Dev #194

Merged
altavir merged 266 commits from dev into master 2021-01-20 17:32:32 +03:00
10 changed files with 74 additions and 72 deletions
Showing only changes of commit 89c0d863d2 - Show all commits

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@ -69,7 +69,7 @@ benchmark {
// This one matches sourceSet name above
configurations.register("fast") {
warmups = 5 // number of warmup iterations
warmups = 1 // number of warmup iterations
iterations = 3 // number of iterations
iterationTime = 500 // time in seconds per iteration
iterationTimeUnit = "ms" // time unity for iterationTime, default is seconds

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@ -1,4 +1,4 @@
package kscience.kmath.structures
package kscience.kmath.benchmarks
import org.openjdk.jmh.annotations.Benchmark
import org.openjdk.jmh.annotations.Scope

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@ -1,7 +1,9 @@
package kscience.kmath.structures
package kscience.kmath.benchmarks
import kscience.kmath.operations.Complex
import kscience.kmath.operations.complex
import kscience.kmath.structures.MutableBuffer
import kscience.kmath.structures.RealBuffer
import org.openjdk.jmh.annotations.Benchmark
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State

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@ -0,0 +1,50 @@
package kscience.kmath.linear
import kotlinx.benchmark.Benchmark
import kscience.kmath.commons.linear.CMMatrixContext
import kscience.kmath.commons.linear.CMMatrixContext.dot
import kscience.kmath.commons.linear.inverse
import kscience.kmath.commons.linear.toCM
import kscience.kmath.ejml.EjmlMatrixContext
import kscience.kmath.ejml.inverse
import kscience.kmath.ejml.toEjml
import kscience.kmath.operations.invoke
import kscience.kmath.structures.Matrix
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
import kotlin.random.Random
@State(Scope.Benchmark)
class LinearAlgebraBenchmark {
companion object {
val random = Random(1224)
val dim = 100
//creating invertible matrix
val u = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
val l = Matrix.real(dim, dim) { i, j -> if (i >= j) random.nextDouble() else 0.0 }
val matrix = l dot u
}
@Benchmark
fun kmathLUPInversion() {
MatrixContext.real.inverse(matrix)
}
@Benchmark
fun cmLUPInversion() {
CMMatrixContext {
val cm = matrix.toCM() //avoid overhead on conversion
inverse(cm)
}
}
@Benchmark
fun ejmlInverse() {
EjmlMatrixContext {
val km = matrix.toEjml() //avoid overhead on conversion
inverse(km)
}
}
}

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@ -1,4 +1,4 @@
package kscience.kmath.structures
package kscience.kmath.benchmarks
import kotlinx.benchmark.Benchmark
import kscience.kmath.commons.linear.CMMatrixContext
@ -7,8 +7,8 @@ import kscience.kmath.commons.linear.toCM
import kscience.kmath.ejml.EjmlMatrixContext
import kscience.kmath.ejml.toEjml
import kscience.kmath.linear.real
import kscience.kmath.operations.RealField
import kscience.kmath.operations.invoke
import kscience.kmath.structures.Matrix
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
import kotlin.random.Random
@ -44,6 +44,15 @@ class MultiplicationBenchmark {
}
}
@Benchmark
fun ejmlMultiplicationwithConversion() {
val ejmlMatrix1 = matrix1.toEjml()
val ejmlMatrix2 = matrix2.toEjml()
EjmlMatrixContext.invoke {
ejmlMatrix1 dot ejmlMatrix2
}
}
@Benchmark
fun bufferedMultiplication() {
matrix1 dot matrix2

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@ -1,7 +1,8 @@
package kscience.kmath.structures
package kscience.kmath.benchmarks
import kscience.kmath.operations.RealField
import kscience.kmath.operations.invoke
import kscience.kmath.structures.*
import org.openjdk.jmh.annotations.Benchmark
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State

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@ -1,7 +1,10 @@
package kscience.kmath.structures
package kscience.kmath.benchmarks
import kscience.kmath.operations.RealField
import kscience.kmath.operations.invoke
import kscience.kmath.structures.BufferedNDField
import kscience.kmath.structures.NDField
import kscience.kmath.structures.RealNDField
import kscience.kmath.viktor.ViktorNDField
import org.jetbrains.bio.viktor.F64Array
import org.openjdk.jmh.annotations.Benchmark
@ -36,7 +39,7 @@ internal class ViktorBenchmark {
@Benchmark
fun rawViktor() {
val one = F64Array.full(init = 1.0, shape = *intArrayOf(dim, dim))
val one = F64Array.full(init = 1.0, shape = intArrayOf(dim, dim))
var res = one
repeat(n) { res = res + one }
}

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@ -1,11 +0,0 @@
package kscience.kmath.utils
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
import kotlin.system.measureTimeMillis
internal inline fun measureAndPrint(title: String, block: () -> Unit) {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
val time = measureTimeMillis(block)
println("$title completed in $time millis")
}

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@ -1,52 +0,0 @@
package kscience.kmath.linear
import kscience.kmath.commons.linear.CMMatrixContext
import kscience.kmath.commons.linear.CMMatrixContext.dot
import kscience.kmath.commons.linear.inverse
import kscience.kmath.commons.linear.toCM
import kscience.kmath.ejml.EjmlMatrixContext
import kscience.kmath.ejml.inverse
import kscience.kmath.ejml.toEjml
import kscience.kmath.operations.RealField
import kscience.kmath.operations.invoke
import kscience.kmath.structures.Matrix
import kotlin.random.Random
import kotlin.system.measureTimeMillis
fun main() {
val random = Random(1224)
val dim = 100
//creating invertible matrix
val u = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
val l = Matrix.real(dim, dim) { i, j -> if (i >= j) random.nextDouble() else 0.0 }
val matrix = l dot u
val n = 5000 // iterations
MatrixContext.real {
repeat(50) { inverse(matrix) }
val inverseTime = measureTimeMillis { repeat(n) { inverse(matrix) } }
println("[kmath] Inversion of $n matrices $dim x $dim finished in $inverseTime millis")
}
//commons-math
val commonsTime = measureTimeMillis {
CMMatrixContext {
val cm = matrix.toCM() //avoid overhead on conversion
repeat(n) { inverse(cm) }
}
}
println("[commons-math] Inversion of $n matrices $dim x $dim finished in $commonsTime millis")
val ejmlTime = measureTimeMillis {
EjmlMatrixContext {
val km = matrix.toEjml() //avoid overhead on conversion
repeat(n) { inverse(km) }
}
}
println("[ejml] Inversion of $n matrices $dim x $dim finished in $ejmlTime millis")
}