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Adjust benchmarks.

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This commit is contained in:
Alexander Nozik 2021-09-22 22:09:46 +03:00
parent 89eebbecb7
commit 974d73e25c
3 changed files with 77 additions and 30 deletions
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29
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/BigIntBenchmark.kt
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@ -11,7 +11,10 @@ import org.openjdk.jmh.annotations.Benchmark
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.operations.*
import space.kscience.kmath.operations.BigIntField
import space.kscience.kmath.operations.JBigIntegerField
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.operations.parseBigInteger
import java.math.BigInteger
@ -19,12 +22,24 @@ import java.math.BigInteger
@State(Scope.Benchmark)
internal class BigIntBenchmark {
val kmSmallNumber = BigIntField.number(100)
val jvmSmallNumber = JBigIntegerField.number(100)
val kmNumber = BigIntField.number(Int.MAX_VALUE)
val jvmNumber = JBigIntegerField.number(Int.MAX_VALUE)
val largeKmNumber = BigIntField { number(11).pow(100_000U) }
val largeJvmNumber: BigInteger = JBigIntegerField { number(11).pow(100_000) }
val kmLargeNumber = BigIntField { number(11).pow(100_000U) }
val jvmLargeNumber: BigInteger = JBigIntegerField { number(11).pow(100_000) }
val bigExponent = 50_000
@Benchmark
fun kmSmallAdd(blackhole: Blackhole) = BigIntField {
blackhole.consume(kmSmallNumber + kmSmallNumber + kmSmallNumber)
}
@Benchmark
fun jvmSmallAdd(blackhole: Blackhole) = JBigIntegerField {
blackhole.consume(jvmSmallNumber + jvmSmallNumber + jvmSmallNumber)
}
@Benchmark
fun kmAdd(blackhole: Blackhole) = BigIntField {
blackhole.consume(kmNumber + kmNumber + kmNumber)
@ -37,12 +52,12 @@ internal class BigIntBenchmark {
@Benchmark
fun kmAddLarge(blackhole: Blackhole) = BigIntField {
blackhole.consume(largeKmNumber + largeKmNumber + largeKmNumber)
blackhole.consume(kmLargeNumber + kmLargeNumber + kmLargeNumber)
}
@Benchmark
fun jvmAddLarge(blackhole: Blackhole) = JBigIntegerField {
blackhole.consume(largeJvmNumber + largeJvmNumber + largeJvmNumber)
blackhole.consume(jvmLargeNumber + jvmLargeNumber + jvmLargeNumber)
}
@Benchmark
@ -52,7 +67,7 @@ internal class BigIntBenchmark {
@Benchmark
fun kmMultiplyLarge(blackhole: Blackhole) = BigIntField {
blackhole.consume(largeKmNumber*largeKmNumber)
blackhole.consume(kmLargeNumber*kmLargeNumber)
}
@Benchmark
@ -62,7 +77,7 @@ internal class BigIntBenchmark {
@Benchmark
fun jvmMultiplyLarge(blackhole: Blackhole) = JBigIntegerField {
blackhole.consume(largeJvmNumber*largeJvmNumber)
blackhole.consume(jvmLargeNumber*jvmLargeNumber)
}
@Benchmark

44
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/DotBenchmark.kt
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@ -11,7 +11,6 @@ import kotlinx.benchmark.Scope
import kotlinx.benchmark.State
import space.kscience.kmath.commons.linear.CMLinearSpace
import space.kscience.kmath.ejml.EjmlLinearSpaceDDRM
import space.kscience.kmath.linear.LinearSpace
import space.kscience.kmath.linear.invoke
import space.kscience.kmath.linear.linearSpace
import space.kscience.kmath.operations.DoubleField
@ -26,8 +25,12 @@ internal class DotBenchmark {
const val dim = 1000
//creating invertible matrix
val matrix1 = LinearSpace.double.buildMatrix(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
val matrix2 = LinearSpace.double.buildMatrix(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
val matrix1 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j ->
if (i <= j) random.nextDouble() else 0.0
}
val matrix2 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j ->
if (i <= j) random.nextDouble() else 0.0
}
val cmMatrix1 = CMLinearSpace { matrix1.toCM() }
val cmMatrix2 = CMLinearSpace { matrix2.toCM() }
@ -37,37 +40,32 @@ internal class DotBenchmark {
}
@Benchmark
fun cmDot(blackhole: Blackhole) {
CMLinearSpace {
blackhole.consume(cmMatrix1 dot cmMatrix2)
}
fun cmDotWithConversion(blackhole: Blackhole) = CMLinearSpace {
blackhole.consume(matrix1 dot matrix2)
}
@Benchmark
fun ejmlDot(blackhole: Blackhole) {
EjmlLinearSpaceDDRM {
blackhole.consume(ejmlMatrix1 dot ejmlMatrix2)
}
fun cmDot(blackhole: Blackhole) = CMLinearSpace {
blackhole.consume(cmMatrix1 dot cmMatrix2)
}
@Benchmark
fun ejmlDotWithConversion(blackhole: Blackhole) {
EjmlLinearSpaceDDRM {
blackhole.consume(matrix1 dot matrix2)
}
fun ejmlDot(blackhole: Blackhole) = EjmlLinearSpaceDDRM {
blackhole.consume(ejmlMatrix1 dot ejmlMatrix2)
}
@Benchmark
fun bufferedDot(blackhole: Blackhole) {
with(DoubleField.linearSpace(Buffer.Companion::auto)) {
blackhole.consume(matrix1 dot matrix2)
}
fun ejmlDotWithConversion(blackhole: Blackhole) = EjmlLinearSpaceDDRM {
blackhole.consume(matrix1 dot matrix2)
}
@Benchmark
fun doubleDot(blackhole: Blackhole) {
with(Double.algebra.linearSpace) {
blackhole.consume(matrix1 dot matrix2)
}
fun bufferedDot(blackhole: Blackhole) = with(DoubleField.linearSpace(Buffer.Companion::auto)) {
blackhole.consume(matrix1 dot matrix2)
}
@Benchmark
fun doubleDot(blackhole: Blackhole) = with(Double.algebra.linearSpace) {
blackhole.consume(matrix1 dot matrix2)
}
}

34
examples/src/main/kotlin/space/kscience/kmath/linear/dotPerformance.kt Normal file
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@ -0,0 +1,34 @@
/*
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.linear
import space.kscience.kmath.operations.algebra
import kotlin.random.Random
import kotlin.system.measureTimeMillis
fun main() {
val random = Random(12224)
val dim = 1000
//creating invertible matrix
val matrix1 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j ->
if (i <= j) random.nextDouble() else 0.0
}
val matrix2 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j ->
if (i <= j) random.nextDouble() else 0.0
}
val time = measureTimeMillis {
with(Double.algebra.linearSpace) {
repeat(10) {
val res = matrix1 dot matrix2
}
}
}
println(time)
}