版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/wangbingfengf98/article/details/89048582
It's impossible to write JMH code to run it from the command line, but recommended approach is to let the JMH system run the tests for we.
JMH attempts to make benchmarks as easy as possible.
// validating/jmh/JMH1.java
// (c)2017 MindView LLC: see Copyright.txt
// We make no guarantees that this code is fit for any purpose.
// Visit http://OnJava8.com for more book information.
package validating.jmh;
import java.util.*;
import java.util.concurrent.TimeUnit;
import org.openjdk.jmh.annotations.*;
@State(Scope.Thread)
@BenchmarkMode(Mode.AverageTime)
@OutputTimeUnit(TimeUnit.MICROSECONDS)
// Increase these three for more accuracy:
@Warmup(iterations = 5)
@Measurement(iterations = 5)
@Fork(1)
public class JMH1 {
private long[] la;
@Setup
public void setup() {
la = new long[250_000_000];
}
@Benchmark
public void setAll() {
Arrays.setAll(la, n -> n);
}
@Benchmark
public void parallelSetAll() {
Arrays.parallelSetAll(la, n -> n);
}
}
// validating/jmh/JMH2.java
// (c)2017 MindView LLC: see Copyright.txt
// We make no guarantees that this code is fit for any purpose.
// Visit http://OnJava8.com for more book information.
package validating.jmh;
import java.util.*;
import java.util.concurrent.TimeUnit;
import org.openjdk.jmh.annotations.*;
@State(Scope.Thread)
@BenchmarkMode(Mode.AverageTime)
@OutputTimeUnit(TimeUnit.MICROSECONDS)
@Warmup(iterations = 5)
@Measurement(iterations = 5)
@Fork(1)
public class JMH2 {
private long[] la;
@Param({
"1",
"10",
"100",
"1000",
"10000",
"100000",
"1000000",
"10000000",
"100000000",
"250000000"
})
int size;
@Setup
public void setup() {
la = new long[size];
}
@Benchmark
public void setAll() {
Arrays.setAll(la, n -> n);
}
@Benchmark
public void parallelSetAll() {
Arrays.parallelSetAll(la, n -> n);
}
}
// validating/jmh/JMH3.java
// (c)2017 MindView LLC: see Copyright.txt
// We make no guarantees that this code is fit for any purpose.
// Visit http://OnJava8.com for more book information.
package validating.jmh;
import java.util.*;
import java.util.concurrent.TimeUnit;
import org.openjdk.jmh.annotations.*;
@State(Scope.Thread)
@BenchmarkMode(Mode.AverageTime)
@OutputTimeUnit(TimeUnit.MICROSECONDS)
@Warmup(iterations = 5)
@Measurement(iterations = 5)
@Fork(1)
public class JMH3 {
private long[] la;
@Param({
"1",
"10",
"100",
"1000",
"10000",
"100000",
"1000000",
"10000000",
"100000000",
"250000000"
})
int size;
@Setup
public void setup() {
la = new long[size];
}
public static long f(long x) {
long quadratic = 42 * x * x + 19 * x + 47;
return Long.divideUnsigned(quadratic, x + 1);
}
@Benchmark
public void setAll() {
Arrays.setAll(la, n -> f(n));
}
@Benchmark
public void parallelSetAll() {
Arrays.parallelSetAll(la, n -> f(n));
}
}
My output:
Benchmark (size) Mode Cnt Score Error Units
JMH1.parallelSetAll N/A avgt 5 157727.439 ± 6052.117 us/op
JMH1.setAll N/A avgt 5 169563.712 ± 6135.280 us/op
JMH2.parallelSetAll 1 avgt 5 0.036 ± 0.001 us/op
JMH2.parallelSetAll 10 avgt 5 19.264 ± 2.765 us/op
JMH2.parallelSetAll 100 avgt 5 16.902 ± 4.910 us/op
JMH2.parallelSetAll 1000 avgt 5 22.066 ± 21.134 us/op
JMH2.parallelSetAll 10000 avgt 5 18.744 ± 4.856 us/op
JMH2.parallelSetAll 100000 avgt 5 33.685 ± 6.873 us/op
JMH2.parallelSetAll 1000000 avgt 5 221.920 ± 71.894 us/op
JMH2.parallelSetAll 10000000 avgt 5 6421.304 ± 365.326 us/op
JMH2.parallelSetAll 100000000 avgt 5 62885.102 ± 1960.576 us/op
JMH2.parallelSetAll 250000000 avgt 5 157213.034 ± 4628.642 us/op
JMH2.setAll 1 avgt 5 0.001 ± 0.001 us/op
JMH2.setAll 10 avgt 5 0.004 ± 0.001 us/op
JMH2.setAll 100 avgt 5 0.032 ± 0.004 us/op
JMH2.setAll 1000 avgt 5 0.324 ± 0.019 us/op
JMH2.setAll 10000 avgt 5 3.382 ± 0.135 us/op
JMH2.setAll 100000 avgt 5 36.090 ± 2.067 us/op
JMH2.setAll 1000000 avgt 5 394.066 ± 46.446 us/op
JMH2.setAll 10000000 avgt 5 6507.366 ± 291.420 us/op
JMH2.setAll 100000000 avgt 5 66977.791 ± 4388.401 us/op
JMH2.setAll 250000000 avgt 5 168200.696 ± 10460.397 us/op
JMH3.parallelSetAll 1 avgt 5 0.044 ± 0.005 us/op
JMH3.parallelSetAll 10 avgt 5 19.242 ± 6.843 us/op
JMH3.parallelSetAll 100 avgt 5 18.658 ± 6.920 us/op
JMH3.parallelSetAll 1000 avgt 5 25.622 ± 13.989 us/op
JMH3.parallelSetAll 10000 avgt 5 51.037 ± 3.264 us/op
JMH3.parallelSetAll 100000 avgt 5 354.044 ± 63.255 us/op
JMH3.parallelSetAll 1000000 avgt 5 3042.517 ± 464.108 us/op
JMH3.parallelSetAll 10000000 avgt 5 27311.351 ± 1740.651 us/op
JMH3.parallelSetAll 100000000 avgt 5 201354.372 ± 6309.172 us/op
JMH3.parallelSetAll 250000000 avgt 5 482230.222 ± 34593.250 us/op
JMH3.setAll 1 avgt 5 0.011 ± 0.001 us/op
JMH3.setAll 10 avgt 5 0.084 ± 0.002 us/op
JMH3.setAll 100 avgt 5 0.826 ± 0.005 us/op
JMH3.setAll 1000 avgt 5 8.218 ± 0.141 us/op
JMH3.setAll 10000 avgt 5 83.738 ± 0.937 us/op
JMH3.setAll 100000 avgt 5 804.059 ± 16.302 us/op
JMH3.setAll 1000000 avgt 5 8376.048 ± 101.816 us/op
JMH3.setAll 10000000 avgt 5 83754.080 ± 949.626 us/op
JMH3.setAll 100000000 avgt 5 841625.117 ± 15733.665 us/op
JMH3.setAll 250000000 avgt 5 2097034.637 ± 15399.819 us/opavgt: Average Time
us/op Microseconds per operation
see more info click JMH samples.
Aprofiler finds the slow spots so we can look for the easiest, most obvious way speed things up.
Note the difference between profiling and benchmarking. Profiling looks at our completed program working on our actual data, whereas benchmarking looks at an isolated fragment of a program, typically to optimize an algorithm
references:
1. On Java 8 - Bruce Eckel
2. https://github.com/wangbingfeng/OnJava8-Examples/blob/master/validating/jmh/JMH1.java
3. https://github.com/wangbingfeng/OnJava8-Examples/blob/master/validating/jmh/JMH2.java
4. https://github.com/wangbingfeng/OnJava8-Examples/blob/master/validating/jmh/JMH3.java