forked from kscience/kmath
Benchmark refactoring
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@ -69,7 +69,7 @@ benchmark {
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// This one matches sourceSet name above
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configurations.register("fast") {
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warmups = 5 // number of warmup iterations
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warmups = 1 // number of warmup iterations
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iterations = 3 // number of iterations
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iterationTime = 500 // time in seconds per iteration
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iterationTimeUnit = "ms" // time unity for iterationTime, default is seconds
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@ -1,4 +1,4 @@
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package kscience.kmath.structures
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package kscience.kmath.benchmarks
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import org.openjdk.jmh.annotations.Benchmark
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import org.openjdk.jmh.annotations.Scope
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@ -1,7 +1,9 @@
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package kscience.kmath.structures
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package kscience.kmath.benchmarks
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import kscience.kmath.operations.Complex
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import kscience.kmath.operations.complex
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import kscience.kmath.structures.MutableBuffer
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import kscience.kmath.structures.RealBuffer
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import org.openjdk.jmh.annotations.Benchmark
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import org.openjdk.jmh.annotations.Scope
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import org.openjdk.jmh.annotations.State
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@ -0,0 +1,50 @@
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package kscience.kmath.linear
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import kotlinx.benchmark.Benchmark
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import kscience.kmath.commons.linear.CMMatrixContext
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import kscience.kmath.commons.linear.CMMatrixContext.dot
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import kscience.kmath.commons.linear.inverse
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import kscience.kmath.commons.linear.toCM
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import kscience.kmath.ejml.EjmlMatrixContext
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import kscience.kmath.ejml.inverse
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import kscience.kmath.ejml.toEjml
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import kscience.kmath.operations.invoke
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import kscience.kmath.structures.Matrix
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import org.openjdk.jmh.annotations.Scope
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import org.openjdk.jmh.annotations.State
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import kotlin.random.Random
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@State(Scope.Benchmark)
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class LinearAlgebraBenchmark {
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companion object {
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val random = Random(1224)
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val dim = 100
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//creating invertible matrix
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val u = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
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val l = Matrix.real(dim, dim) { i, j -> if (i >= j) random.nextDouble() else 0.0 }
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val matrix = l dot u
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}
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@Benchmark
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fun kmathLUPInversion() {
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MatrixContext.real.inverse(matrix)
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}
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@Benchmark
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fun cmLUPInversion() {
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CMMatrixContext {
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val cm = matrix.toCM() //avoid overhead on conversion
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inverse(cm)
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}
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}
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@Benchmark
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fun ejmlInverse() {
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EjmlMatrixContext {
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val km = matrix.toEjml() //avoid overhead on conversion
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inverse(km)
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}
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}
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}
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@ -1,4 +1,4 @@
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package kscience.kmath.structures
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package kscience.kmath.benchmarks
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import kotlinx.benchmark.Benchmark
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import kscience.kmath.commons.linear.CMMatrixContext
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@ -7,8 +7,8 @@ import kscience.kmath.commons.linear.toCM
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import kscience.kmath.ejml.EjmlMatrixContext
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import kscience.kmath.ejml.toEjml
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import kscience.kmath.linear.real
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import kscience.kmath.operations.RealField
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import kscience.kmath.operations.invoke
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import kscience.kmath.structures.Matrix
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import org.openjdk.jmh.annotations.Scope
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import org.openjdk.jmh.annotations.State
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import kotlin.random.Random
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@ -44,6 +44,15 @@ class MultiplicationBenchmark {
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}
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}
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@Benchmark
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fun ejmlMultiplicationwithConversion() {
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val ejmlMatrix1 = matrix1.toEjml()
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val ejmlMatrix2 = matrix2.toEjml()
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EjmlMatrixContext.invoke {
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ejmlMatrix1 dot ejmlMatrix2
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}
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}
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@Benchmark
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fun bufferedMultiplication() {
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matrix1 dot matrix2
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@ -1,7 +1,8 @@
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package kscience.kmath.structures
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package kscience.kmath.benchmarks
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import kscience.kmath.operations.RealField
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import kscience.kmath.operations.invoke
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import kscience.kmath.structures.*
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import org.openjdk.jmh.annotations.Benchmark
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import org.openjdk.jmh.annotations.Scope
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import org.openjdk.jmh.annotations.State
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@ -1,7 +1,10 @@
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package kscience.kmath.structures
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package kscience.kmath.benchmarks
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import kscience.kmath.operations.RealField
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import kscience.kmath.operations.invoke
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import kscience.kmath.structures.BufferedNDField
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import kscience.kmath.structures.NDField
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import kscience.kmath.structures.RealNDField
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import kscience.kmath.viktor.ViktorNDField
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import org.jetbrains.bio.viktor.F64Array
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import org.openjdk.jmh.annotations.Benchmark
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@ -36,7 +39,7 @@ internal class ViktorBenchmark {
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@Benchmark
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fun rawViktor() {
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val one = F64Array.full(init = 1.0, shape = *intArrayOf(dim, dim))
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val one = F64Array.full(init = 1.0, shape = intArrayOf(dim, dim))
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var res = one
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repeat(n) { res = res + one }
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}
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@ -1,11 +0,0 @@
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package kscience.kmath.utils
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import kotlin.contracts.InvocationKind
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import kotlin.contracts.contract
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import kotlin.system.measureTimeMillis
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internal inline fun measureAndPrint(title: String, block: () -> Unit) {
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contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
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val time = measureTimeMillis(block)
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println("$title completed in $time millis")
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}
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@ -1,52 +0,0 @@
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package kscience.kmath.linear
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import kscience.kmath.commons.linear.CMMatrixContext
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import kscience.kmath.commons.linear.CMMatrixContext.dot
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import kscience.kmath.commons.linear.inverse
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import kscience.kmath.commons.linear.toCM
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import kscience.kmath.ejml.EjmlMatrixContext
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import kscience.kmath.ejml.inverse
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import kscience.kmath.ejml.toEjml
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import kscience.kmath.operations.RealField
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import kscience.kmath.operations.invoke
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import kscience.kmath.structures.Matrix
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import kotlin.random.Random
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import kotlin.system.measureTimeMillis
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fun main() {
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val random = Random(1224)
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val dim = 100
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//creating invertible matrix
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val u = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
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val l = Matrix.real(dim, dim) { i, j -> if (i >= j) random.nextDouble() else 0.0 }
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val matrix = l dot u
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val n = 5000 // iterations
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MatrixContext.real {
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repeat(50) { inverse(matrix) }
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val inverseTime = measureTimeMillis { repeat(n) { inverse(matrix) } }
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println("[kmath] Inversion of $n matrices $dim x $dim finished in $inverseTime millis")
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}
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//commons-math
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val commonsTime = measureTimeMillis {
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CMMatrixContext {
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val cm = matrix.toCM() //avoid overhead on conversion
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repeat(n) { inverse(cm) }
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}
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}
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println("[commons-math] Inversion of $n matrices $dim x $dim finished in $commonsTime millis")
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val ejmlTime = measureTimeMillis {
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EjmlMatrixContext {
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val km = matrix.toEjml() //avoid overhead on conversion
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repeat(n) { inverse(km) }
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}
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}
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println("[ejml] Inversion of $n matrices $dim x $dim finished in $ejmlTime millis")
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}
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