@OperationsPerInvocation
allows you to communicate the benchmark does more than one operation, and let
JMH to adjust the scores appropriately.
For example, a benchmark which uses the internal
loop to have multiple operations, may want to measure the performance of a
single operation.
@Benchmark
@OperationsPerInvocation(10)
public BigInteger add10TimesWithInvocationCount() {
for (int i = 0; i < 10; i++) {
b1.add(b2);
}
return b1;
}
This
annotation may be put at Benchmark method to have effect on that method only,
or at the enclosing class instance to have the effect over all Benchmark
methods in the class.
Find the
below working application.
OperationsPerInvocationDemo.java
package com.sample.app;
import java.math.BigInteger;
import java.util.concurrent.TimeUnit;
import org.openjdk.jmh.annotations.Benchmark;
import org.openjdk.jmh.annotations.BenchmarkMode;
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;
@State(Scope.Thread)
@BenchmarkMode(Mode.AverageTime)
@OutputTimeUnit(TimeUnit.NANOSECONDS)
public class OperationsPerInvocationDemo {
private BigInteger b1 = new BigInteger("23433686961963291297886553421867564211323");
private BigInteger b2 = new BigInteger("9788655342186756421132397886553421867564211323");
@Benchmark
@OperationsPerInvocation(10)
public BigInteger add10TimesWithInvocationCount() {
for (int i = 0; i < 10; i++) {
b1.add(b2);
}
return b1;
}
@Benchmark
public BigInteger add10Times() {
for (int i = 0; i < 10; i++) {
b1.add(b2);
}
return b1;
}
public static void main(String[] args) throws RunnerException {
Options opt = new OptionsBuilder()
.include(OperationsPerInvocationDemo.class.getSimpleName())
.forks(1)
.measurementIterations(10)
.warmupIterations(5)
.build();
new Runner(opt).run();
}
}
Output
# JMH version: 1.35
# VM version: JDK 15.0.2, Java HotSpot(TM) 64-Bit Server VM, 15.0.2+7-27
# VM invoker: /Library/Java/JavaVirtualMachines/jdk-15.0.2.jdk/Contents/Home/bin/java
# VM options: -Dfile.encoding=UTF-8 -XX:+ShowCodeDetailsInExceptionMessages
# Blackhole mode: full + dont-inline hint (auto-detected, use -Djmh.blackhole.autoDetect=false to disable)
# Warmup: 5 iterations, 10 s each
# Measurement: 10 iterations, 10 s each
# Timeout: 10 min per iteration
# Threads: 1 thread, will synchronize iterations
# Benchmark mode: Average time, time/op
# Benchmark: com.sample.app.OperationsPerInvocationDemo.add10Times
# Run progress: 0.00
# Fork: 1 of 1
# Warmup Iteration 1: 107.531 ns/op
# Warmup Iteration 2: 106.741 ns/op
# Warmup Iteration 3: 103.800 ns/op
# Warmup Iteration 4: 101.851 ns/op
# Warmup Iteration 5: 99.021 ns/op
Iteration 1: 97.184 ns/op
Iteration 2: 100.575 ns/op
Iteration 3: 101.358 ns/op
Iteration 4: 98.152 ns/op
Iteration 5: 102.376 ns/op
Iteration 6: 102.304 ns/op
Iteration 7: 101.872 ns/op
Iteration 8: 104.272 ns/op
Iteration 9: 104.036 ns/op
Iteration 10: 104.204 ns/op
Result "com.sample.app.OperationsPerInvocationDemo.add10Times":
101.633 ±(99.9
(min, avg, max) = (97.184, 101.633, 104.272), stdev = 2.439
CI (99.9
# JMH version: 1.35
# VM version: JDK 15.0.2, Java HotSpot(TM) 64-Bit Server VM, 15.0.2+7-27
# VM invoker: /Library/Java/JavaVirtualMachines/jdk-15.0.2.jdk/Contents/Home/bin/java
# VM options: -Dfile.encoding=UTF-8 -XX:+ShowCodeDetailsInExceptionMessages
# Blackhole mode: full + dont-inline hint (auto-detected, use -Djmh.blackhole.autoDetect=false to disable)
# Warmup: 5 iterations, 10 s each
# Measurement: 10 iterations, 10 s each
# Timeout: 10 min per iteration
# Threads: 1 thread, will synchronize iterations
# Benchmark mode: Average time, time/op
# Benchmark: com.sample.app.OperationsPerInvocationDemo.add10TimesWithInvocationCount
# Run progress: 50.00
# Fork: 1 of 1
# Warmup Iteration 1: 10.766 ns/op
# Warmup Iteration 2: 10.316 ns/op
# Warmup Iteration 3: 9.563 ns/op
# Warmup Iteration 4: 9.545 ns/op
# Warmup Iteration 5: 9.820 ns/op
Iteration 1: 10.140 ns/op
Iteration 2: 10.121 ns/op
Iteration 3: 10.178 ns/op
Iteration 4: 10.023 ns/op
Iteration 5: 9.588 ns/op
Iteration 6: 9.473 ns/op
Iteration 7: 9.595 ns/op
Iteration 8: 9.559 ns/op
Iteration 9: 9.677 ns/op
Iteration 10: 9.419 ns/op
Result "com.sample.app.OperationsPerInvocationDemo.add10TimesWithInvocationCount":
9.777 ±(99.9
(min, avg, max) = (9.419, 9.777, 10.178), stdev = 0.302
CI (99.9
# Run complete. Total time: 00:05:00
REMEMBER: The numbers below are just data. To gain reusable insights, you need to follow up on
why the numbers are the way they are. Use profilers (see -prof, -lprof), design factorial
experiments, perform baseline and negative tests that provide experimental control, make sure
the benchmarking environment is safe on JVM/OS/HW level, ask for reviews from the domain experts.
Do not assume the numbers tell you what you want them to tell.
Benchmark Mode Cnt Score Error Units
OperationsPerInvocationDemo.add10Times avgt 10 101.633 ± 3.687 ns/op
OperationsPerInvocationDemo.add10TimesWithInvocationCount avgt 10 9.777 ± 0.456 ns/op
From the
output, you can notice that the average time for the method add10TimesWithInvocationCount
is calculated for each loop iteration.
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