPlot.Geom\n", "import org.jetbrains.letsPlot.core.spec.plotson.coord\n", "import org.jetbrains.letsPlot.themes.margin\n", "\n", "// Plot each group as a bar plot with the error displayed as error bars.\n", "// This approach assumes that each group has tests roughly within the same \"scale\".\n", "// If this is not the case, some plots might look very squished. If this happens,\n", "// you can play around with using a LOG10 scale or modifying the limits to focus\n", "// on the changes.\n", "plotData.forEach { (fileName, dataframe) ->\n", " val plot = dataframe.plot {\n", " bars {\n", " x(\"label\") {\n", " axis.name = \"\"\n", " }\n", " y(\"score\")\n", " }\n", " errorBars {\n", " x(\"label\")\n", " y(\"score\")\n", " yMin(\"errorMin\")\n", " yMax(\"errorMax\")\n", " }\n", " coordinatesTransformation = CoordinatesTransformation.cartesianFlipped()\n", " // y.axis.limits = dataframe.min(\"errorMin\")..dataframe.max(\"errorMax\")\n", " layout {\n", " this.yAxisLabel = dataframe.first().getValue<String>(\"unit\")\n", " style {\n", " global {\n", " title {\n", " margin(10.0, 0.0)\n", " }\n", " text {\n", " fontFamily = FontFamily.MONO\n", " }\n", " }\n", " }\n", " // Adjust the height of the Kandy plot based on the number of tests.\n", " size = 800 to ((50 * dataframe.size().nrow) + 100)\n", " }\n", " }\n", " DISPLAY(HTML(\"<h4 >$fileName</h4>\"))\n", " DISPLAY(plot)\n", "}" ],
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