org.openjdk.jmh.samples.JMHSample_11_Loops Maven / Gradle / Ivy

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package org.openjdk.jmh.samples;

import org.openjdk.jmh.annotations.BenchmarkMode;
import org.openjdk.jmh.annotations.GenerateMicroBenchmark;
import org.openjdk.jmh.annotations.Mode;
import org.openjdk.jmh.annotations.OperationsPerInvocation;
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 java.util.concurrent.TimeUnit;

@State(Scope.Thread)
@BenchmarkMode(Mode.AverageTime)
@OutputTimeUnit(TimeUnit.NANOSECONDS)
public class JMHSample_11_Loops {

    /*
     * It would be tempting for users to do loops within the benchmarked method.
     * (This is the bad thing Caliper taught everyone). This tests explains why
     * this is a bad idea.
     *
     * Looping uses the idea of minimize the overhead for calling the test method,
     * if we do the operations inside the loop inside the method call.
     * Don't buy this argument; you will see there is more magic
     * happening when we allow optimizers to merge the loop iterations.
     */

    /*
     * Suppose we want to measure how much it takes to sum two integers:
     */

    int x = 1;
    int y = 2;

    /*
     * This is what you do with JMH.
     */

    @GenerateMicroBenchmark
    public int measureRight() {
        return (x + y);
    }

    /*
     * The following tests emulate the naive looping.
     * This is the Caliper-style benchmark.
     */
    private int reps(int reps) {
        int s = 0;
        for (int i = 0; i < reps; i++) {
            s += (x + y);
        }
        return s;
    }

    /*
     * We would like to measure this with different repetitions count.
     * Special annotation is used to get the individual operation cost.
     */

    @GenerateMicroBenchmark
    @OperationsPerInvocation(1)
    public int measureWrong_1() {
        return reps(1);
    }

    @GenerateMicroBenchmark
    @OperationsPerInvocation(10)
    public int measureWrong_10() {
        return reps(10);
    }

    @GenerateMicroBenchmark
    @OperationsPerInvocation(100)
    public int measureWrong_100() {
        return reps(100);
    }

    @GenerateMicroBenchmark
    @OperationsPerInvocation(1000)
    public int measureWrong_1000() {
        return reps(1000);
    }

    @GenerateMicroBenchmark
    @OperationsPerInvocation(10000)
    public int measureWrong_10000() {
        return reps(10000);
    }

    @GenerateMicroBenchmark
    @OperationsPerInvocation(100000)
    public int measureWrong_100000() {
        return reps(100000);
    }

    /*
     * ============================== HOW TO RUN THIS TEST: ====================================
     *
     * You might notice the larger the repetitions count, the lower the "perceived"
     * cost of the operation being measured. Up to the point we do each addition with 1/20 ns,
     * well beyond what hardware can actually do.
     *
     * This happens because the loop is heavily unrolled/pipelined, and the operation
     * to be measured is hoisted from the loop. Morale: don't overuse loops, rely on JMH
     * to get the measurement right.
     *
     * You can run this test:
     *
     * a) Via the command line:
     *    $ mvn clean install
     *    $ java -jar target/microbenchmarks.jar ".*JMHSample_11.*" -wi 5 -i 5 -f 1
     *    (we requested 5 warmup/measurement iterations, single fork)
     *
     * b) Via the Java API:
     */

    public static void main(String[] args) throws RunnerException {
        Options opt = new OptionsBuilder()
                .include(".*" + JMHSample_11_Loops.class.getSimpleName() + ".*")
                .warmupIterations(5)
                .measurementIterations(5)
                .forks(1)
                .build();

        new Runner(opt).run();
    }

}