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# WARNING: Unable to acquire the JMH lock (/tmp/jmh.lock): already taken by another JMH instance, ignored by user's request.
# JMH 1.5.2 (released 152 days ago, please consider updating!)
# VM invoker: /home/my/dev/java/jdk1.8.0_40/jre/bin/java
# VM options: -javaagent:/usr/share/java/jayatanaag.jar -Djmh.ignoreLock=true
# Warmup: 20 iterations, 1 s each
# Measurement: 10 iterations, 1000 ms each, 100 calls per op
# Timeout: 10 min per iteration
# Threads: 1 thread, will synchronize iterations
# Benchmark mode: Sampling time
# Benchmark: fr.soat.SampleTimeModeSampleBenchmark.benchmarkSomething
# Run progress: 0,00% complete, ETA 00:05:00
# Fork: 1 of 10
Picked up JAVA_TOOL_OPTIONS: -javaagent:/usr/share/java/jayatanaag.jar
# Warmup Iteration 1: n = 39755, mean = 1 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 23, 957, 12896 ns/op
# Warmup Iteration 2: n = 20437, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 68, 71 ns/op
# Warmup Iteration 3: n = 10160, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 73 ns/op
# Warmup Iteration 4: n = 10217, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 1, 1 ns/op
# Warmup Iteration 5: n = 10221, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 71, 73 ns/op
# Warmup Iteration 6: n = 10235, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 74 ns/op
# Warmup Iteration 7: n = 10229, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 1, 1 ns/op
# Warmup Iteration 8: n = 10083, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 71, 72 ns/op
# Warmup Iteration 9: n = 10173, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 28, 28 ns/op
# Warmup Iteration 10: n = 10202, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 71, 72 ns/op
# Warmup Iteration 11: n = 10153, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 1, 1 ns/op
# Warmup Iteration 12: n = 10083, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 79, 79 ns/op
# Warmup Iteration 13: n = 10008, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 5, 5 ns/op
# Warmup Iteration 14: n = 10031, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 3, 3 ns/op
# Warmup Iteration 15: n = 10086, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 2, 71, 72 ns/op
# Warmup Iteration 16: n = 10099, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 4, 4 ns/op
# Warmup Iteration 17: n = 10157, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 70, 71 ns/op
# Warmup Iteration 18: n = 10115, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 72, 73 ns/op
# Warmup Iteration 19: n = 10137, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 71, 72 ns/op
# Warmup Iteration 20: n = 10076, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 2, 70, 71 ns/op
Iteration 1: n = 100, mean = 25 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 25, 136, 137, 137, 137 ns/op
Iteration 2: n = 102, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 95, 97, 97, 97 ns/op
Iteration 3: n = 102, mean = 26 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 97, 97, 97, 97 ns/op
Iteration 4: n = 102, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 23, 23, 93, 95, 95, 95 ns/op
Iteration 5: n = 99, mean = 25 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 97, 97, 97, 97 ns/op
Iteration 6: n = 100, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 25, 25, 25, 25 ns/op
Iteration 7: n = 100, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 25, 25, 25, 25, 25, 25 ns/op
Iteration 8: n = 100, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 94, 95, 95, 95 ns/op
Iteration 9: n = 101, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 25, 25, 25, 25 ns/op
Iteration 10: n = 99, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 25, 25, 25, 25, 25 ns/op
# Run progress: 10,00% complete, ETA 00:04:34
# Fork: 2 of 10
Picked up JAVA_TOOL_OPTIONS: -javaagent:/usr/share/java/jayatanaag.jar
# Warmup Iteration 1: n = 39190, mean = 1 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 58, 1023, 7448 ns/op
# Warmup Iteration 2: n = 20517, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 72 ns/op
# Warmup Iteration 3: n = 19600, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 4, 73 ns/op
# Warmup Iteration 4: n = 10099, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 73 ns/op
# Warmup Iteration 5: n = 10072, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 74, 74 ns/op
# Warmup Iteration 6: n = 10059, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 73 ns/op
# Warmup Iteration 7: n = 10043, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 72 ns/op
# Warmup Iteration 8: n = 10061, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 73 ns/op
# Warmup Iteration 9: n = 10034, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 75, 75 ns/op
# Warmup Iteration 10: n = 10172, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 0, 0 ns/op
# Warmup Iteration 11: n = 10096, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 71, 72 ns/op
# Warmup Iteration 12: n = 10177, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 0, 0 ns/op
# Warmup Iteration 13: n = 10109, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 71, 72 ns/op
# Warmup Iteration 14: n = 10085, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 71, 72 ns/op
# Warmup Iteration 15: n = 10120, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 1, 1 ns/op
# Warmup Iteration 16: n = 10050, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 1, 1 ns/op
# Warmup Iteration 17: n = 10148, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 7, 7 ns/op
# Warmup Iteration 18: n = 10055, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 1, 1 ns/op
# Warmup Iteration 19: n = 10004, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 5, 5 ns/op
# Warmup Iteration 20: n = 10017, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 73 ns/op
Iteration 1: n = 101, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 25, 25, 25, 25 ns/op
Iteration 2: n = 101, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 25, 25, 25, 25 ns/op
Iteration 3: n = 101, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 23, 24, 26, 26, 26, 26 ns/op
Iteration 4: n = 102, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 34, 34, 34, 34 ns/op
Iteration 5: n = 101, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 25, 25, 25, 25 ns/op
Iteration 6: n = 101, mean = 26 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 128, 129, 129, 129 ns/op
Iteration 7: n = 97, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 25, 26, 26, 26, 26 ns/op
Iteration 8: n = 101, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 24, 24, 24, 24 ns/op
Iteration 9: n = 101, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 25, 26, 26, 26, 26 ns/op
Iteration 10: n = 101, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 25, 25, 25, 25 ns/op
# Run progress: 20,00% complete, ETA 00:04:03
# Fork: 3 of 10
Picked up JAVA_TOOL_OPTIONS: -javaagent:/usr/share/java/jayatanaag.jar
# Warmup Iteration 1: n = 39117, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 54, 449, 4704 ns/op
# Warmup Iteration 2: n = 20282, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 73 ns/op
# Warmup Iteration 3: n = 10091, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 74, 74 ns/op
# Warmup Iteration 4: n = 10361, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 73 ns/op
# Warmup Iteration 5: n = 10219, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 2, 2 ns/op
# Warmup Iteration 6: n = 10128, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 73 ns/op
# Warmup Iteration 7: n = 10179, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 0, 0 ns/op
# Warmup Iteration 8: n = 10077, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 74 ns/op
# Warmup Iteration 9: n = 10170, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 70, 71 ns/op
# Warmup Iteration 10: n = 10088, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 1, 1 ns/op
# Warmup Iteration 11: n = 10095, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 73 ns/op
# Warmup Iteration 12: n = 10106, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 73 ns/op
# Warmup Iteration 13: n = 10117, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 73 ns/op
# Warmup Iteration 14: n = 10016, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 72 ns/op
# Warmup Iteration 15: n = 9968, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 71, 71 ns/op
# Warmup Iteration 16: n = 10160, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 2, 2 ns/op
# Warmup Iteration 17: n = 10134, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 0, 0 ns/op
# Warmup Iteration 18: n = 10030, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 74, 74 ns/op
# Warmup Iteration 19: n = 10073, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 0, 0 ns/op
# Warmup Iteration 20: n = 10048, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 0, 0 ns/op
Iteration 1: n = 101, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 95, 96, 96, 96 ns/op
Iteration 2: n = 101, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 24, 24, 24, 24 ns/op
Iteration 3: n = 100, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 25, 25, 25, 25 ns/op
Iteration 4: n = 100, mean = 25 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 97, 97, 97, 97 ns/op
Iteration 5: n = 101, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 95, 96, 96, 96 ns/op
Iteration 6: n = 102, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 24, 24, 24, 24 ns/op
Iteration 7: n = 102, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 92, 94, 94, 94 ns/op
Iteration 8: n = 101, mean = 26 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 96, 96, 96, 96 ns/op
Iteration 9: n = 101, mean = 25 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 23, 24, 97, 97, 97, 97 ns/op
Iteration 10: n = 102, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 25, 25, 25, 25 ns/op
# Run progress: 30,00% complete, ETA 00:03:32
# Fork: 4 of 10
Picked up JAVA_TOOL_OPTIONS: -javaagent:/usr/share/java/jayatanaag.jar
# Warmup Iteration 1: n = 20132, mean = 1 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 265, 2195, 2568 ns/op
# Warmup Iteration 2: n = 10223, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 71, 73 ns/op
# Warmup Iteration 3: n = 9345, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 72 ns/op
# Warmup Iteration 4: n = 9278, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 72 ns/op
# Warmup Iteration 5: n = 9265, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 72 ns/op
# Warmup Iteration 6: n = 9232, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 71, 71 ns/op
# Warmup Iteration 7: n = 9217, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 74, 74 ns/op
# Warmup Iteration 8: n = 9228, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 73 ns/op
# Warmup Iteration 9: n = 9272, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 74, 74 ns/op
# Warmup Iteration 10: n = 9355, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 2, 2 ns/op
# Warmup Iteration 11: n = 9312, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 73, 73 ns/op
# Warmup Iteration 12: n = 9328, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 72 ns/op
# Warmup Iteration 13: n = 9187, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 72 ns/op
# Warmup Iteration 14: n = 9246, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 3, 3 ns/op
# Warmup Iteration 15: n = 9243, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 2, 2 ns/op
# Warmup Iteration 16: n = 9262, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 10, 10 ns/op
# Warmup Iteration 17: n = 9289, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 2, 2 ns/op
# Warmup Iteration 18: n = 9215, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 1, 1 ns/op
# Warmup Iteration 19: n = 9220, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 73 ns/op
# Warmup Iteration 20: n = 9238, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 0, 0 ns/op
Iteration 1: n = 103, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 22, 23, 24, 24, 24, 24, 24, 24 ns/op
Iteration 2: n = 102, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 22, 23, 24, 24, 24, 24, 24, 24 ns/op
Iteration 3: n = 103, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 22, 23, 23, 24, 24, 24, 24, 24 ns/op
Iteration 4: n = 102, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 22, 23, 24, 24, 26, 26, 26, 26 ns/op
Iteration 5: n = 102, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 22, 23, 24, 24, 25, 25, 25, 25 ns/op
Iteration 6: n = 103, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 22, 23, 24, 24, 25, 25, 25, 25 ns/op
Iteration 7: n = 101, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 22, 23, 24, 24, 25, 25, 25, 25 ns/op
Iteration 8: n = 102, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 22, 23, 24, 24, 24, 24, 24, 24 ns/op
Iteration 9: n = 102, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 22, 23, 24, 24, 95, 97, 97, 97 ns/op
Iteration 10: n = 104, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 22, 23, 23, 24, 24, 24, 24, 24 ns/op
# Run progress: 40,00% complete, ETA 00:03:02
# Fork: 5 of 10
Picked up JAVA_TOOL_OPTIONS: -javaagent:/usr/share/java/jayatanaag.jar
# Warmup Iteration 1: n = 39763, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 42, 301, 2528 ns/op
# Warmup Iteration 2: n = 20446, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 73, 73 ns/op
# Warmup Iteration 3: n = 10208, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 70, 71 ns/op
# Warmup Iteration 4: n = 10038, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 15, 15 ns/op
# Warmup Iteration 5: n = 10041, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 72 ns/op
# Warmup Iteration 6: n = 10019, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 72 ns/op
# Warmup Iteration 7: n = 10176, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 73 ns/op
# Warmup Iteration 8: n = 10042, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 72, 72 ns/op
# Warmup Iteration 9: n = 9996, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 73 ns/op
# Warmup Iteration 10: n = 10131, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 74, 75 ns/op
# Warmup Iteration 11: n = 10107, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 70, 70 ns/op
# Warmup Iteration 12: n = 9937, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 73 ns/op
# Warmup Iteration 13: n = 10128, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 1, 1 ns/op
# Warmup Iteration 14: n = 10063, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 72 ns/op
# Warmup Iteration 15: n = 10009, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 72, 72 ns/op
# Warmup Iteration 16: n = 9923, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 2, 2 ns/op
# Warmup Iteration 17: n = 10142, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 70, 71 ns/op
# Warmup Iteration 18: n = 10051, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 80, 80 ns/op
# Warmup Iteration 19: n = 10012, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 70, 70 ns/op
# Warmup Iteration 20: n = 10048, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 72, 72 ns/op
Iteration 1: n = 100, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 24, 26, 26, 26, 26 ns/op
Iteration 2: n = 100, mean = 25 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 24, 111, 112, 112, 112 ns/op
Iteration 3: n = 101, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 24, 25, 25, 25, 25 ns/op
Iteration 4: n = 101, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 24, 25, 25, 25, 25 ns/op
Iteration 5: n = 101, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 97, 98, 98, 98 ns/op
Iteration 6: n = 99, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 25, 27, 27, 27, 27 ns/op
Iteration 7: n = 99, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 25, 25, 25, 25, 25 ns/op
Iteration 8: n = 101, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 24, 25, 25, 25, 25 ns/op
Iteration 9: n = 102, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 94, 96, 96, 96 ns/op
Iteration 10: n = 101, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 24, 24, 24, 24, 24 ns/op
# Run progress: 50,00% complete, ETA 00:02:31
# Fork: 6 of 10
Picked up JAVA_TOOL_OPTIONS: -javaagent:/usr/share/java/jayatanaag.jar
# Warmup Iteration 1: n = 38717, mean = 1 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 81, 2027, 5640 ns/op
# Warmup Iteration 2: n = 20235, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 5, 75 ns/op
# Warmup Iteration 3: n = 10090, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 1, 1 ns/op
# Warmup Iteration 4: n = 10150, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 70, 71 ns/op
# Warmup Iteration 5: n = 9973, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 72, 72 ns/op
# Warmup Iteration 6: n = 9993, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 73, 73 ns/op
# Warmup Iteration 7: n = 10079, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 74, 74 ns/op
# Warmup Iteration 8: n = 10104, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 73, 73 ns/op
# Warmup Iteration 9: n = 10042, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 72, 72 ns/op
# Warmup Iteration 10: n = 10163, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 70, 71 ns/op
# Warmup Iteration 11: n = 10146, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 1, 1 ns/op
# Warmup Iteration 12: n = 10099, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 17, 17 ns/op
# Warmup Iteration 13: n = 10050, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 2, 2 ns/op
# Warmup Iteration 14: n = 9946, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 2, 2 ns/op
# Warmup Iteration 15: n = 10052, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 70, 70 ns/op
# Warmup Iteration 16: n = 10013, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 2, 2 ns/op
# Warmup Iteration 17: n = 9971, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 73 ns/op
# Warmup Iteration 18: n = 10038, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 76, 76 ns/op
# Warmup Iteration 19: n = 10127, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 73 ns/op
# Warmup Iteration 20: n = 10170, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 3, 3 ns/op
Iteration 1: n = 102, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 23, 24, 24, 24, 24, 24 ns/op
Iteration 2: n = 102, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 25, 25, 25, 25 ns/op
Iteration 3: n = 101, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 26, 26, 26, 26 ns/op
Iteration 4: n = 99, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 103, 103, 103, 103 ns/op
Iteration 5: n = 102, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 23, 23, 94, 96, 96, 96 ns/op
Iteration 6: n = 103, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 23, 23, 94, 97, 97, 97 ns/op
Iteration 7: n = 102, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 25, 25, 25, 25 ns/op
Iteration 8: n = 100, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 25, 25, 25, 25 ns/op
Iteration 9: n = 100, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 49, 49, 49, 49 ns/op
Iteration 10: n = 101, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 24, 24, 24, 24 ns/op
# Run progress: 60,00% complete, ETA 00:02:01
# Fork: 7 of 10
Picked up JAVA_TOOL_OPTIONS: -javaagent:/usr/share/java/jayatanaag.jar
# Warmup Iteration 1: n = 39742, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 22, 337, 8096 ns/op
# Warmup Iteration 2: n = 20158, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 2, 4 ns/op
# Warmup Iteration 3: n = 17323, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 123, 190 ns/op
# Warmup Iteration 4: n = 18413, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 16, 74 ns/op
# Warmup Iteration 5: n = 18596, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 13, 83 ns/op
# Warmup Iteration 6: n = 18691, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 10, 72 ns/op
# Warmup Iteration 7: n = 18382, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 72, 73 ns/op
# Warmup Iteration 8: n = 18236, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 73 ns/op
# Warmup Iteration 9: n = 18498, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 74 ns/op
# Warmup Iteration 10: n = 18377, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 72, 72 ns/op
# Warmup Iteration 11: n = 17946, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 127, 337 ns/op
# Warmup Iteration 12: n = 18552, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 72 ns/op
# Warmup Iteration 13: n = 18376, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 71, 72 ns/op
# Warmup Iteration 14: n = 18576, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 72 ns/op
# Warmup Iteration 15: n = 18187, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 220, 776 ns/op
# Warmup Iteration 16: n = 18459, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 73, 73 ns/op
# Warmup Iteration 17: n = 18135, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 27, 73 ns/op
# Warmup Iteration 18: n = 18323, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 8, 35 ns/op
# Warmup Iteration 19: n = 18205, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 2, 4 ns/op
# Warmup Iteration 20: n = 17887, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 18, 73 ns/op
Iteration 1: n = 201, mean = 37 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 24, 95, 2728, 2728, 2728 ns/op
Iteration 2: n = 205, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 26, 97, 97, 97 ns/op
Iteration 3: n = 201, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 22, 24, 24, 25, 25, 61, 61, 61 ns/op
Iteration 4: n = 199, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 25, 25, 26, 26, 26 ns/op
Iteration 5: n = 202, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 22, 23, 24, 24, 24, 28, 28, 28 ns/op
Iteration 6: n = 202, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 22, 24, 24, 24, 30, 103, 103, 103 ns/op
Iteration 7: n = 198, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 22, 24, 24, 24, 96, 96, 96, 96 ns/op
Iteration 8: n = 202, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 22, 24, 24, 24, 25, 25, 25, 25 ns/op
Iteration 9: n = 201, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 24, 25, 25, 25, 25 ns/op
Iteration 10: n = 199, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 22, 24, 24, 25, 26, 95, 95, 95 ns/op
# Run progress: 70,00% complete, ETA 00:01:31
# Fork: 8 of 10
Picked up JAVA_TOOL_OPTIONS: -javaagent:/usr/share/java/jayatanaag.jar
# Warmup Iteration 1: n = 38519, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 79, 432, 2560 ns/op
# Warmup Iteration 2: n = 19926, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 74, 184 ns/op
# Warmup Iteration 3: n = 19973, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 71, 74 ns/op
# Warmup Iteration 4: n = 19985, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 74, 74 ns/op
# Warmup Iteration 5: n = 19469, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 6, 72 ns/op
# Warmup Iteration 6: n = 19683, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 73, 73 ns/op
# Warmup Iteration 7: n = 19928, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 73, 94 ns/op
# Warmup Iteration 8: n = 19847, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 72 ns/op
# Warmup Iteration 9: n = 19915, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 73 ns/op
# Warmup Iteration 10: n = 19866, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 2, 2 ns/op
# Warmup Iteration 11: n = 19964, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 1, 73 ns/op
# Warmup Iteration 12: n = 19942, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 1, 72 ns/op
# Warmup Iteration 13: n = 20001, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 73 ns/op
# Warmup Iteration 14: n = 19977, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 2, 73 ns/op
# Warmup Iteration 15: n = 19641, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 74, 126 ns/op
# Warmup Iteration 16: n = 19664, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 73 ns/op
# Warmup Iteration 17: n = 19945, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 18, 71 ns/op
# Warmup Iteration 18: n = 19645, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 73 ns/op
# Warmup Iteration 19: n = 19648, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 73 ns/op
# Warmup Iteration 20: n = 19968, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 71, 73 ns/op
Iteration 1: n = 197, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 42, 47, 47, 47 ns/op
Iteration 2: n = 199, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 25, 100, 100, 100 ns/op
Iteration 3: n = 200, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 24, 95, 96, 96, 96 ns/op
Iteration 4: n = 193, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 24, 29, 96, 96, 96 ns/op
Iteration 5: n = 199, mean = 25 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 24, 96, 98, 98, 98 ns/op
Iteration 6: n = 199, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 24, 95, 97, 97, 97 ns/op
Iteration 7: n = 198, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 25, 27, 97, 97, 97 ns/op
Iteration 8: n = 199, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 24, 25, 25, 25, 25 ns/op
Iteration 9: n = 196, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 27, 97, 97, 97 ns/op
Iteration 10: n = 200, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 24, 96, 96, 96 ns/op
# Run progress: 80,00% complete, ETA 00:01:00
# Fork: 9 of 10
Picked up JAVA_TOOL_OPTIONS: -javaagent:/usr/share/java/jayatanaag.jar
# Warmup Iteration 1: n = 38346, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 73, 381, 3384 ns/op
# Warmup Iteration 2: n = 20053, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 1, 78 ns/op
# Warmup Iteration 3: n = 19016, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 28, 268 ns/op
# Warmup Iteration 4: n = 19069, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 194, 338 ns/op
# Warmup Iteration 5: n = 19973, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 71, 73 ns/op
# Warmup Iteration 6: n = 19971, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 24, 73 ns/op
# Warmup Iteration 7: n = 19983, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 2, 73 ns/op
# Warmup Iteration 8: n = 19968, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 73 ns/op
# Warmup Iteration 9: n = 19973, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 73 ns/op
# Warmup Iteration 10: n = 19995, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 70, 73 ns/op
# Warmup Iteration 11: n = 19978, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 3, 72 ns/op
# Warmup Iteration 12: n = 10001, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 72, 72 ns/op
# Warmup Iteration 13: n = 9953, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 72, 72 ns/op
# Warmup Iteration 14: n = 9958, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 73, 73 ns/op
# Warmup Iteration 15: n = 9990, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 74, 74 ns/op
# Warmup Iteration 16: n = 9947, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 2, 2 ns/op
# Warmup Iteration 17: n = 8679, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 404, 404 ns/op
# Warmup Iteration 18: n = 9772, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 72, 72 ns/op
# Warmup Iteration 19: n = 9921, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 2, 73, 73 ns/op
# Warmup Iteration 20: n = 9692, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 1, 3, 3 ns/op
Iteration 1: n = 97, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 24, 97, 97, 97, 97 ns/op
Iteration 2: n = 99, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 25, 97, 97, 97, 97 ns/op
Iteration 3: n = 99, mean = 26 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 25, 25, 97, 97, 97, 97 ns/op
Iteration 4: n = 99, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 25, 25, 25, 25, 25 ns/op
Iteration 5: n = 100, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 24, 95, 96, 96, 96 ns/op
Iteration 6: n = 99, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 24, 25, 25, 25, 25 ns/op
Iteration 7: n = 98, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 25, 29, 29, 29, 29 ns/op
Iteration 8: n = 100, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 26, 26, 26, 26 ns/op
Iteration 9: n = 100, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 24, 96, 97, 97, 97 ns/op
Iteration 10: n = 99, mean = 25 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 25, 97, 97, 97, 97 ns/op
# Run progress: 90,00% complete, ETA 00:00:30
# Fork: 10 of 10
Picked up JAVA_TOOL_OPTIONS: -javaagent:/usr/share/java/jayatanaag.jar
# Warmup Iteration 1: n = 38445, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 71, 291, 2684 ns/op
# Warmup Iteration 2: n = 19961, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 72 ns/op
# Warmup Iteration 3: n = 19929, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 74 ns/op
# Warmup Iteration 4: n = 19868, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 86 ns/op
# Warmup Iteration 5: n = 19996, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 0, 0 ns/op
# Warmup Iteration 6: n = 19940, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 72 ns/op
# Warmup Iteration 7: n = 19887, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 73 ns/op
# Warmup Iteration 8: n = 19588, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 78, 230 ns/op
# Warmup Iteration 9: n = 19961, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 0, 73 ns/op
# Warmup Iteration 10: n = 19956, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 73 ns/op
# Warmup Iteration 11: n = 19587, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 73, 74 ns/op
# Warmup Iteration 12: n = 19881, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 1, 1 ns/op
# Warmup Iteration 13: n = 19920, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 2, 73 ns/op
# Warmup Iteration 14: n = 10003, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 74, 74 ns/op
# Warmup Iteration 15: n = 9719, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 1, 1 ns/op
# Warmup Iteration 16: n = 9869, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 0, 0 ns/op
# Warmup Iteration 17: n = 10006, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 72 ns/op
# Warmup Iteration 18: n = 9985, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 1, 1 ns/op
# Warmup Iteration 19: n = 10000, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 72, 72 ns/op
# Warmup Iteration 20: n = 9999, mean = 0 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 0, 0, 0, 0, 0, 0, 0, 0 ns/op
Iteration 1: n = 100, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 95, 96, 96, 96 ns/op
Iteration 2: n = 100, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 25, 25, 25, 25 ns/op
Iteration 3: n = 100, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 24, 24, 24, 24 ns/op
Iteration 4: n = 100, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 95, 96, 96, 96 ns/op
Iteration 5: n = 99, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 25, 25, 25, 25 ns/op
Iteration 6: n = 100, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 24, 24, 24, 24 ns/op
Iteration 7: n = 100, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 94, 95, 95, 95 ns/op
Iteration 8: n = 99, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 24, 24, 25, 25, 25, 25, 25 ns/op
Iteration 9: n = 100, mean = 23 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 25, 25, 25, 25 ns/op
Iteration 10: n = 100, mean = 24 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 23, 23, 24, 24, 95, 96, 96, 96 ns/op
Result: 24,033 ±(99.9%) 0,756 ns/op [Average]
Statistics: (min, avg, max) = (22,000, 24,033, 2728,000), stdev = 25,221
Confidence interval (99.9%): [23,276, 24,789]
Samples, N = 12042
mean = 24,033 ±(99.9%) 0,756 ns/op
min = 22,000 ns/op
p( 0,0000) = 22,000 ns/op
p(50,0000) = 23,000 ns/op
p(90,0000) = 24,000 ns/op
p(95,0000) = 24,000 ns/op
p(99,0000) = 25,000 ns/op
p(99,9000) = 97,000 ns/op
p(99,9900) = 2198,659 ns/op
p(99,9990) = 2728,000 ns/op
p(99,9999) = 2728,000 ns/op
max = 2728,000 ns/op
# Run complete. Total time: 00:05:03
Benchmark Mode Cnt Score Error Units
SampleTimeModeSampleBenchmark.benchmarkSomething sample 12042 24,033 ± 0,756 ns/op