commons-rng-examples/examples-jmh/src/main/java/org/apache/commons/rng/examples/jmh/distribution/DiscreteSamplersPerformance.java

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 * 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.
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package org.apache.commons.rng.examples.jmh.distribution;

import org.apache.commons.rng.UniformRandomProvider;
import org.apache.commons.rng.examples.jmh.RandomSources;
import org.apache.commons.rng.sampling.distribution.AliasMethodDiscreteSampler;
import org.apache.commons.rng.sampling.distribution.DiscreteSampler;
import org.apache.commons.rng.sampling.distribution.DiscreteUniformSampler;
import org.apache.commons.rng.sampling.distribution.GeometricSampler;
import org.apache.commons.rng.sampling.distribution.GuideTableDiscreteSampler;
import org.apache.commons.rng.sampling.distribution.LargeMeanPoissonSampler;
import org.apache.commons.rng.sampling.distribution.MarsagliaTsangWangDiscreteSampler;
import org.apache.commons.rng.sampling.distribution.RejectionInversionZipfSampler;
import org.apache.commons.rng.sampling.distribution.SmallMeanPoissonSampler;

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 benchmark to compare the speed of generation of random numbers
 * from the various source providers for different types of {@link DiscreteSampler}.
 */
@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 DiscreteSamplersPerformance {
    /**
     * The value.
     *
     * <p>This must NOT be final!</p>
     */
    private int value;

    /**
     * The {@link DiscreteSampler} samplers to use for testing. Creates the sampler for each
     * {@link org.apache.commons.rng.simple.RandomSource RandomSource} in the default
     * {@link RandomSources}.
     */
    @State(Scope.Benchmark)
    public static class Sources extends RandomSources {
        /** The probabilities for the discrete distribution. */
        private static final double[] DISCRETE_PROBABILITIES;

        static {
            // The size of this distribution will effect the relative performance
            // of the AliasMethodDiscreteSampler against the other samplers. The
            // Alias sampler is optimised for power of 2 tables and will zero pad
            // the distribution. Pick a midpoint value between a power of 2 size to
            // baseline half of a possible speed advantage.
            final int size = (32 + 64) / 2;
            DISCRETE_PROBABILITIES = new double[size];
            for (int i = 0; i < size; i++) {
                DISCRETE_PROBABILITIES[i] = (i + 1.0) / size;
            }
        }

        /**
         * The sampler type.
         */
        @Param({"DiscreteUniformSampler",
                "RejectionInversionZipfSampler",
                "SmallMeanPoissonSampler",
                "LargeMeanPoissonSampler",
                "GeometricSampler",
                "MarsagliaTsangWangDiscreteSampler",
                "MarsagliaTsangWangPoissonSampler",
                "MarsagliaTsangWangBinomialSampler",
                "GuideTableDiscreteSampler",
                "AliasMethodDiscreteSampler",
                })
        private String samplerType;

        /** The sampler. */
        private DiscreteSampler sampler;

        /**
         * @return the sampler.
         */
        public DiscreteSampler getSampler() {
            return sampler;
        }

        /** Instantiates sampler. */
        @Override
        @Setup
        public void setup() {
            super.setup();
            final UniformRandomProvider rng = getGenerator();
            if ("DiscreteUniformSampler".equals(samplerType)) {
                sampler = DiscreteUniformSampler.of(rng, -98, 76);
            } else if ("RejectionInversionZipfSampler".equals(samplerType)) {
                sampler = RejectionInversionZipfSampler.of(rng, 43, 2.1);
            } else if ("SmallMeanPoissonSampler".equals(samplerType)) {
                sampler = SmallMeanPoissonSampler.of(rng, 8.9);
            } else if ("LargeMeanPoissonSampler".equals(samplerType)) {
                // Note: Use with a fractional part to the mean includes a small mean sample
                sampler = LargeMeanPoissonSampler.of(rng, 41.7);
            } else if ("GeometricSampler".equals(samplerType)) {
                sampler = GeometricSampler.of(rng, 0.21);
            } else if ("MarsagliaTsangWangDiscreteSampler".equals(samplerType)) {
                sampler = MarsagliaTsangWangDiscreteSampler.Enumerated.of(rng, DISCRETE_PROBABILITIES);
            } else if ("MarsagliaTsangWangPoissonSampler".equals(samplerType)) {
                sampler = MarsagliaTsangWangDiscreteSampler.Poisson.of(rng, 8.9);
            } else if ("MarsagliaTsangWangBinomialSampler".equals(samplerType)) {
                sampler = MarsagliaTsangWangDiscreteSampler.Binomial.of(rng, 20, 0.33);
            } else if ("GuideTableDiscreteSampler".equals(samplerType)) {
                sampler = GuideTableDiscreteSampler.of(rng, DISCRETE_PROBABILITIES);
            } else if ("AliasMethodDiscreteSampler".equals(samplerType)) {
                sampler = AliasMethodDiscreteSampler.of(rng, DISCRETE_PROBABILITIES);
            }
        }
    }

    // Benchmarks methods below.

    /**
     * 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();
    }
}