| /* |
| * Licensed to the Apache Software Foundation (ASF) under one or more |
| * contributor license agreements. See the NOTICE file distributed with |
| * this work for additional information regarding copyright ownership. |
| * The ASF licenses this file to You under the Apache License, Version 2.0 |
| * (the "License"); you may not use this file except in compliance with |
| * the License. You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an "AS IS" BASIS, |
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| */ |
| |
| package org.apache.commons.rng.examples.jmh.distribution; |
| |
| import org.apache.commons.rng.UniformRandomProvider; |
| import org.apache.commons.rng.sampling.distribution.DiscreteInverseCumulativeProbabilityFunction; |
| import org.apache.commons.rng.sampling.distribution.DiscreteSampler; |
| import org.apache.commons.rng.sampling.distribution.GeometricSampler; |
| import org.apache.commons.rng.sampling.distribution.InverseTransformDiscreteSampler; |
| import org.apache.commons.rng.simple.RandomSource; |
| import org.openjdk.jmh.annotations.Benchmark; |
| import org.openjdk.jmh.annotations.BenchmarkMode; |
| import org.openjdk.jmh.annotations.Fork; |
| import org.openjdk.jmh.annotations.Measurement; |
| import org.openjdk.jmh.annotations.Mode; |
| import org.openjdk.jmh.annotations.OutputTimeUnit; |
| import org.openjdk.jmh.annotations.Param; |
| import org.openjdk.jmh.annotations.Scope; |
| import org.openjdk.jmh.annotations.Setup; |
| import org.openjdk.jmh.annotations.State; |
| import org.openjdk.jmh.annotations.Warmup; |
| |
| import java.util.concurrent.TimeUnit; |
| |
| /** |
| * Executes a benchmark to compare the speed of generation of Geometric random numbers |
| * using different methods. |
| */ |
| @BenchmarkMode(Mode.AverageTime) |
| @OutputTimeUnit(TimeUnit.NANOSECONDS) |
| @Warmup(iterations = 5, time = 1, timeUnit = TimeUnit.SECONDS) |
| @Measurement(iterations = 5, time = 1, timeUnit = TimeUnit.SECONDS) |
| @State(Scope.Benchmark) |
| @Fork(value = 1, jvmArgs = {"-server", "-Xms128M", "-Xmx128M"}) |
| public class GeometricSamplersPerformance { |
| /** |
| * The value. |
| * |
| * <p>This must NOT be final!</p> |
| */ |
| private int value; |
| |
| /** |
| * The samplers's to use for testing. Defines the RandomSource, probability of success |
| * and the type of Geometric sampler. |
| */ |
| @State(Scope.Benchmark) |
| public static class Sources { |
| /** |
| * RNG providers. |
| * |
| * <p>Use different speeds.</p> |
| * |
| * @see <a href="https://commons.apache.org/proper/commons-rng/userguide/rng.html"> |
| * Commons RNG user guide</a> |
| */ |
| @Param({"SPLIT_MIX_64", |
| "MWC_256", |
| "JDK"}) |
| private String randomSourceName; |
| |
| /** |
| * The probability of success. |
| */ |
| @Param({"0.1", "0.3"}) |
| private double probabilityOfSuccess; |
| |
| /** |
| * The sampler type. |
| */ |
| @Param({"GeometricSampler", "InverseTransformDiscreteSampler"}) |
| private String samplerType; |
| |
| /** The sampler. */ |
| private DiscreteSampler sampler; |
| |
| /** |
| * @return the sampler. |
| */ |
| public DiscreteSampler getSampler() { |
| return sampler; |
| } |
| |
| /** Instantiates sampler. */ |
| @Setup |
| public void setup() { |
| final RandomSource randomSource = RandomSource.valueOf(randomSourceName); |
| final UniformRandomProvider rng = RandomSource.create(randomSource); |
| if ("GeometricSampler".equals(samplerType)) { |
| sampler = GeometricSampler.of(rng, probabilityOfSuccess); |
| } else { |
| final DiscreteInverseCumulativeProbabilityFunction geometricFunction = |
| new GeometricDiscreteInverseCumulativeProbabilityFunction(probabilityOfSuccess); |
| sampler = InverseTransformDiscreteSampler.of(rng, geometricFunction); |
| } |
| } |
| } |
| |
| /** |
| * Define the inverse cumulative probability function for the Geometric distribution. |
| * |
| * <p>Adapted from org.apache.commons.math3.distribution.GeometricDistribution. |
| */ |
| private static class GeometricDiscreteInverseCumulativeProbabilityFunction |
| implements DiscreteInverseCumulativeProbabilityFunction { |
| /** |
| * {@code log(1 - p)} where p is the probability of success. |
| */ |
| private final double log1mProbabilityOfSuccess; |
| |
| /** |
| * @param probabilityOfSuccess the probability of success |
| */ |
| GeometricDiscreteInverseCumulativeProbabilityFunction(double probabilityOfSuccess) { |
| // No validation that 0 < p <= 1 |
| log1mProbabilityOfSuccess = Math.log1p(-probabilityOfSuccess); |
| } |
| |
| @Override |
| public int inverseCumulativeProbability(double cumulativeProbability) { |
| // This is the equivalent of floor(log(u)/ln(1-p)) |
| // where: |
| // u = cumulative probability |
| // p = probability of success |
| // See: https://en.wikipedia.org/wiki/Geometric_distribution#Related_distributions |
| // --- |
| // Note: if cumulativeProbability == 0 then log1p(-0) is zero and the result |
| // after the range check is 0. |
| // Note: if cumulativeProbability == 1 then log1p(-1) is negative infinity, the result |
| // of the divide is positive infinity and the result after the range check is |
| // Integer.MAX_VALUE. |
| return Math.max(0, (int) Math.ceil(Math.log1p(-cumulativeProbability) / log1mProbabilityOfSuccess - 1)); |
| } |
| } |
| |
| /** |
| * Baseline for the JMH timing overhead for production of an {@code int} value. |
| * |
| * @return the {@code int} value |
| */ |
| @Benchmark |
| public int baseline() { |
| return value; |
| } |
| |
| /** |
| * Run the sampler. |
| * |
| * @param sources Source of randomness. |
| * @return the sample value |
| */ |
| @Benchmark |
| public int sample(Sources sources) { |
| return sources.getSampler().sample(); |
| } |
| } |