Optimized blocking chains for primitive numbers generation.

examples/src/main/kotlin/scientifik/kmath/commons/prob/DistributionBenchmark.kt

@@ -0,0 +1,71 @@
+package scientifik.kmath.commons.prob
+
+import kotlinx.coroutines.Dispatchers
+import kotlinx.coroutines.async
+import kotlinx.coroutines.runBlocking
+import org.apache.commons.rng.sampling.distribution.ZigguratNormalizedGaussianSampler
+import org.apache.commons.rng.simple.RandomSource
+import scientifik.kmath.chains.BlockingRealChain
+import scientifik.kmath.prob.*
+import java.time.Duration
+import java.time.Instant
+
+
+private suspend fun runChain(): Duration {
+    val generator = RandomGenerator.fromSource(RandomSource.MT, 123L)
+
+    val normal = Distribution.normal(NormalSamplerMethod.Ziggurat)
+    val chain = normal.sample(generator) as BlockingRealChain
+
+    val startTime = Instant.now()
+    var sum = 0.0
+    repeat(10000001) { counter ->
+
+        sum += chain.nextDouble()
+
+        if (counter % 100000 == 0) {
+            val duration = Duration.between(startTime, Instant.now())
+            val meanValue = sum / counter
+            println("Chain sampler completed $counter elements in $duration: $meanValue")
+        }
+    }
+    return Duration.between(startTime, Instant.now())
+}
+
+private fun runDirect(): Duration {
+    val provider = RandomSource.create(RandomSource.MT, 123L)
+    val sampler = ZigguratNormalizedGaussianSampler(provider)
+    val startTime = Instant.now()
+
+    var sum = 0.0
+    repeat(10000001) { counter ->
+
+        sum += sampler.sample()
+
+        if (counter % 100000 == 0) {
+            val duration = Duration.between(startTime, Instant.now())
+            val meanValue = sum / counter
+            println("Direct sampler completed $counter elements in $duration: $meanValue")
+        }
+    }
+    return Duration.between(startTime, Instant.now())
+}
+
+/**
+ * Comparing chain sampling performance with direct sampling performance
+ */
+fun main() {
+    runBlocking(Dispatchers.Default) {
+        val chainJob = async {
+            runChain()
+        }
+
+        val directJob = async {
+            runDirect()
+        }
+
+        println("Chain: ${chainJob.await()}")
+        println("Direct: ${directJob.await()}")
+    }
+
+}

kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/chains/BlockingIntChain.kt

@@ -0,0 +1,12 @@
+package scientifik.kmath.chains
+
+/**
+ * Performance optimized chain for integer values
+ */
+abstract class BlockingIntChain : Chain<Int> {
+    abstract fun nextInt(): Int
+
+    override suspend fun next(): Int = nextInt()
+
+    fun nextBlock(size: Int): IntArray = IntArray(size) { nextInt() }
+}

kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/chains/BlockingRealChain.kt

@@ -0,0 +1,12 @@
+package scientifik.kmath.chains
+
+/**
+ * Performance optimized chain for real values
+ */
+abstract class BlockingRealChain : Chain<Double> {
+    abstract fun nextDouble(): Double
+
+    override suspend fun next(): Double = nextDouble()
+
+    fun nextBlock(size: Int): DoubleArray = DoubleArray(size) { nextDouble() }
+}

kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/chains/Chain.kt

@@ -48,7 +48,6 @@ interface Chain<out R>: Flow<R> {
     }
 
     companion object
-
 }
 
 

kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/streaming/BufferFlow.kt

@@ -2,9 +2,11 @@ package scientifik.kmath.streaming
 
 import kotlinx.coroutines.FlowPreview
 import kotlinx.coroutines.flow.*
+import scientifik.kmath.chains.BlockingRealChain
 import scientifik.kmath.structures.Buffer
 import scientifik.kmath.structures.BufferFactory
 import scientifik.kmath.structures.DoubleBuffer
+import scientifik.kmath.structures.asBuffer
 
 /**
  * Create a [Flow] from buffer
@@ -45,6 +47,13 @@ fun <T> Flow<T>.chunked(bufferSize: Int, bufferFactory: BufferFactory<T>): Flow<
  */
 fun Flow<Double>.chunked(bufferSize: Int): Flow<DoubleBuffer> = flow {
     require(bufferSize > 0) { "Resulting chunk size must be more than zero" }
+
+    if (this@chunked is BlockingRealChain) {
+        //performance optimization for blocking primitive chain
+        while (true) {
+            emit(nextBlock(bufferSize).asBuffer())
+        }
+    } else {
         val array = DoubleArray(bufferSize)
         var counter = 0
 
@@ -60,6 +69,7 @@ fun Flow<Double>.chunked(bufferSize: Int): Flow<DoubleBuffer> = flow {
         if (counter > 0) {
             emit(DoubleBuffer(counter) { array[it] })
         }
+    }
 }
 
 /**

kmath-prob/src/jvmMain/kotlin/scientifik/kmath/prob/RandomSourceGenerator.kt

@@ -5,7 +5,7 @@ import org.apache.commons.rng.simple.RandomSource
 
 class RandomSourceGenerator(val source: RandomSource, seed: Long?) : RandomGenerator {
     internal val random: UniformRandomProvider = seed?.let {
-        RandomSource.create(source, seed, null)
+        RandomSource.create(source, seed)
     } ?: RandomSource.create(source)
 
     override fun nextBoolean(): Boolean = random.nextBoolean()
@@ -59,3 +59,9 @@ fun RandomGenerator.asUniformRandomProvider(): UniformRandomProvider = if (this
 } else {
     RandomGeneratorProvider(this)
 }
+
+fun RandomGenerator.Companion.fromSource(source: RandomSource, seed: Long? = null): RandomSourceGenerator =
+    RandomSourceGenerator(source, seed)
+
+fun RandomGenerator.Companion.mersenneTwister(seed: Long? = null): RandomSourceGenerator =
+    fromSource(RandomSource.MT, seed)

kmath-prob/src/jvmMain/kotlin/scientifik/kmath/prob/distributions.kt

@@ -2,6 +2,8 @@ package scientifik.kmath.prob
 
 import org.apache.commons.rng.UniformRandomProvider
 import org.apache.commons.rng.sampling.distribution.*
+import scientifik.kmath.chains.BlockingIntChain
+import scientifik.kmath.chains.BlockingRealChain
 import scientifik.kmath.chains.Chain
 import java.util.*
 import kotlin.math.PI
@@ -11,32 +13,32 @@ import kotlin.math.sqrt
 
 abstract class ContinuousSamplerDistribution : Distribution<Double> {
 
-    private inner class ContinuousSamplerChain(val generator: RandomGenerator) : Chain<Double> {
+    private inner class ContinuousSamplerChain(val generator: RandomGenerator) : BlockingRealChain() {
-        private val sampler = buildSampler(generator)
+        private val sampler = buildCMSampler(generator)
 
-        override suspend fun next(): Double = sampler.sample()
+        override fun nextDouble(): Double = sampler.sample()
 
         override fun fork(): Chain<Double> = ContinuousSamplerChain(generator.fork())
     }
 
-    protected abstract fun buildSampler(generator: RandomGenerator): ContinuousSampler
+    protected abstract fun buildCMSampler(generator: RandomGenerator): ContinuousSampler
 
-    override fun sample(generator: RandomGenerator): Chain<Double> = ContinuousSamplerChain(generator)
+    override fun sample(generator: RandomGenerator): BlockingRealChain = ContinuousSamplerChain(generator)
 }
 
 abstract class DiscreteSamplerDistribution : Distribution<Int> {
 
-    private inner class ContinuousSamplerChain(val generator: RandomGenerator) : Chain<Int> {
+    private inner class ContinuousSamplerChain(val generator: RandomGenerator) : BlockingIntChain() {
         private val sampler = buildSampler(generator)
 
-        override suspend fun next(): Int = sampler.sample()
+        override fun nextInt(): Int = sampler.sample()
 
         override fun fork(): Chain<Int> = ContinuousSamplerChain(generator.fork())
     }
 
     protected abstract fun buildSampler(generator: RandomGenerator): DiscreteSampler
 
-    override fun sample(generator: RandomGenerator): Chain<Int> = ContinuousSamplerChain(generator)
+    override fun sample(generator: RandomGenerator): BlockingIntChain = ContinuousSamplerChain(generator)
 }
 
 enum class NormalSamplerMethod {
@@ -55,7 +57,7 @@ private fun normalSampler(method: NormalSamplerMethod, provider: UniformRandomPr
 fun Distribution.Companion.normal(
     method: NormalSamplerMethod = NormalSamplerMethod.Ziggurat
 ): Distribution<Double> = object : ContinuousSamplerDistribution() {
-    override fun buildSampler(generator: RandomGenerator): ContinuousSampler {
+    override fun buildCMSampler(generator: RandomGenerator): ContinuousSampler {
         val provider: UniformRandomProvider = generator.asUniformRandomProvider()
         return normalSampler(method, provider)
     }
@@ -69,11 +71,11 @@ fun Distribution.Companion.normal(
     mean: Double,
     sigma: Double,
     method: NormalSamplerMethod = NormalSamplerMethod.Ziggurat
-): Distribution<Double> = object : ContinuousSamplerDistribution() {
+): ContinuousSamplerDistribution = object : ContinuousSamplerDistribution() {
     private val sigma2 = sigma.pow(2)
     private val norm = sigma * sqrt(PI * 2)
 
-    override fun buildSampler(generator: RandomGenerator): ContinuousSampler {
+    override fun buildCMSampler(generator: RandomGenerator): ContinuousSampler {
         val provider: UniformRandomProvider = generator.asUniformRandomProvider()
         val normalizedSampler = normalSampler(method, provider)
         return GaussianSampler(normalizedSampler, mean, sigma)
@@ -86,7 +88,7 @@ fun Distribution.Companion.normal(
 
 fun Distribution.Companion.poisson(
     lambda: Double
-): Distribution<Int> = object : DiscreteSamplerDistribution() {
+): DiscreteSamplerDistribution = object : DiscreteSamplerDistribution() {
 
     override fun buildSampler(generator: RandomGenerator): DiscreteSampler {
         return PoissonSampler.of(generator.asUniformRandomProvider(), lambda)

kmath-prob/src/jvmTest/kotlin/scientifik/kmath/prob/CommonsDistributionsTest.kt

@@ -8,12 +8,21 @@ import org.junit.jupiter.api.Test
 
 class CommonsDistributionsTest {
     @Test
-    fun testNormalDistribution(){
+    fun testNormalDistributionSuspend() {
-        val distribution = Distribution.normal(7.0,2.0)
+        val distribution = Distribution.normal(7.0, 2.0)
         val generator = RandomGenerator.default(1)
         val sample = runBlocking {
             distribution.sample(generator).take(1000).toList()
         }
         Assertions.assertEquals(7.0, sample.average(), 0.1)
     }
+
+    @Test
+    fun testNormalDistributionBlocking() {
+        val distribution = Distribution.normal(7.0, 2.0)
+        val generator = RandomGenerator.default(1)
+        val sample = distribution.sample(generator).nextBlock(1000)
+        Assertions.assertEquals(7.0, sample.average(), 0.1)
+    }
+
 }