forked from kscience/kmath
Adjust benchmarks.
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@ -11,7 +11,10 @@ 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.Scope
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import org.openjdk.jmh.annotations.State
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import org.openjdk.jmh.annotations.State
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import space.kscience.kmath.misc.UnstableKMathAPI
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import space.kscience.kmath.misc.UnstableKMathAPI
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import space.kscience.kmath.operations.*
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import space.kscience.kmath.operations.BigIntField
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import space.kscience.kmath.operations.JBigIntegerField
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import space.kscience.kmath.operations.invoke
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import space.kscience.kmath.operations.parseBigInteger
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import java.math.BigInteger
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import java.math.BigInteger
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@ -19,12 +22,24 @@ import java.math.BigInteger
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@State(Scope.Benchmark)
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@State(Scope.Benchmark)
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internal class BigIntBenchmark {
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internal class BigIntBenchmark {
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val kmSmallNumber = BigIntField.number(100)
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val jvmSmallNumber = JBigIntegerField.number(100)
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val kmNumber = BigIntField.number(Int.MAX_VALUE)
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val kmNumber = BigIntField.number(Int.MAX_VALUE)
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val jvmNumber = JBigIntegerField.number(Int.MAX_VALUE)
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val jvmNumber = JBigIntegerField.number(Int.MAX_VALUE)
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val largeKmNumber = BigIntField { number(11).pow(100_000U) }
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val kmLargeNumber = BigIntField { number(11).pow(100_000U) }
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val largeJvmNumber: BigInteger = JBigIntegerField { number(11).pow(100_000) }
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val jvmLargeNumber: BigInteger = JBigIntegerField { number(11).pow(100_000) }
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val bigExponent = 50_000
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val bigExponent = 50_000
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@Benchmark
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fun kmSmallAdd(blackhole: Blackhole) = BigIntField {
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blackhole.consume(kmSmallNumber + kmSmallNumber + kmSmallNumber)
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}
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@Benchmark
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fun jvmSmallAdd(blackhole: Blackhole) = JBigIntegerField {
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blackhole.consume(jvmSmallNumber + jvmSmallNumber + jvmSmallNumber)
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}
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@Benchmark
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@Benchmark
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fun kmAdd(blackhole: Blackhole) = BigIntField {
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fun kmAdd(blackhole: Blackhole) = BigIntField {
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blackhole.consume(kmNumber + kmNumber + kmNumber)
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blackhole.consume(kmNumber + kmNumber + kmNumber)
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@ -37,12 +52,12 @@ internal class BigIntBenchmark {
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@Benchmark
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@Benchmark
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fun kmAddLarge(blackhole: Blackhole) = BigIntField {
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fun kmAddLarge(blackhole: Blackhole) = BigIntField {
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blackhole.consume(largeKmNumber + largeKmNumber + largeKmNumber)
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blackhole.consume(kmLargeNumber + kmLargeNumber + kmLargeNumber)
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}
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}
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@Benchmark
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@Benchmark
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fun jvmAddLarge(blackhole: Blackhole) = JBigIntegerField {
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fun jvmAddLarge(blackhole: Blackhole) = JBigIntegerField {
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blackhole.consume(largeJvmNumber + largeJvmNumber + largeJvmNumber)
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blackhole.consume(jvmLargeNumber + jvmLargeNumber + jvmLargeNumber)
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}
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}
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@Benchmark
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@Benchmark
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@ -52,7 +67,7 @@ internal class BigIntBenchmark {
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@Benchmark
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@Benchmark
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fun kmMultiplyLarge(blackhole: Blackhole) = BigIntField {
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fun kmMultiplyLarge(blackhole: Blackhole) = BigIntField {
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blackhole.consume(largeKmNumber*largeKmNumber)
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blackhole.consume(kmLargeNumber*kmLargeNumber)
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}
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}
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@Benchmark
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@Benchmark
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@ -62,7 +77,7 @@ internal class BigIntBenchmark {
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@Benchmark
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@Benchmark
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fun jvmMultiplyLarge(blackhole: Blackhole) = JBigIntegerField {
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fun jvmMultiplyLarge(blackhole: Blackhole) = JBigIntegerField {
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blackhole.consume(largeJvmNumber*largeJvmNumber)
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blackhole.consume(jvmLargeNumber*jvmLargeNumber)
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}
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}
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@Benchmark
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@Benchmark
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@ -11,7 +11,6 @@ import kotlinx.benchmark.Scope
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import kotlinx.benchmark.State
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import kotlinx.benchmark.State
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import space.kscience.kmath.commons.linear.CMLinearSpace
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import space.kscience.kmath.commons.linear.CMLinearSpace
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import space.kscience.kmath.ejml.EjmlLinearSpaceDDRM
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import space.kscience.kmath.ejml.EjmlLinearSpaceDDRM
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import space.kscience.kmath.linear.LinearSpace
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import space.kscience.kmath.linear.invoke
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import space.kscience.kmath.linear.invoke
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import space.kscience.kmath.linear.linearSpace
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import space.kscience.kmath.linear.linearSpace
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import space.kscience.kmath.operations.DoubleField
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import space.kscience.kmath.operations.DoubleField
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@ -26,8 +25,12 @@ internal class DotBenchmark {
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const val dim = 1000
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const val dim = 1000
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//creating invertible matrix
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//creating invertible matrix
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val matrix1 = LinearSpace.double.buildMatrix(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
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val matrix1 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j ->
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val matrix2 = LinearSpace.double.buildMatrix(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
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if (i <= j) random.nextDouble() else 0.0
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}
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val matrix2 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j ->
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if (i <= j) random.nextDouble() else 0.0
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}
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val cmMatrix1 = CMLinearSpace { matrix1.toCM() }
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val cmMatrix1 = CMLinearSpace { matrix1.toCM() }
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val cmMatrix2 = CMLinearSpace { matrix2.toCM() }
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val cmMatrix2 = CMLinearSpace { matrix2.toCM() }
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@ -37,37 +40,32 @@ internal class DotBenchmark {
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}
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}
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@Benchmark
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@Benchmark
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fun cmDot(blackhole: Blackhole) {
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fun cmDotWithConversion(blackhole: Blackhole) = CMLinearSpace {
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CMLinearSpace {
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blackhole.consume(matrix1 dot matrix2)
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blackhole.consume(cmMatrix1 dot cmMatrix2)
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}
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}
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}
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@Benchmark
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@Benchmark
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fun ejmlDot(blackhole: Blackhole) {
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fun cmDot(blackhole: Blackhole) = CMLinearSpace {
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EjmlLinearSpaceDDRM {
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blackhole.consume(cmMatrix1 dot cmMatrix2)
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blackhole.consume(ejmlMatrix1 dot ejmlMatrix2)
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}
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}
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}
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@Benchmark
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@Benchmark
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fun ejmlDotWithConversion(blackhole: Blackhole) {
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fun ejmlDot(blackhole: Blackhole) = EjmlLinearSpaceDDRM {
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EjmlLinearSpaceDDRM {
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blackhole.consume(ejmlMatrix1 dot ejmlMatrix2)
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blackhole.consume(matrix1 dot matrix2)
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}
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}
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}
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@Benchmark
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@Benchmark
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fun bufferedDot(blackhole: Blackhole) {
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fun ejmlDotWithConversion(blackhole: Blackhole) = EjmlLinearSpaceDDRM {
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with(DoubleField.linearSpace(Buffer.Companion::auto)) {
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blackhole.consume(matrix1 dot matrix2)
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blackhole.consume(matrix1 dot matrix2)
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}
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}
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}
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@Benchmark
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@Benchmark
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fun doubleDot(blackhole: Blackhole) {
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fun bufferedDot(blackhole: Blackhole) = with(DoubleField.linearSpace(Buffer.Companion::auto)) {
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with(Double.algebra.linearSpace) {
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blackhole.consume(matrix1 dot matrix2)
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blackhole.consume(matrix1 dot matrix2)
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}
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}
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@Benchmark
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fun doubleDot(blackhole: Blackhole) = with(Double.algebra.linearSpace) {
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blackhole.consume(matrix1 dot matrix2)
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}
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}
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}
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}
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@ -0,0 +1,34 @@
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/*
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* Copyright 2018-2021 KMath contributors.
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* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
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*/
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package space.kscience.kmath.linear
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import space.kscience.kmath.operations.algebra
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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(12224)
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val dim = 1000
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//creating invertible matrix
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val matrix1 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j ->
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if (i <= j) random.nextDouble() else 0.0
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}
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val matrix2 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j ->
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if (i <= j) random.nextDouble() else 0.0
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}
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val time = measureTimeMillis {
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with(Double.algebra.linearSpace) {
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repeat(10) {
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val res = matrix1 dot matrix2
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}
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}
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}
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println(time)
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}
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