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Optimize Real NDField

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This commit is contained in:
Alexander Nozik 2021-01-24 10:15:16 +03:00
parent 1cb41f4dc2
commit 9829a16a32
11 changed files with 226 additions and 78 deletions
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30
examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/ViktorBenchmark.kt
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@ -15,9 +15,9 @@ internal class ViktorBenchmark {
final val n: Int = 100
// automatically build context most suited for given type.
final val autoField: BufferedNDField<Double, RealField> = NDAlgebra.auto(RealField, dim, dim)
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))
final val viktorField: ViktorNDField = ViktorNDField(dim, dim)
@Benchmark
fun automaticFieldAddition() {
@ -27,6 +27,14 @@ internal class ViktorBenchmark {
}
}
@Benchmark
fun realFieldAddition() {
realField {
var res: NDStructure<Double> = one
repeat(n) { res += one }
}
}
@Benchmark
fun viktorFieldAddition() {
viktorField {
@ -41,22 +49,4 @@ internal class ViktorBenchmark {
var res = one
repeat(n) { res = res + one }
}
@Benchmark
fun realFieldLog() {
realField {
val fortyTwo = produce { 42.0 }
var res = one
repeat(n) { res = ln(fortyTwo) }
}
}
@Benchmark
fun rawViktorLog() {
val fortyTwo = F64Array.full(dim, dim, init = 42.0)
var res: F64Array
repeat(n) {
res = fortyTwo.log()
}
}
}

40
examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/ViktorLogBenchmark.kt Normal file
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@ -0,0 +1,40 @@
package kscience.kmath.benchmarks
import kscience.kmath.nd.*
import kscience.kmath.operations.RealField
import kscience.kmath.operations.invoke
import kscience.kmath.viktor.ViktorNDField
import org.jetbrains.bio.viktor.F64Array
import org.openjdk.jmh.annotations.Benchmark
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
@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: BufferedNDField<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() {
realField {
val fortyTwo = produce { 42.0 }
var res = one
repeat(n) { res = ln(fortyTwo) }
}
}
@Benchmark
fun rawViktorLog() {
val fortyTwo = F64Array.full(dim, dim, init = 42.0)
var res: F64Array
repeat(n) {
res = fortyTwo.log()
}
}
}

39
examples/src/main/kotlin/kscience/kmath/structures/NDField.kt
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@ -5,6 +5,7 @@ import kscience.kmath.nd.*
import kscience.kmath.nd4j.Nd4jArrayField
import kscience.kmath.operations.RealField
import kscience.kmath.operations.invoke
import kscience.kmath.viktor.ViktorNDField
import org.nd4j.linalg.factory.Nd4j
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
@ -25,18 +26,15 @@ fun main() {
// automatically build context most suited for given type.
val autoField = NDAlgebra.auto(RealField, dim, dim)
// specialized nd-field for Double. It works as generic Double field as well
val specializedField = NDAlgebra.real(dim, dim)
val realField = NDAlgebra.real(dim, dim)
//A generic boxing field. It should be used for objects, not primitives.
val boxingField = NDAlgebra.field(RealField, Buffer.Companion::boxing, dim, dim)
// Nd4j specialized field.
val nd4jField = Nd4jArrayField.real(dim, dim)
measureAndPrint("Automatic field addition") {
autoField {
var res: NDStructure<Double> = one
repeat(n) { res += 1.0 }
}
}
//viktor field
val viktorField = ViktorNDField(dim,dim)
//parallel processing based on Java Streams
val parallelField = NDAlgebra.realWithStream(dim,dim)
measureAndPrint("Boxing addition") {
boxingField {
@ -46,7 +44,7 @@ fun main() {
}
measureAndPrint("Specialized addition") {
specializedField {
realField {
var res: NDStructure<Double> = one
repeat(n) { res += 1.0 }
}
@ -59,8 +57,29 @@ fun main() {
}
}
measureAndPrint("Viktor addition") {
viktorField {
var res: NDStructure<Double> = one
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Parallel stream addition") {
parallelField {
var res: NDStructure<Double> = one
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Automatic field addition") {
autoField {
var res: NDStructure<Double> = one
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Lazy addition") {
val res = specializedField.one.mapAsync(GlobalScope) {
val res = realField.one.mapAsync(GlobalScope) {
var c = 0.0
repeat(n) {
c += 1.0

103
examples/src/main/kotlin/kscience/kmath/structures/ParallelRealNDField.kt Normal file
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@ -0,0 +1,103 @@
package kscience.kmath.structures
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.nd.*
import kscience.kmath.operations.ExtendedField
import kscience.kmath.operations.RealField
import kscience.kmath.operations.RingWithNumbers
import java.util.*
import java.util.stream.IntStream
/**
* A demonstration implementation of NDField over Real using Java [DoubleStream] for parallel execution
*/
@OptIn(UnstableKMathAPI::class)
public class StreamRealNDField(
shape: IntArray,
) : BufferedNDField<Double, RealField>(shape, RealField, Buffer.Companion::real),
RingWithNumbers<NDStructure<Double>>,
ExtendedField<NDStructure<Double>> {
override val zero: NDBuffer<Double> by lazy { produce { zero } }
override val one: NDBuffer<Double> by lazy { produce { one } }
override fun number(value: Number): NDBuffer<Double> {
val d = value.toDouble() // minimize conversions
return produce { d }
}
override val NDStructure<Double>.buffer: RealBuffer
get() = when {
!shape.contentEquals(this@StreamRealNDField.shape) -> throw ShapeMismatchException(
this@StreamRealNDField.shape,
shape
)
this is NDBuffer && this.strides == this@StreamRealNDField.strides -> this.buffer as RealBuffer
else -> RealBuffer(strides.linearSize) { offset -> get(strides.index(offset)) }
}
override fun produce(initializer: RealField.(IntArray) -> Double): NDBuffer<Double> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
val index = strides.index(offset)
RealField.initializer(index)
}.toArray()
return NDBuffer(strides, array.asBuffer())
}
override fun map(
arg: NDStructure<Double>,
transform: RealField.(Double) -> Double,
): NDBuffer<Double> {
val array = Arrays.stream(arg.buffer.array).parallel().map { RealField.transform(it) }.toArray()
return NDBuffer(strides, array.asBuffer())
}
override fun mapIndexed(
arg: NDStructure<Double>,
transform: RealField.(index: IntArray, Double) -> Double,
): NDBuffer<Double> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
RealField.transform(
strides.index(offset),
arg.buffer.array[offset]
)
}.toArray()
return NDBuffer(strides, array.asBuffer())
}
override fun combine(
a: NDStructure<Double>,
b: NDStructure<Double>,
transform: RealField.(Double, Double) -> Double,
): NDBuffer<Double> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
RealField.transform(a.buffer.array[offset], b.buffer.array[offset])
}.toArray()
return NDBuffer(strides, array.asBuffer())
}
override fun power(arg: NDStructure<Double>, pow: Number): NDBuffer<Double> = map(arg) { power(it, pow) }
override fun exp(arg: NDStructure<Double>): NDBuffer<Double> = map(arg) { exp(it) }
override fun ln(arg: NDStructure<Double>): NDBuffer<Double> = map(arg) { ln(it) }
override fun sin(arg: NDStructure<Double>): NDBuffer<Double> = map(arg) { sin(it) }
override fun cos(arg: NDStructure<Double>): NDBuffer<Double> = map(arg) { cos(it) }
override fun tan(arg: NDStructure<Double>): NDBuffer<Double> = map(arg) { tan(it) }
override fun asin(arg: NDStructure<Double>): NDBuffer<Double> = map(arg) { asin(it) }
override fun acos(arg: NDStructure<Double>): NDBuffer<Double> = map(arg) { acos(it) }
override fun atan(arg: NDStructure<Double>): NDBuffer<Double> = map(arg) { atan(it) }
override fun sinh(arg: NDStructure<Double>): NDBuffer<Double> = map(arg) { sinh(it) }
override fun cosh(arg: NDStructure<Double>): NDBuffer<Double> = map(arg) { cosh(it) }
override fun tanh(arg: NDStructure<Double>): NDBuffer<Double> = map(arg) { tanh(it) }
override fun asinh(arg: NDStructure<Double>): NDBuffer<Double> = map(arg) { asinh(it) }
override fun acosh(arg: NDStructure<Double>): NDBuffer<Double> = map(arg) { acosh(it) }
override fun atanh(arg: NDStructure<Double>): NDBuffer<Double> = map(arg) { atanh(it) }
}
fun NDAlgebra.Companion.realWithStream(vararg shape: Int): StreamRealNDField = StreamRealNDField(shape)

22
kmath-core/src/commonMain/kotlin/kscience/kmath/nd/BufferNDAlgebra.kt
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@ -18,40 +18,36 @@ public interface BufferNDAlgebra<T, C> : NDAlgebra<T, C> {
}
)
public val NDStructure<T>.ndBuffer: NDBuffer<T>
public val NDStructure<T>.buffer: Buffer<T>
get() = when {
!shape.contentEquals(this@BufferNDAlgebra.shape) -> throw ShapeMismatchException(
this@BufferNDAlgebra.shape,
shape
)
this is NDBuffer && this.strides == this@BufferNDAlgebra.strides -> this
else -> produce { this@ndBuffer[it] }
this is NDBuffer && this.strides == this@BufferNDAlgebra.strides -> this.buffer
else -> bufferFactory(strides.linearSize) { offset -> get(strides.index(offset)) }
}
override fun map(arg: NDStructure<T>, transform: C.(T) -> T): NDBuffer<T> {
val argAsBuffer = arg.ndBuffer
val buffer = bufferFactory(strides.linearSize) { offset ->
elementContext.transform(argAsBuffer.buffer[offset])
elementContext.transform(arg.buffer[offset])
}
return NDBuffer(strides, buffer)
}
override fun mapIndexed(arg: NDStructure<T>, transform: C.(index: IntArray, T) -> T): NDStructure<T> {
val argAsBuffer = arg.ndBuffer
val buffer = bufferFactory(strides.linearSize) { offset ->
elementContext.transform(
strides.index(offset),
argAsBuffer[offset]
arg.buffer[offset]
)
}
return NDBuffer(strides, buffer)
}
override fun combine(a: NDStructure<T>, b: NDStructure<T>, transform: C.(T, T) -> T): NDStructure<T> {
val aBuffer = a.ndBuffer
val bBuffer = b.ndBuffer
val buffer = bufferFactory(strides.linearSize) { offset ->
elementContext.transform(aBuffer.buffer[offset], bBuffer.buffer[offset])
elementContext.transform(a.buffer[offset], b.buffer[offset])
}
return NDBuffer(strides, buffer)
}
@ -119,10 +115,14 @@ public fun <T, A : Field<T>> NDAlgebra.Companion.field(
vararg shape: Int,
): BufferedNDField<T, A> = BufferedNDField(shape, field, bufferFactory)
@Suppress("UNCHECKED_CAST")
public inline fun <reified T : Any, A : Field<T>> NDAlgebra.Companion.auto(
field: A,
vararg shape: Int,
): BufferedNDField<T, A> = BufferedNDField(shape, field, Buffer.Companion::auto)
): NDField<T, A> = when (field) {
RealField -> RealNDField(shape) as NDField<T, A>
else -> BufferedNDField(shape, field, Buffer.Companion::auto)
}
public inline fun <T, A : Field<T>, R> A.ndField(
noinline bufferFactory: BufferFactory<T>,

2
kmath-core/src/commonMain/kotlin/kscience/kmath/nd/ComplexNDField.kt
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@ -11,7 +11,7 @@ import kotlin.contracts.contract
* An optimized nd-field for complex numbers
*/
@OptIn(UnstableKMathAPI::class)
public open class ComplexNDField(
public class ComplexNDField(
shape: IntArray,
) : BufferedNDField<Complex, ComplexField>(shape, ComplexField, Buffer.Companion::complex),
RingWithNumbers<NDStructure<Complex>>,

56
kmath-core/src/commonMain/kotlin/kscience/kmath/nd/RealNDField.kt
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@ -24,37 +24,46 @@ public class RealNDField(
return produce { d }
}
override val NDStructure<Double>.buffer: RealBuffer
get() = when {
!shape.contentEquals(this@RealNDField.shape) -> throw ShapeMismatchException(
this@RealNDField.shape,
shape
)
this is NDBuffer && this.strides == this@RealNDField.strides -> this.buffer as RealBuffer
else -> RealBuffer(strides.linearSize) { offset -> get(strides.index(offset)) }
}
@Suppress("OVERRIDE_BY_INLINE")
override inline fun map(
arg: NDStructure<Double>,
transform: RealField.(Double) -> Double,
): NDBuffer<Double> {
val argAsBuffer = arg.ndBuffer
val buffer = RealBuffer(strides.linearSize) { offset -> RealField.transform(argAsBuffer.buffer[offset]) }
val buffer = RealBuffer(strides.linearSize) { offset -> RealField.transform(arg.buffer.array[offset]) }
return NDBuffer(strides, buffer)
}
@Suppress("OVERRIDE_BY_INLINE")
override inline fun produce(initializer: RealField.(IntArray) -> Double): NDBuffer<Double> {
val buffer = RealBuffer(strides.linearSize) { offset -> elementContext.initializer(strides.index(offset)) }
return NDBuffer(strides, buffer)
val array = DoubleArray(strides.linearSize) { offset ->
val index = strides.index(offset)
RealField.initializer(index)
}
return NDBuffer(strides, RealBuffer(array))
}
@Suppress("OVERRIDE_BY_INLINE")
override inline fun mapIndexed(
arg: NDStructure<Double>,
transform: RealField.(index: IntArray, Double) -> Double,
): NDBuffer<Double> {
val argAsBuffer = arg.ndBuffer
return NDBuffer(
strides,
RealBuffer(strides.linearSize) { offset ->
elementContext.transform(
strides.index(offset),
argAsBuffer.buffer[offset]
)
})
}
): NDBuffer<Double> = NDBuffer(
strides,
buffer = RealBuffer(strides.linearSize) { offset ->
RealField.transform(
strides.index(offset),
arg.buffer.array[offset]
)
})
@Suppress("OVERRIDE_BY_INLINE")
override inline fun combine(
@ -62,10 +71,8 @@ public class RealNDField(
b: NDStructure<Double>,
transform: RealField.(Double, Double) -> Double,
): NDBuffer<Double> {
val aBuffer = a.ndBuffer
val bBuffer = b.ndBuffer
val buffer = RealBuffer(strides.linearSize) { offset ->
elementContext.transform(aBuffer.buffer[offset], bBuffer.buffer[offset])
RealField.transform(a.buffer.array[offset], b.buffer.array[offset])
}
return NDBuffer(strides, buffer)
}
@ -91,19 +98,6 @@ public class RealNDField(
override fun atanh(arg: NDStructure<Double>): NDBuffer<Double> = map(arg) { atanh(it) }
}
/**
* Fast element production using function inlining
*/
public inline fun BufferedNDField<Double, RealField>.produceInline(crossinline initializer: RealField.(IntArray) -> Double): NDBuffer<Double> {
contract { callsInPlace(initializer, InvocationKind.EXACTLY_ONCE) }
val array = DoubleArray(strides.linearSize) { offset ->
val index = strides.index(offset)
RealField.initializer(index)
}
return NDBuffer(strides, RealBuffer(array))
}
public fun NDAlgebra.Companion.real(vararg shape: Int): RealNDField = RealNDField(shape)
/**

2
kmath-core/src/commonMain/kotlin/kscience/kmath/nd/ShortNDRing.kt
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@ -9,7 +9,7 @@ import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
@OptIn(UnstableKMathAPI::class)
public open class ShortNDRing(
public class ShortNDRing(
shape: IntArray,
) : BufferedNDRing<Short, ShortRing>(shape, ShortRing, Buffer.Companion::auto),
RingWithNumbers<NDStructure<Short>> {

2
kmath-core/src/commonMain/kotlin/kscience/kmath/nd/Structure2D.kt
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@ -58,7 +58,7 @@ public interface Structure2D<T> : NDStructure<T> {
rows: Int,
columns: Int,
crossinline init: (i: Int, j: Int) -> Double,
): Matrix<Double> = NDAlgebra.real(rows, columns).produceInline { (i, j) ->
): Matrix<Double> = NDAlgebra.real(rows, columns).produce { (i, j) ->
init(i, j)
}.as2D()
}

4
kmath-core/src/commonTest/kotlin/kscience/kmath/structures/NumberNDFieldTest.kt
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@ -11,8 +11,8 @@ import kotlin.test.assertEquals
@Suppress("UNUSED_VARIABLE")
class NumberNDFieldTest {
val algebra = NDAlgebra.real(3,3)
val array1 = algebra.produceInline { (i, j) -> (i + j).toDouble() }
val array2 = algebra.produceInline { (i, j) -> (i - j).toDouble() }
val array1 = algebra.produce { (i, j) -> (i + j).toDouble() }
val array2 = algebra.produce { (i, j) -> (i - j).toDouble() }
@Test
fun testSum() {

2
kmath-viktor/src/main/kotlin/kscience/kmath/viktor/ViktorNDStructure.kt
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@ -94,3 +94,5 @@ public class ViktorNDField(public override val shape: IntArray) : NDField<Double
public override inline fun NDStructure<Double>.plus(arg: Double): ViktorNDStructure =
(f64Buffer.plus(arg)).asStructure()
}
public fun ViktorNDField(vararg shape: Int): ViktorNDField = ViktorNDField(shape)