org.openjdk.jmh.samples.JMHSample_36_BranchPrediction Maven / Gradle / Ivy
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* Copyright (c) 2015, Oracle America, Inc.
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package org.openjdk.jmh.samples;
import org.openjdk.jmh.annotations.*;
import org.openjdk.jmh.infra.Blackhole;
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.Arrays;
import java.util.Random;
import java.util.concurrent.TimeUnit;
@BenchmarkMode(Mode.AverageTime)
@OutputTimeUnit(TimeUnit.NANOSECONDS)
@Warmup(iterations = 5, time = 1, timeUnit = TimeUnit.SECONDS)
@Measurement(iterations = 5, time = 1, timeUnit = TimeUnit.SECONDS)
@Fork(5)
@State(Scope.Benchmark)
public class JMHSample_36_BranchPrediction {
/*
* This sample serves as a warning against regular data sets.
*
* It is very tempting to present a regular data set to benchmark, either due to
* naive generation strategy, or just from feeling better about regular data sets.
* Unfortunately, it frequently backfires: the regular datasets are known to be
* optimized well by software and hardware. This example exploits one of these
* optimizations: branch prediction.
*
* Imagine our benchmark selects the branch based on the array contents, as
* we are streaming through it:
*/
private static final int COUNT = 1024 * 1024;
private byte[] sorted;
private byte[] unsorted;
@Setup
public void setup() {
sorted = new byte[COUNT];
unsorted = new byte[COUNT];
Random random = new Random(1234);
random.nextBytes(sorted);
random.nextBytes(unsorted);
Arrays.sort(sorted);
}
@Benchmark
@OperationsPerInvocation(COUNT)
public void sorted(Blackhole bh1, Blackhole bh2) {
for (byte v : sorted) {
if (v > 0) {
bh1.consume(v);
} else {
bh2.consume(v);
}
}
}
@Benchmark
@OperationsPerInvocation(COUNT)
public void unsorted(Blackhole bh1, Blackhole bh2) {
for (byte v : unsorted) {
if (v > 0) {
bh1.consume(v);
} else {
bh2.consume(v);
}
}
}
/*
There is a substantial difference in performance for these benchmarks!
It is explained by good branch prediction in "sorted" case, and branch mispredicts in "unsorted"
case. -prof perfnorm conveniently highlights that, with larger "branch-misses", and larger "CPI"
for "unsorted" case:
Benchmark Mode Cnt Score Error Units
JMHSample_36_BranchPrediction.sorted avgt 25 2.160 ± 0.049 ns/op
JMHSample_36_BranchPrediction.sorted:·CPI avgt 5 0.286 ± 0.025 #/op
JMHSample_36_BranchPrediction.sorted:·branch-misses avgt 5 ≈ 10⁻⁴ #/op
JMHSample_36_BranchPrediction.sorted:·branches avgt 5 7.606 ± 1.742 #/op
JMHSample_36_BranchPrediction.sorted:·cycles avgt 5 8.998 ± 1.081 #/op
JMHSample_36_BranchPrediction.sorted:·instructions avgt 5 31.442 ± 4.899 #/op
JMHSample_36_BranchPrediction.unsorted avgt 25 5.943 ± 0.018 ns/op
JMHSample_36_BranchPrediction.unsorted:·CPI avgt 5 0.775 ± 0.052 #/op
JMHSample_36_BranchPrediction.unsorted:·branch-misses avgt 5 0.529 ± 0.026 #/op <--- OOPS
JMHSample_36_BranchPrediction.unsorted:·branches avgt 5 7.841 ± 0.046 #/op
JMHSample_36_BranchPrediction.unsorted:·cycles avgt 5 24.793 ± 0.434 #/op
JMHSample_36_BranchPrediction.unsorted:·instructions avgt 5 31.994 ± 2.342 #/op
It is an open question if you want to measure only one of these tests. In many cases, you have to measure
both to get the proper best-case and worst-case estimate!
*/
/*
* ============================== HOW TO RUN THIS TEST: ====================================
*
* You can run this test:
*
* a) Via the command line:
* $ mvn clean install
* $ java -jar target/benchmarks.jar JMHSample_36
*
* 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_36_BranchPrediction.class.getSimpleName() + ".*")
.build();
new Runner(opt).run();
}
}
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