org.openjdk.jmh.samples.JMHSample_13_RunToRun Maven / Gradle / Ivy

/*
 * Copyright (c) 2014, Oracle America, Inc.
 * All rights reserved.
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 *
 *  * Redistributions in binary form must reproduce the above copyright
 *    notice, this list of conditions and the following disclaimer in the
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 *    to endorse or promote products derived from this software without
 *    specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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package org.openjdk.jmh.samples;

import org.openjdk.jmh.annotations.Benchmark;
import org.openjdk.jmh.annotations.BenchmarkMode;
import org.openjdk.jmh.annotations.Fork;
import org.openjdk.jmh.annotations.Mode;
import org.openjdk.jmh.annotations.OutputTimeUnit;
import org.openjdk.jmh.annotations.Scope;
import org.openjdk.jmh.annotations.Setup;
import org.openjdk.jmh.annotations.State;
import org.openjdk.jmh.runner.Runner;
import org.openjdk.jmh.runner.RunnerException;
import org.openjdk.jmh.runner.options.Options;
import org.openjdk.jmh.runner.options.OptionsBuilder;

import java.util.concurrent.TimeUnit;

@State(Scope.Thread)
@BenchmarkMode(Mode.AverageTime)
@OutputTimeUnit(TimeUnit.MILLISECONDS)
public class JMHSample_13_RunToRun {

    /*
     * Forking also allows to estimate run-to-run variance.
     *
     * JVMs are complex systems, and the non-determinism is inherent for them.
     * This requires us to always account the run-to-run variance as the one
     * of the effects in our experiments.
     *
     * Luckily, forking aggregates the results across several JVM launches.
     */

    /*
     * In order to introduce readily measurable run-to-run variance, we build
     * the workload which performance differs from run to run. Note that many workloads
     * will have the similar behavior, but we do that artificially to make a point.
     */

    @State(Scope.Thread)
    public static class SleepyState {
        public long sleepTime;

        @Setup
        public void setup() {
            sleepTime = (long) (Math.random() * 1000);
        }
    }

    /*
     * Now, we will run this different number of times.
     */

    @Benchmark
    @Fork(1)
    public void baseline(SleepyState s) throws InterruptedException {
        TimeUnit.MILLISECONDS.sleep(s.sleepTime);
    }

    @Benchmark
    @Fork(5)
    public void fork_1(SleepyState s) throws InterruptedException {
        TimeUnit.MILLISECONDS.sleep(s.sleepTime);
    }

    @Benchmark
    @Fork(20)
    public void fork_2(SleepyState s) throws InterruptedException {
        TimeUnit.MILLISECONDS.sleep(s.sleepTime);
    }

    /*
     * ============================== HOW TO RUN THIS TEST: ====================================
     *
     * Note the baseline is random within [0..1000] msec; and both forked runs
     * are estimating the average 500 msec with some confidence.
     *
     * You can run this test:
     *
     * a) Via the command line:
     *    $ mvn clean install
     *    $ java -jar target/benchmarks.jar JMHSample_13 -wi 0 -i 3
     *    (we requested no warmup, 3 measurement iterations; there are also other options, see -h)
     *
     * b) Via the Java API:
     *    (see the JMH homepage for possible caveats when running from IDE:
     *      http://openjdk.java.net/projects/code-tools/jmh/)
     */

    public static void main(String[] args) throws RunnerException {
        Options opt = new OptionsBuilder()
                .include(JMHSample_13_RunToRun.class.getSimpleName())
                .warmupIterations(0)
                .measurementIterations(3)
                .build();

        new Runner(opt).run();
    }

}