org.openjdk.jmh.samples.JMHSample_17_SyncIterations Maven / Gradle / Ivy

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/*
 * Copyright (c) 2014, Oracle America, Inc.
 * All rights reserved.
 *
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 *    this list of conditions and the following disclaimer.
 *
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 *    specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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 * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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package org.openjdk.jmh.samples;

import org.openjdk.jmh.annotations.Benchmark;
import org.openjdk.jmh.annotations.OutputTimeUnit;
import org.openjdk.jmh.annotations.Scope;
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 org.openjdk.jmh.runner.options.TimeValue;

import java.util.concurrent.TimeUnit;

@State(Scope.Thread)
@OutputTimeUnit(TimeUnit.MILLISECONDS)
public class JMHSample_17_SyncIterations {

    /*
     * This is the another thing that is enabled in JMH by default.
     *
     * Suppose we have this simple benchmark.
     */

    private double src;

    @Benchmark
    public double test() {
        double s = src;
        for (int i = 0; i < 1000; i++) {
            s = Math.sin(s);
        }
        return s;
    }

    /*
     * It turns out if you run the benchmark with multiple threads,
     * the way you start and stop the worker threads seriously affects
     * performance.
     *
     * The natural way would be to park all the threads on some sort
     * of barrier, and the let them go "at once". However, that does
     * not work: there are no guarantees the worker threads will start
     * at the same time, meaning other worker threads are working
     * in better conditions, skewing the result.
     *
     * The better solution would be to introduce bogus iterations,
     * ramp up the threads executing the iterations, and then atomically
     * shift the system to measuring stuff. The same thing can be done
     * during the rampdown. This sounds complicated, but JMH already
     * handles that for you.
     *

     */

    /*
     * ============================== HOW TO RUN THIS TEST: ====================================
     *
     * You will need to oversubscribe the system to make this effect
     * clearly visible; however, this effect can also be shown on the
     * unsaturated systems.*
     *
     * Note the performance of -si false version is more flaky, even
     * though it is "better". This is the false improvement, granted by
     * some of the threads executing in solo. The -si true version more stable
     * and coherent.
     *
     * -si true is enabled by default.
     *
     * Say, $CPU is the number of CPUs on your machine.
     *
     * You can run this test with:
     *
     * a) Via the command line:
     *    $ mvn clean install
     *    $ java -jar target/benchmarks.jar JMHSample_17 \
     *        -w 1s -r 1s -f 1 -t ${CPU*16} -si {true|false}
     *    (we requested shorter warmup/measurement iterations, single fork,
     *     lots of threads, and changeable "synchronize iterations" option)
     *
     * 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_17_SyncIterations.class.getSimpleName())
                .warmupTime(TimeValue.seconds(1))
                .measurementTime(TimeValue.seconds(1))
                .threads(Runtime.getRuntime().availableProcessors()*16)
                .forks(1)
                .syncIterations(true) // try to switch to "false"
                .build();

        new Runner(opt).run();
    }

}