org.openjdk.jmh.samples.JMHSample_13_RunToRun Maven / Gradle / Ivy
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*
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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)
*
* 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(5)
.build();
new Runner(opt).run();
}
}
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