Revise benchmarks code

Revise benchmarks code by using kotlinx.benchmark type aliases
(it will simplify creating multiplatform benchmarks), using
Blackhole class to consume results, moving all the constant state
to private companion objects

examples/src/benchmarks/kotlin/space/kscience/kmath/benchmarks/ArrayBenchmark.kt

@@ -1,34 +1,38 @@
 package space.kscience.kmath.benchmarks
 
-import org.openjdk.jmh.annotations.Benchmark
-import org.openjdk.jmh.annotations.Scope
-import org.openjdk.jmh.annotations.State
+import kotlinx.benchmark.Benchmark
+import kotlinx.benchmark.Blackhole
+import kotlinx.benchmark.Scope
+import kotlinx.benchmark.State
 import java.nio.IntBuffer
 
 @State(Scope.Benchmark)
 internal class ArrayBenchmark {
     @Benchmark
-    fun benchmarkArrayRead() {
+    fun benchmarkArrayRead(blackhole: Blackhole) {
         var res = 0
-        for (i in 1..space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.size) res += space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.array[space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.size - i]
+        for (i in 1..size) res += array[size - i]
+        blackhole.consume(res)
     }
 
     @Benchmark
-    fun benchmarkBufferRead() {
+    fun benchmarkBufferRead(blackhole: Blackhole) {
         var res = 0
-        for (i in 1..space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.size) res += space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.arrayBuffer[space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.size - i]
+        for (i in 1..size) res += arrayBuffer[size - i]
+        blackhole.consume(res)
     }
 
     @Benchmark
-    fun nativeBufferRead() {
+    fun nativeBufferRead(blackhole: Blackhole) {
         var res = 0
-        for (i in 1..space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.size) res += space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.nativeBuffer[space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.size - i]
+        for (i in 1..size) res += nativeBuffer[size - i]
+        blackhole.consume(res)
     }
 
-    companion object {
-        const val size: Int = 1000
-        val array: IntArray = IntArray(space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.size) { it }
-        val arrayBuffer: IntBuffer = IntBuffer.wrap(space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.array)
-        val nativeBuffer: IntBuffer = IntBuffer.allocate(space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.size).also { for (i in 0 until space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.size) it.put(i, i) }
+    private companion object {
+        private const val size = 1000
+        private val array = IntArray(size) { it }
+        private val arrayBuffer = IntBuffer.wrap(array)
+        private val nativeBuffer = IntBuffer.allocate(size).also { for (i in 0 until size) it.put(i, i) }
     }
 }

examples/src/benchmarks/kotlin/space/kscience/kmath/benchmarks/BufferBenchmark.kt

@@ -1,8 +1,8 @@
 package space.kscience.kmath.benchmarks
 
-import org.openjdk.jmh.annotations.Benchmark
-import org.openjdk.jmh.annotations.Scope
-import org.openjdk.jmh.annotations.State
+import kotlinx.benchmark.Benchmark
+import kotlinx.benchmark.Scope
+import kotlinx.benchmark.State
 import space.kscience.kmath.complex.Complex
 import space.kscience.kmath.complex.complex
 import space.kscience.kmath.structures.MutableBuffer
@@ -28,7 +28,7 @@ internal class BufferBenchmark {
         }
     }
 
-    companion object {
-        const val size: Int = 100
+    private companion object {
+        private const val size = 100
     }
 }

examples/src/benchmarks/kotlin/space/kscience/kmath/benchmarks/DotBenchmark.kt

@@ -1,8 +1,9 @@
 package space.kscience.kmath.benchmarks
 
 import kotlinx.benchmark.Benchmark
-import org.openjdk.jmh.annotations.Scope
-import org.openjdk.jmh.annotations.State
+import kotlinx.benchmark.Blackhole
+import kotlinx.benchmark.Scope
+import kotlinx.benchmark.State
 import space.kscience.kmath.commons.linear.CMMatrixContext
 import space.kscience.kmath.ejml.EjmlMatrixContext
 import space.kscience.kmath.linear.BufferMatrixContext
@@ -17,7 +18,7 @@ import kotlin.random.Random
 internal class DotBenchmark {
     companion object {
         val random = Random(12224)
-        val dim = 1000
+        const val dim = 1000
 
         //creating invertible matrix
         val matrix1 = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
@@ -31,37 +32,37 @@ internal class DotBenchmark {
     }
 
     @Benchmark
-    fun cmDot() {
+    fun cmDot(blackhole: Blackhole) {
         CMMatrixContext {
-            cmMatrix1 dot cmMatrix2
+            blackhole.consume(cmMatrix1 dot cmMatrix2)
         }
     }
 
     @Benchmark
-    fun ejmlDot() {
+    fun ejmlDot(blackhole: Blackhole) {
         EjmlMatrixContext {
-            ejmlMatrix1 dot ejmlMatrix2
+            blackhole.consume(ejmlMatrix1 dot ejmlMatrix2)
         }
     }
 
     @Benchmark
-    fun ejmlDotWithConversion() {
+    fun ejmlDotWithConversion(blackhole: Blackhole) {
         EjmlMatrixContext {
-            matrix1 dot matrix2
+            blackhole.consume(matrix1 dot matrix2)
         }
     }
 
     @Benchmark
-    fun bufferedDot() {
+    fun bufferedDot(blackhole: Blackhole) {
         BufferMatrixContext(RealField, Buffer.Companion::real).invoke {
-            matrix1 dot matrix2
+            blackhole.consume(matrix1 dot matrix2)
         }
     }
 
     @Benchmark
-    fun realDot() {
+    fun realDot(blackhole: Blackhole) {
         RealMatrixContext {
-            matrix1 dot matrix2
+            blackhole.consume(matrix1 dot matrix2)
         }
     }
 }

examples/src/benchmarks/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt

@@ -1,64 +1,62 @@
 package space.kscience.kmath.benchmarks
 
-import org.openjdk.jmh.annotations.Benchmark
-import org.openjdk.jmh.annotations.Scope
-import org.openjdk.jmh.annotations.State
+import kotlinx.benchmark.Benchmark
+import kotlinx.benchmark.Blackhole
+import kotlinx.benchmark.Scope
+import kotlinx.benchmark.State
 import space.kscience.kmath.asm.compile
 import space.kscience.kmath.ast.mstInField
 import space.kscience.kmath.expressions.Expression
 import space.kscience.kmath.expressions.expressionInField
 import space.kscience.kmath.expressions.invoke
 import space.kscience.kmath.expressions.symbol
-import space.kscience.kmath.operations.Field
 import space.kscience.kmath.operations.RealField
 import space.kscience.kmath.operations.bindSymbol
 import kotlin.random.Random
 
 @State(Scope.Benchmark)
 internal class ExpressionsInterpretersBenchmark {
-    private val algebra: Field<Double> = RealField
-    val x by symbol
-
     @Benchmark
-    fun functionalExpression() {
+    fun functionalExpression(blackhole: Blackhole) {
         val expr = algebra.expressionInField {
             val x = bindSymbol(x)
             x * const(2.0) + const(2.0) / x - const(16.0)
         }
 
-        invokeAndSum(expr)
+        invokeAndSum(expr, blackhole)
     }
 
     @Benchmark
-    fun mstExpression() {
+    fun mstExpression(blackhole: Blackhole) {
         val expr = algebra.mstInField {
             val x = bindSymbol(x)
             x * 2.0 + 2.0 / x - 16.0
         }
 
-        invokeAndSum(expr)
+        invokeAndSum(expr, blackhole)
     }
 
     @Benchmark
-    fun asmExpression() {
+    fun asmExpression(blackhole: Blackhole) {
         val expr = algebra.mstInField {
             val x = bindSymbol(x)
             x * 2.0 + 2.0 / x - 16.0
         }.compile()
 
-        invokeAndSum(expr)
+        invokeAndSum(expr, blackhole)
     }
 
     @Benchmark
-    fun rawExpression() {
+    fun rawExpression(blackhole: Blackhole) {
         val expr = Expression<Double> { args ->
             val x = args.getValue(x)
             x * 2.0 + 2.0 / x - 16.0
         }
-        invokeAndSum(expr)
+
+        invokeAndSum(expr, blackhole)
     }
 
-    private fun invokeAndSum(expr: Expression<Double>) {
+    private fun invokeAndSum(expr: Expression<Double>, blackhole: Blackhole) {
         val random = Random(0)
         var sum = 0.0
 
@@ -66,6 +64,11 @@ internal class ExpressionsInterpretersBenchmark {
             sum += expr(x to random.nextDouble())
         }
 
-        println(sum)
+        blackhole.consume(sum)
+    }
+
+    private companion object {
+        private val algebra = RealField
+        private val x by symbol
     }
 }

examples/src/benchmarks/kotlin/space/kscience/kmath/benchmarks/LinearAlgebraBenchmark.kt

@@ -1,8 +1,9 @@
 package space.kscience.kmath.benchmarks
 
 import kotlinx.benchmark.Benchmark
-import org.openjdk.jmh.annotations.Scope
-import org.openjdk.jmh.annotations.State
+import kotlinx.benchmark.Blackhole
+import kotlinx.benchmark.Scope
+import kotlinx.benchmark.State
 import space.kscience.kmath.commons.linear.CMMatrixContext
 import space.kscience.kmath.commons.linear.CMMatrixContext.dot
 import space.kscience.kmath.commons.linear.inverse
@@ -18,7 +19,7 @@ import kotlin.random.Random
 internal class LinearAlgebraBenchmark {
     companion object {
         val random = Random(1224)
-        val dim = 100
+        const val dim = 100
 
         //creating invertible matrix
         val u = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
@@ -27,21 +28,21 @@ internal class LinearAlgebraBenchmark {
     }
 
     @Benchmark
-    fun kmathLupInversion() {
-        MatrixContext.real.inverseWithLup(matrix)
+    fun kmathLupInversion(blackhole: Blackhole) {
+        blackhole.consume(MatrixContext.real.inverseWithLup(matrix))
     }
 
     @Benchmark
-    fun cmLUPInversion() {
+    fun cmLUPInversion(blackhole: Blackhole) {
         with(CMMatrixContext) {
-            inverse(matrix)
+            blackhole.consume(inverse(matrix))
         }
     }
 
     @Benchmark
-    fun ejmlInverse() {
+    fun ejmlInverse(blackhole: Blackhole) {
         with(EjmlMatrixContext) {
-            inverse(matrix)
+            blackhole.consume(inverse(matrix))
         }
     }
 }

examples/src/benchmarks/kotlin/space/kscience/kmath/benchmarks/NDFieldBenchmark.kt

@@ -1,8 +1,9 @@
 package space.kscience.kmath.benchmarks
 
-import org.openjdk.jmh.annotations.Benchmark
-import org.openjdk.jmh.annotations.Scope
-import org.openjdk.jmh.annotations.State
+import kotlinx.benchmark.Benchmark
+import kotlinx.benchmark.Blackhole
+import kotlinx.benchmark.Scope
+import kotlinx.benchmark.State
 import space.kscience.kmath.nd.*
 import space.kscience.kmath.operations.RealField
 import space.kscience.kmath.structures.Buffer
@@ -10,35 +11,38 @@ import space.kscience.kmath.structures.Buffer
 @State(Scope.Benchmark)
 internal class NDFieldBenchmark {
     @Benchmark
-    fun autoFieldAdd() {
+    fun autoFieldAdd(blackhole: Blackhole) {
         with(autoField) {
             var res: NDStructure<Double> = one
             repeat(n) { res += one }
+            blackhole.consume(res)
         }
     }
 
     @Benchmark
-    fun specializedFieldAdd() {
+    fun specializedFieldAdd(blackhole: Blackhole) {
         with(specializedField) {
             var res: NDStructure<Double> = one
             repeat(n) { res += 1.0 }
+            blackhole.consume(res)
         }
     }
 
 
     @Benchmark
-    fun boxingFieldAdd() {
+    fun boxingFieldAdd(blackhole: Blackhole) {
         with(genericField) {
             var res: NDStructure<Double> = one
             repeat(n) { res += 1.0 }
+            blackhole.consume(res)
         }
     }
 
-    companion object {
-        const val dim: Int = 1000
-        const val n: Int = 100
-        val autoField = NDAlgebra.auto(RealField, dim, dim)
-        val specializedField: RealNDField = NDAlgebra.real(dim, dim)
-        val genericField = NDAlgebra.field(RealField, Buffer.Companion::boxing, dim, dim)
+    private companion object {
+        private const val dim = 1000
+        private const val n = 100
+        private val autoField = NDAlgebra.auto(RealField, dim, dim)
+        private val specializedField = NDAlgebra.real(dim, dim)
+        private val genericField = NDAlgebra.field(RealField, Buffer.Companion::boxing, dim, dim)
     }
 }

examples/src/benchmarks/kotlin/space/kscience/kmath/benchmarks/ViktorBenchmark.kt

@@ -1,51 +1,61 @@
 package space.kscience.kmath.benchmarks
 
+import kotlinx.benchmark.Benchmark
+import kotlinx.benchmark.Blackhole
+import kotlinx.benchmark.Scope
+import kotlinx.benchmark.State
 import org.jetbrains.bio.viktor.F64Array
-import org.openjdk.jmh.annotations.Benchmark
-import org.openjdk.jmh.annotations.Scope
-import org.openjdk.jmh.annotations.State
-import space.kscience.kmath.nd.*
+import space.kscience.kmath.nd.NDAlgebra
+import space.kscience.kmath.nd.NDStructure
+import space.kscience.kmath.nd.auto
+import space.kscience.kmath.nd.real
 import space.kscience.kmath.operations.RealField
 import space.kscience.kmath.viktor.ViktorNDField
 
 @State(Scope.Benchmark)
 internal class ViktorBenchmark {
-    final val dim: Int = 1000
-    final val n: Int = 100
-
-    // automatically build context most suited for given type.
-    final val autoField: NDField<Double, RealField> = NDAlgebra.auto(RealField, dim, dim)
-    final val realField: RealNDField = NDAlgebra.real(dim, dim)
-    final val viktorField: ViktorNDField = ViktorNDField(dim, dim)
-
     @Benchmark
-    fun automaticFieldAddition() {
+    fun automaticFieldAddition(blackhole: Blackhole) {
         with(autoField) {
             var res: NDStructure<Double> = one
             repeat(n) { res += 1.0 }
+            blackhole.consume(res)
         }
     }
 
     @Benchmark
-    fun realFieldAddition() {
+    fun realFieldAddition(blackhole: Blackhole) {
         with(realField) {
             var res: NDStructure<Double> = one
             repeat(n) { res += 1.0 }
+            blackhole.consume(res)
         }
     }
 
     @Benchmark
-    fun viktorFieldAddition() {
+    fun viktorFieldAddition(blackhole: Blackhole) {
         with(viktorField) {
             var res = one
             repeat(n) { res += 1.0 }
+            blackhole.consume(res)
         }
     }
 
     @Benchmark
-    fun rawViktor() {
+    fun rawViktor(blackhole: Blackhole) {
         val one = F64Array.full(init = 1.0, shape = intArrayOf(dim, dim))
         var res = one
         repeat(n) { res = res + one }
+        blackhole.consume(res)
+    }
+
+    private companion object {
+        private const val dim = 1000
+        private const val n = 100
+
+        // automatically build context most suited for given type.
+        private val autoField = NDAlgebra.auto(RealField, dim, dim)
+        private val realField = NDAlgebra.real(dim, dim)
+        private val viktorField = ViktorNDField(dim, dim)
     }
 }

examples/src/benchmarks/kotlin/space/kscience/kmath/benchmarks/ViktorLogBenchmark.kt

@@ -1,48 +1,53 @@
 package space.kscience.kmath.benchmarks
 
+import kotlinx.benchmark.Benchmark
+import kotlinx.benchmark.Blackhole
+import kotlinx.benchmark.Scope
+import kotlinx.benchmark.State
 import org.jetbrains.bio.viktor.F64Array
-import org.openjdk.jmh.annotations.Benchmark
-import org.openjdk.jmh.annotations.Scope
-import org.openjdk.jmh.annotations.State
-import space.kscience.kmath.nd.*
+import space.kscience.kmath.nd.NDAlgebra
+import space.kscience.kmath.nd.auto
+import space.kscience.kmath.nd.real
 import space.kscience.kmath.operations.RealField
 import space.kscience.kmath.viktor.ViktorNDField
 
 @State(Scope.Benchmark)
 internal class ViktorLogBenchmark {
-    final val dim: Int = 1000
-    final val n: Int = 100
-
-    // automatically build context most suited for given type.
-    final val autoField: NDField<Double, RealField> = NDAlgebra.auto(RealField, dim, dim)
-    final val realField: RealNDField = NDAlgebra.real(dim, dim)
-    final val viktorField: ViktorNDField = ViktorNDField(intArrayOf(dim, dim))
-
-
     @Benchmark
-    fun realFieldLog() {
+    fun realFieldLog(blackhole: Blackhole) {
         with(realField) {
             val fortyTwo = produce { 42.0 }
             var res = one
             repeat(n) { res = ln(fortyTwo) }
+            blackhole.consume(res)
         }
     }
 
     @Benchmark
-    fun viktorFieldLog() {
+    fun viktorFieldLog(blackhole: Blackhole) {
         with(viktorField) {
             val fortyTwo = produce { 42.0 }
             var res = one
             repeat(n) { res = ln(fortyTwo) }
+            blackhole.consume(res)
         }
     }
 
     @Benchmark
-    fun rawViktorLog() {
+    fun rawViktorLog(blackhole: Blackhole) {
         val fortyTwo = F64Array.full(dim, dim, init = 42.0)
-        var res: F64Array
-        repeat(n) {
-            res = fortyTwo.log()
-        }
+        lateinit var res: F64Array
+        repeat(n) { res = fortyTwo.log() }
+        blackhole.consume(res)
+    }
+
+    private companion object {
+        private const val dim = 1000
+        private const val n = 100
+
+        // automatically build context most suited for given type.
+        private val autoField = NDAlgebra.auto(RealField, dim, dim)
+        private val realField = NDAlgebra.real(dim, dim)
+        private val viktorField = ViktorNDField(intArrayOf(dim, dim))
     }
 }