Moved optimizations to branch refactor/polynomials
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Gleb Minaev
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9fc99a4c72
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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/LICENSE.txt file.
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*/
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package space.kscience.kmath.functions
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import space.kscience.kmath.operations.Ring
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import space.kscience.kmath.operations.invoke
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import kotlin.math.max
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import kotlin.math.min
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// TODO: Optimized copies of substitution and invocation
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@UnstablePolynomialBoxingOptimization
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@Suppress("NOTHING_TO_INLINE")
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internal inline fun <C> copyTo(
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origin: List<C>,
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originDegree: Int,
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target: MutableList<C>,
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) {
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for (deg in 0 .. originDegree) target[deg] = origin[deg]
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}
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@UnstablePolynomialBoxingOptimization
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@Suppress("NOTHING_TO_INLINE")
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internal inline fun <C> multiplyAddingToUpdater(
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ring: Ring<C>,
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multiplicand: MutableList<C>,
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multiplicandDegree: Int,
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multiplier: List<C>,
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multiplierDegree: Int,
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updater: MutableList<C>,
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zero: C,
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) {
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multiplyAddingTo(
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ring = ring,
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multiplicand = multiplicand,
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multiplicandDegree = multiplicandDegree,
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multiplier = multiplier,
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multiplierDegree = multiplierDegree,
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target = updater
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)
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for (updateDeg in 0 .. multiplicandDegree + multiplierDegree) {
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multiplicand[updateDeg] = updater[updateDeg]
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updater[updateDeg] = zero
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}
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}
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@UnstablePolynomialBoxingOptimization
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@Suppress("NOTHING_TO_INLINE")
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internal inline fun <C> multiplyAddingTo(
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ring: Ring<C>,
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multiplicand: List<C>,
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multiplicandDegree: Int,
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multiplier: List<C>,
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multiplierDegree: Int,
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target: MutableList<C>
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) = ring {
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for (d in 0 .. multiplicandDegree + multiplierDegree)
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for (k in max(0, d - multiplierDegree)..min(multiplicandDegree, d))
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target[d] += multiplicand[k] * multiplier[d - k]
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}
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@UnstablePolynomialBoxingOptimization
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public fun <C> ListPolynomial<C>.substitute2(ring: Ring<C>, arg: ListPolynomial<C>) : ListPolynomial<C> = ring {
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if (coefficients.isEmpty()) return ListPolynomial(emptyList())
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val thisDegree = coefficients.lastIndex
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if (thisDegree == -1) return ListPolynomial(emptyList())
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val argDegree = arg.coefficients.lastIndex
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if (argDegree == -1) return coefficients[0].asListPolynomial()
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val constantZero = zero
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val resultCoefs: MutableList<C> = MutableList(thisDegree * argDegree + 1) { constantZero }
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resultCoefs[0] = coefficients[thisDegree]
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val resultCoefsUpdate: MutableList<C> = MutableList(thisDegree * argDegree + 1) { constantZero }
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var resultDegree = 0
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for (deg in thisDegree - 1 downTo 0) {
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resultCoefsUpdate[0] = coefficients[deg]
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multiplyAddingToUpdater(
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ring = ring,
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multiplicand = resultCoefs,
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multiplicandDegree = resultDegree,
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multiplier = arg.coefficients,
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multiplierDegree = argDegree,
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updater = resultCoefsUpdate,
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zero = constantZero
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)
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resultDegree += argDegree
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}
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return ListPolynomial<C>(resultCoefs)
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}
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/**
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* Returns numerator (polynomial) of rational function gotten by substitution rational function [arg] to the polynomial instance.
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* More concrete, if [arg] is a fraction `f(x)/g(x)` and the receiving instance is `p(x)`, then
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* ```
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* p(f/g) * g^deg(p)
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* ```
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* is returned.
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*
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* Used in [ListPolynomial.substitute] and [ListRationalFunction.substitute] for performance optimisation.
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*/ // TODO: Дописать
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@UnstablePolynomialBoxingOptimization
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internal fun <C> ListPolynomial<C>.substituteRationalFunctionTakeNumerator(ring: Ring<C>, arg: ListRationalFunction<C>): ListPolynomial<C> = ring {
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if (coefficients.isEmpty()) return ListPolynomial(emptyList())
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val thisDegree = coefficients.lastIndex
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if (thisDegree == -1) return ListPolynomial(emptyList())
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val thisDegreeLog2 = 31 - thisDegree.countLeadingZeroBits()
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val numeratorDegree = arg.numerator.coefficients.lastIndex
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val denominatorDegree = arg.denominator.coefficients.lastIndex
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val argDegree = max(numeratorDegree, denominatorDegree)
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val constantZero = zero
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val powersOf2 = buildList<Int>(thisDegreeLog2 + 1) {
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var result = 1
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for (exp in 0 .. thisDegreeLog2) {
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add(result)
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result = result shl 1
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}
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}
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val hashes = powersOf2.runningReduce { acc, i -> acc + i }
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val numeratorPowers = buildList<List<C>>(thisDegreeLog2 + 1) {
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add(arg.numerator.coefficients)
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repeat(thisDegreeLog2) {
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val next = MutableList<C>(powersOf2[it + 1] * numeratorDegree + 1) { constantZero }
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add(next)
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val last = last()
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multiplyAddingTo(
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ring = ring,
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multiplicand = last,
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multiplicandDegree = powersOf2[it] * numeratorDegree + 1,
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multiplier = last,
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multiplierDegree = powersOf2[it] * numeratorDegree + 1,
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target = next,
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)
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}
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}
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val denominatorPowers = buildList<List<C>>(thisDegreeLog2 + 1) {
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add(arg.denominator.coefficients)
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repeat(thisDegreeLog2) {
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val next = MutableList<C>(powersOf2[it + 1] * denominatorDegree + 1) { constantZero }
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add(next)
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val last = last()
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multiplyAddingTo(
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ring = ring,
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multiplicand = last,
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multiplicandDegree = powersOf2[it] * denominatorDegree + 1,
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multiplier = last,
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multiplierDegree = powersOf2[it] * denominatorDegree + 1,
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target = next,
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)
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}
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}
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val levelResultCoefsPool = buildList<MutableList<C>>(thisDegreeLog2 + 1) {
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repeat(thisDegreeLog2 + 1) {
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add(MutableList(hashes[it] * argDegree) { constantZero })
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}
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}
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val edgedMultiplier = MutableList<C>(0) { TODO() }
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val edgedMultiplierUpdater = MutableList<C>(0) { TODO() }
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fun MutableList<C>.reset() {
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for (i in indices) set(i, constantZero)
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}
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fun processLevel(level: Int, start: Int, end: Int) : List<C> {
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val levelResultCoefs = levelResultCoefsPool[level + 1]
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if (level == -1) {
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levelResultCoefs[0] = coefficients[start]
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} else {
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levelResultCoefs.reset()
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multiplyAddingTo(
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ring = ring,
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multiplicand = processLevel(level = level - 1, start = start, end = (start + end) / 2),
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multiplicandDegree = hashes[level] * argDegree,
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multiplier = denominatorPowers[level],
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multiplierDegree = powersOf2[level] * denominatorDegree,
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target = levelResultCoefs
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)
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multiplyAddingTo(
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ring = ring,
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multiplicand = processLevel(level = level - 1, start = (start + end) / 2, end = end),
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multiplicandDegree = hashes[level] * argDegree,
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multiplier = numeratorPowers[level],
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multiplierDegree = powersOf2[level] * numeratorDegree,
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target = levelResultCoefs
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)
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}
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return levelResultCoefs
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}
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fun processLevelEdged(level: Int, start: Int, end: Int) : List<C> {
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val levelResultCoefs = levelResultCoefsPool[level + 1]
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if (level == -1) {
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levelResultCoefs[0] = coefficients[start]
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} else {
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val levelsPowerOf2 = powersOf2[level]
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if (end - start >= levelsPowerOf2) {
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multiplyAddingTo(
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ring = ring,
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multiplicand = processLevelEdged(level = level - 1, start = start + levelsPowerOf2, end = end),
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multiplicandDegree = hashes[level] * argDegree, // TODO: Ввести переменную
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multiplier = numeratorPowers[level],
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multiplierDegree = powersOf2[level] * numeratorDegree,
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target = levelResultCoefs
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)
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multiplyAddingTo(
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ring = ring,
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multiplicand = processLevel(level = level - 1, start = start, end = start + levelsPowerOf2),
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multiplicandDegree = hashes[level] * argDegree,
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multiplier = edgedMultiplier,
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multiplierDegree = max((hashes[level] and thisDegree) - powersOf2[level] + 1, 0) * denominatorDegree, // TODO: Ввести переменную
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target = levelResultCoefs
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)
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if (level != thisDegreeLog2) {
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multiplyAddingToUpdater(
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ring = ring,
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multiplicand = edgedMultiplier,
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multiplicandDegree = max((hashes[level] and thisDegree) - powersOf2[level] + 1, 0) * denominatorDegree, // TODO: Ввести переменную
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multiplier = denominatorPowers[level],
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multiplierDegree = powersOf2[level] * denominatorDegree,
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updater = edgedMultiplierUpdater,
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zero = constantZero
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)
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}
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} else {
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copyTo(
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origin = processLevelEdged(level = level - 1, start = start + levelsPowerOf2, end = end),
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originDegree = hashes[level] * argDegree, // TODO: Ввести переменную
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target = levelResultCoefs
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)
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}
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}
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return levelResultCoefs
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}
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return ListPolynomial(
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processLevelEdged(
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level = thisDegreeLog2,
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start = 0,
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end = thisDegree + 1
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)
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)
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}
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@ -6,16 +6,6 @@
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package space.kscience.kmath.functions
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package space.kscience.kmath.functions
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/**
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* Marks operations that are going to be optimized reimplementations by reducing number of boxings but currently is
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* under development and is not stable (or even ready to use).
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*/
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@RequiresOptIn(
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message = "It's copy of operation with optimized boxing. It's currently unstable.",
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level = RequiresOptIn.Level.ERROR
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)
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internal annotation class UnstablePolynomialBoxingOptimization
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/**
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/**
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* Marks declarations that give access to internal entities of polynomials delicate structure. Thus, it allows to
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* Marks declarations that give access to internal entities of polynomials delicate structure. Thus, it allows to
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* optimize performance a bit by skipping standard steps, but such skips may cause critical errors if something is
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* optimize performance a bit by skipping standard steps, but such skips may cause critical errors if something is
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Reference in New Issue
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