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ruippeixotog/ListSetBenchmark.scala

Created May 8, 2016 14:14
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ListSet JMH Benchmark
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listset_custom_builder_results.txt
[info] Running org.openjdk.jmh.Main -i 5 -wi 5 -f2 -t1 .*ListSetBenchmark
[info] # JMH 1.11.3 (released 115 days ago, please consider updating!)
[info] # VM version: JDK 1.8.0_25, VM 25.25-b02
[info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_25.jdk/Contents/Home/jre/bin/java
[info] # VM options: <none>
[info] # Warmup: 5 iterations, 1 s each
[info] # Measurement: 5 iterations, 1 s each
[info] # Timeout: 10 min per iteration
[info] # Threads: 1 thread, will synchronize iterations
[info] # Benchmark mode: Sampling time
[info] # Benchmark: net.ruippeixotog.jmh.ListSetBenchmark.createListSet0
[info]
[info] # Run progress: 0.00% complete, ETA 00:04:00
[info] # Fork: 1 of 2
[info] # Warmup Iteration 1: n = 33533, mean = 7057 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 16, 42, 51, 58, 635, 6305, 23927, 234356736 ns/op
[info] # Warmup Iteration 2: n = 21147, mean = 51 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 29, 42, 51, 63, 126, 677, 18100, 20864 ns/op
[info] # Warmup Iteration 3: n = 18997, mean = 60 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 41, 55, 61, 74, 119, 415, 7549, 11040 ns/op
[info] # Warmup Iteration 4: n = 18826, mean = 63 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 41, 56, 64, 78, 128, 694, 11592, 15024 ns/op
[info] # Warmup Iteration 5: n = 18991, mean = 59 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 40, 55, 62, 74, 131, 488, 2818, 16768 ns/op
[info] Iteration 1: n = 18172, mean = 61 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 40, 56, 75, 96, 142, 520, 2164, 5760 ns/op
[info] Iteration 2: n = 17650, mean = 65 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 40, 57, 78, 99, 158, 527, 15462, 19808 ns/op
[info] Iteration 3: n = 18576, mean = 60 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 41, 56, 65, 80, 130, 463, 8797, 10608 ns/op
[info] Iteration 4: n = 17710, mean = 68 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 41, 56, 79, 99, 153, 584, 20574, 29408 ns/op
[info] Iteration 5: n = 16155, mean = 65 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 40, 58, 86, 103, 164, 519, 1501, 1794 ns/op
[info]
[info] # Run progress: 4.17% complete, ETA 00:03:58
[info] # Fork: 2 of 2
[info] # Warmup Iteration 1: n = 33185, mean = 6928 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 10, 40, 49, 60, 550, 10991, 25831, 227278848 ns/op
[info] # Warmup Iteration 2: n = 19834, mean = 52 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 29, 43, 63, 81, 133, 505, 9569, 10608 ns/op
[info] # Warmup Iteration 3: n = 10347, mean = 54 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 29, 43, 64, 81, 144, 904, 10137, 10192 ns/op
[info] # Warmup Iteration 4: n = 8722, mean = 71 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 42, 62, 83, 110, 180, 619, 19488, 19488 ns/op
[info] # Warmup Iteration 5: n = 9247, mean = 65 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 41, 61, 74, 86, 139, 421, 858, 858 ns/op
[info] Iteration 1: n = 9288, mean = 66 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 41, 60, 74, 89, 140, 373, 6256, 6256 ns/op
[info] Iteration 2: n = 9364, mean = 65 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 41, 60, 71, 80, 141, 482, 1974, 1974 ns/op
[info] Iteration 3: n = 9459, mean = 67 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 43, 60, 70, 81, 157, 572, 10512, 10512 ns/op
[info] Iteration 4: n = 9162, mean = 69 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 41, 60, 76, 97, 151, 676, 13264, 13264 ns/op
[info] Iteration 5: n = 9341, mean = 69 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 41, 60, 73, 84, 157, 508, 25600, 25600 ns/op
[info]
[info]
[info] Result "createListSet0":
[info] N = 134877
[info] mean = 64.911 ±(99.9%) 1.543 ns/op
[info]
[info] Histogram, ns/op:
[info] [ 0.000, 2500.000) = 134856
[info] [ 2500.000, 5000.000) = 1
[info] [ 5000.000, 7500.000) = 4
[info] [ 7500.000, 10000.000) = 6
[info] [10000.000, 12500.000) = 2
[info] [12500.000, 15000.000) = 4
[info] [15000.000, 17500.000) = 0
[info] [17500.000, 20000.000) = 2
[info] [20000.000, 22500.000) = 0
[info] [22500.000, 25000.000) = 0
[info] [25000.000, 27500.000) = 1
[info]
[info] Percentiles, ns/op:
[info] p(0.0000) = 40.000 ns/op
[info] p(50.0000) = 59.000 ns/op
[info] p(90.0000) = 75.000 ns/op
[info] p(95.0000) = 95.000 ns/op
[info] p(99.0000) = 150.000 ns/op
[info] p(99.9000) = 514.732 ns/op
[info] p(99.9900) = 8753.171 ns/op
[info] p(99.9990) = 28079.846 ns/op
[info] p(99.9999) = 29408.000 ns/op
[info] p(100.0000) = 29408.000 ns/op
[info]
[info]
[info] # JMH 1.11.3 (released 115 days ago, please consider updating!)
[info] # VM version: JDK 1.8.0_25, VM 25.25-b02
[info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_25.jdk/Contents/Home/jre/bin/java
[info] # VM options: <none>
[info] # Warmup: 5 iterations, 1 s each
[info] # Measurement: 5 iterations, 1 s each
[info] # Timeout: 10 min per iteration
[info] # Threads: 1 thread, will synchronize iterations
[info] # Benchmark mode: Sampling time
[info] # Benchmark: net.ruippeixotog.jmh.ListSetBenchmark.createListSet1
[info]
[info] # Run progress: 8.33% complete, ETA 00:03:48
[info] # Fork: 1 of 2
[info] # Warmup Iteration 1: n = 23711, mean = 26075 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 334, 361, 1830, 5371, 30136, 86072, 641960, 578813952 ns/op
[info] # Warmup Iteration 2: n = 15508, mean = 606 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 330, 362, 672, 1263, 3808, 25549, 90140, 96768 ns/op
[info] # Warmup Iteration 3: n = 17904, mean = 655 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 329, 356, 1862, 2219, 3204, 16938, 58950, 98816 ns/op
[info] # Warmup Iteration 4: n = 10058, mean = 448 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 329, 355, 634, 711, 1181, 9864, 50175, 50304 ns/op
[info] # Warmup Iteration 5: n = 11383, mean = 396 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 329, 354, 420, 504, 777, 10791, 19252, 19296 ns/op
[info] Iteration 1: n = 11391, mean = 391 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 330, 353, 428, 533, 778, 3150, 17541, 17888 ns/op
[info] Iteration 2: n = 11162, mean = 402 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 329, 356, 449, 566, 846, 6550, 33027, 34560 ns/op
[info] Iteration 3: n = 11337, mean = 393 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 330, 357, 429, 519, 769, 1794, 41849, 43840 ns/op
[info] Iteration 4: n = 11434, mean = 394 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 330, 355, 419, 510, 722, 9492, 17257, 17312 ns/op
[info] Iteration 5: n = 11201, mean = 394 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 328, 354, 453, 530, 787, 2249, 20536, 20832 ns/op
[info]
[info] # Run progress: 12.50% complete, ETA 00:03:39
[info] # Fork: 2 of 2
[info] # Warmup Iteration 1: n = 27226, mean = 21925 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 309, 338, 1283, 4493, 29175, 75416, 1775152, 551550976 ns/op
[info] # Warmup Iteration 2: n = 19070, mean = 499 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 307, 338, 484, 1729, 2633, 14289, 32864, 60672 ns/op
[info] # Warmup Iteration 3: n = 10327, mean = 373 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 305, 330, 373, 445, 808, 10891, 25196, 25440 ns/op
[info] # Warmup Iteration 4: n = 11677, mean = 372 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 306, 332, 422, 543, 782, 2473, 23710, 23968 ns/op
[info] # Warmup Iteration 5: n = 12007, mean = 363 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 305, 331, 382, 445, 659, 6161, 24805, 26656 ns/op
[info] Iteration 1: n = 11657, mean = 379 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 305, 332, 435, 574, 923, 2337, 34862, 38528 ns/op
[info] Iteration 2: n = 11338, mean = 401 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 306, 331, 511, 628, 964, 2465, 182757, 208384 ns/op
[info] Iteration 3: n = 11727, mean = 368 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 307, 332, 418, 525, 764, 2456, 19631, 20256 ns/op
[info] Iteration 4: n = 11811, mean = 368 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 305, 332, 403, 482, 702, 3081, 25173, 26240 ns/op
[info] Iteration 5: n = 11922, mean = 363 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 306, 331, 390, 457, 664, 3259, 17611, 18048 ns/op
[info]
[info]
[info] Result "createListSet1":
[info] N = 114980
[info] mean = 385.070 ±(99.9%) 7.229 ns/op
[info]
[info] Histogram, ns/op:
[info] [ 0.000, 25000.000) = 114974
[info] [ 25000.000, 50000.000) = 5
[info] [ 50000.000, 75000.000) = 0
[info] [ 75000.000, 100000.000) = 0
[info] [100000.000, 125000.000) = 0
[info] [125000.000, 150000.000) = 0
[info] [150000.000, 175000.000) = 0
[info] [175000.000, 200000.000) = 0
[info] [200000.000, 225000.000) = 1
[info] [225000.000, 250000.000) = 0
[info] [250000.000, 275000.000) = 0
[info]
[info] Percentiles, ns/op:
[info] p(0.0000) = 305.000 ns/op
[info] p(50.0000) = 349.000 ns/op
[info] p(90.0000) = 426.000 ns/op
[info] p(95.0000) = 540.000 ns/op
[info] p(99.0000) = 823.190 ns/op
[info] p(99.9000) = 2532.912 ns/op
[info] p(99.9900) = 18544.669 ns/op
[info] p(99.9990) = 183733.663 ns/op
[info] p(99.9999) = 208384.000 ns/op
[info] p(100.0000) = 208384.000 ns/op
[info]
[info]
[info] # JMH 1.11.3 (released 115 days ago, please consider updating!)
[info] # VM version: JDK 1.8.0_25, VM 25.25-b02
[info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_25.jdk/Contents/Home/jre/bin/java
[info] # VM options: <none>
[info] # Warmup: 5 iterations, 1 s each
[info] # Measurement: 5 iterations, 1 s each
[info] # Timeout: 10 min per iteration
[info] # Threads: 1 thread, will synchronize iterations
[info] # Benchmark mode: Sampling time
[info] # Benchmark: net.ruippeixotog.jmh.ListSetBenchmark.createListSet2_distinct
[info]
[info] # Run progress: 16.67% complete, ETA 00:03:29
[info] # Fork: 1 of 2
[info] # Warmup Iteration 1: n = 34453, mean = 19807 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 365, 390, 1392, 3101, 29167, 109104, 1266812, 623902720 ns/op
[info] # Warmup Iteration 2: n = 32267, mean = 568 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 361, 386, 610, 1900, 2732, 13566, 39099, 40704 ns/op
[info] # Warmup Iteration 3: n = 17583, mean = 437 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 361, 384, 440, 553, 920, 10865, 28948, 53120 ns/op
[info] # Warmup Iteration 4: n = 19513, mean = 425 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 360, 382, 476, 581, 807, 2916, 42255, 47552 ns/op
[info] # Warmup Iteration 5: n = 10449, mean = 423 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 361, 381, 444, 564, 1079, 2469, 24400, 24576 ns/op
[info] Iteration 1: n = 10673, mean = 406 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 360, 381, 436, 500, 753, 1696, 15899, 16256 ns/op
[info] Iteration 2: n = 10458, mean = 422 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 360, 383, 458, 539, 816, 5347, 33701, 34496 ns/op
[info] Iteration 3: n = 10084, mean = 437 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 362, 385, 516, 667, 1095, 2682, 24194, 24256 ns/op
[info] Iteration 4: n = 10366, mean = 427 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 361, 383, 460, 577, 878, 8586, 29516, 29888 ns/op
[info] Iteration 5: n = 10437, mean = 420 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 361, 382, 450, 549, 856, 3367, 27307, 27552 ns/op
[info]
[info] # Run progress: 20.83% complete, ETA 00:03:19
[info] # Fork: 2 of 2
[info] # Warmup Iteration 1: n = 21036, mean = 29576 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 352, 390, 2124, 6961, 41576, 111991, 497641, 578813952 ns/op
[info] # Warmup Iteration 2: n = 16267, mean = 562 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 361, 385, 603, 1495, 3059, 16220, 53007, 67328 ns/op
[info] # Warmup Iteration 3: n = 15994, mean = 620 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 362, 386, 555, 714, 2052, 9809, 793422, 825344 ns/op
[info] # Warmup Iteration 4: n = 19155, mean = 451 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 361, 387, 639, 720, 1037, 2586, 18584, 32736 ns/op
[info] # Warmup Iteration 5: n = 10401, mean = 450 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 361, 385, 442, 583, 1124, 12552, 36666, 36800 ns/op
[info] Iteration 1: n = 9817, mean = 455 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 363, 390, 575, 700, 1032, 9788, 19456, 19456 ns/op
[info] Iteration 2: n = 7626, mean = 551 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 363, 476, 729, 771, 1012, 2523, 16896, 16896 ns/op
[info] Iteration 3: n = 8887, mean = 493 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 366, 407, 701, 750, 1045, 4106, 18272, 18272 ns/op
[info] Iteration 4: n = 10099, mean = 434 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 361, 389, 524, 657, 911, 3428, 15857, 15920 ns/op
[info] Iteration 5: n = 10406, mean = 428 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 361, 386, 462, 544, 818, 9959, 26008, 26240 ns/op
[info]
[info]
[info] Result "createListSet2_distinct":
[info] N = 98853
[info] mean = 443.781 ±(99.9%) 4.306 ns/op
[info]
[info] Histogram, ns/op:
[info] [ 0.000, 2500.000) = 98732
[info] [ 2500.000, 5000.000) = 36
[info] [ 5000.000, 7500.000) = 13
[info] [ 7500.000, 10000.000) = 26
[info] [10000.000, 12500.000) = 14
[info] [12500.000, 15000.000) = 7
[info] [15000.000, 17500.000) = 14
[info] [17500.000, 20000.000) = 4
[info] [20000.000, 22500.000) = 2
[info] [22500.000, 25000.000) = 1
[info] [25000.000, 27500.000) = 1
[info] [27500.000, 30000.000) = 2
[info] [30000.000, 32500.000) = 0
[info] [32500.000, 35000.000) = 1
[info] [35000.000, 37500.000) = 0
[info]
[info] Percentiles, ns/op:
[info] p(0.0000) = 360.000 ns/op
[info] p(50.0000) = 388.000 ns/op
[info] p(90.0000) = 589.000 ns/op
[info] p(95.0000) = 692.000 ns/op
[info] p(99.0000) = 925.000 ns/op
[info] p(99.9000) = 3305.840 ns/op
[info] p(99.9900) = 18407.686 ns/op
[info] p(99.9990) = 34496.000 ns/op
[info] p(99.9999) = 34496.000 ns/op
[info] p(100.0000) = 34496.000 ns/op
[info]
[info]
[info] # JMH 1.11.3 (released 115 days ago, please consider updating!)
[info] # VM version: JDK 1.8.0_25, VM 25.25-b02
[info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_25.jdk/Contents/Home/jre/bin/java
[info] # VM options: <none>
[info] # Warmup: 5 iterations, 1 s each
[info] # Measurement: 5 iterations, 1 s each
[info] # Timeout: 10 min per iteration
[info] # Threads: 1 thread, will synchronize iterations
[info] # Benchmark mode: Sampling time
[info] # Benchmark: net.ruippeixotog.jmh.ListSetBenchmark.createListSet2_eq
[info]
[info] # Run progress: 25.00% complete, ETA 00:03:08
[info] # Fork: 1 of 2
[info] # Warmup Iteration 1: n = 25527, mean = 23389 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 342, 371, 1888, 4957, 31511, 85091, 485424, 554696704 ns/op
[info] # Warmup Iteration 2: n = 18077, mean = 433 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 337, 365, 414, 525, 1758, 11491, 46111, 50816 ns/op
[info] # Warmup Iteration 3: n = 18793, mean = 410 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 340, 367, 415, 508, 701, 2302, 50945, 304128 ns/op
[info] # Warmup Iteration 4: n = 10440, mean = 479 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 341, 369, 677, 748, 1224, 12796, 43465, 44288 ns/op
[info] # Warmup Iteration 5: n = 11210, mean = 397 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 341, 368, 415, 470, 719, 3955, 21407, 21984 ns/op
[info] Iteration 1: n = 11181, mean = 402 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 342, 368, 419, 484, 769, 9452, 18584, 19104 ns/op
[info] Iteration 2: n = 11168, mean = 403 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 342, 368, 423, 495, 758, 2090, 70245, 76672 ns/op
[info] Iteration 3: n = 10619, mean = 423 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 341, 370, 522, 628, 884, 9126, 20101, 20256 ns/op
[info] Iteration 4: n = 11036, mean = 402 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 341, 368, 422, 502, 767, 3302, 25729, 26688 ns/op
[info] Iteration 5: n = 11120, mean = 395 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 342, 368, 421, 486, 713, 1721, 28445, 29536 ns/op
[info]
[info] # Run progress: 29.17% complete, ETA 00:02:58
[info] # Fork: 2 of 2
[info] # Warmup Iteration 1: n = 26391, mean = 22393 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 222, 369, 1200, 4203, 31178, 89565, 1907482, 545259520 ns/op
[info] # Warmup Iteration 2: n = 17952, mean = 669 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 338, 376, 1774, 2048, 3870, 18694, 68887, 98816 ns/op
[info] # Warmup Iteration 3: n = 19509, mean = 391 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 338, 362, 405, 462, 701, 3429, 18956, 33472 ns/op
[info] # Warmup Iteration 4: n = 10479, mean = 416 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 338, 362, 447, 604, 984, 9994, 37878, 38720 ns/op
[info] # Warmup Iteration 5: n = 11311, mean = 390 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 339, 362, 410, 469, 702, 1720, 18894, 19200 ns/op
[info] Iteration 1: n = 11340, mean = 398 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 337, 362, 411, 467, 748, 10371, 18751, 19008 ns/op
[info] Iteration 2: n = 11209, mean = 395 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 338, 363, 417, 477, 723, 3724, 14893, 15296 ns/op
[info] Iteration 3: n = 11153, mean = 397 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 338, 363, 417, 523, 780, 2648, 32555, 34880 ns/op
[info] Iteration 4: n = 11122, mean = 396 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 338, 363, 418, 515, 741, 2777, 23920, 24800 ns/op
[info] Iteration 5: n = 11150, mean = 403 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 338, 364, 423, 524, 761, 8186, 33252, 34176 ns/op
[info]
[info]
[info] Result "createListSet2_eq":
[info] N = 111098
[info] mean = 401.180 ±(99.9%) 4.617 ns/op
[info]
[info] Histogram, ns/op:
[info] [ 0.000, 5000.000) = 111002
[info] [ 5000.000, 10000.000) = 37
[info] [10000.000, 15000.000) = 36
[info] [15000.000, 20000.000) = 14
[info] [20000.000, 25000.000) = 3
[info] [25000.000, 30000.000) = 3
[info] [30000.000, 35000.000) = 2
[info] [35000.000, 40000.000) = 0
[info] [40000.000, 45000.000) = 0
[info] [45000.000, 50000.000) = 0
[info] [50000.000, 55000.000) = 0
[info] [55000.000, 60000.000) = 0
[info] [60000.000, 65000.000) = 0
[info] [65000.000, 70000.000) = 0
[info] [70000.000, 75000.000) = 0
[info]
[info] Percentiles, ns/op:
[info] p(0.0000) = 337.000 ns/op
[info] p(50.0000) = 366.000 ns/op
[info] p(90.0000) = 422.000 ns/op
[info] p(95.0000) = 520.000 ns/op
[info] p(99.0000) = 763.000 ns/op
[info] p(99.9000) = 2845.268 ns/op
[info] p(99.9900) = 19093.450 ns/op
[info] p(99.9990) = 72033.506 ns/op
[info] p(99.9999) = 76672.000 ns/op
[info] p(100.0000) = 76672.000 ns/op
[info]
[info]
[info] # JMH 1.11.3 (released 115 days ago, please consider updating!)
[info] # VM version: JDK 1.8.0_25, VM 25.25-b02
[info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_25.jdk/Contents/Home/jre/bin/java
[info] # VM options: <none>
[info] # Warmup: 5 iterations, 1 s each
[info] # Measurement: 5 iterations, 1 s each
[info] # Timeout: 10 min per iteration
[info] # Threads: 1 thread, will synchronize iterations
[info] # Benchmark mode: Sampling time
[info] # Benchmark: net.ruippeixotog.jmh.ListSetBenchmark.createListSet3_distinct
[info]
[info] # Run progress: 33.33% complete, ETA 00:02:47
[info] # Fork: 1 of 2
[info] # Warmup Iteration 1: n = 20599, mean = 28853 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 399, 429, 2064, 6416, 38016, 116070, 2321736, 550502400 ns/op
[info] # Warmup Iteration 2: n = 29619, mean = 576 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 389, 422, 723, 1784, 2464, 10522, 30938, 33856 ns/op
[info] # Warmup Iteration 3: n = 15563, mean = 610 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 389, 421, 680, 1870, 2464, 15935, 79230, 80512 ns/op
[info] # Warmup Iteration 4: n = 18037, mean = 477 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 390, 421, 602, 712, 982, 9661, 19656, 24672 ns/op
[info] # Warmup Iteration 5: n = 18874, mean = 457 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 388, 421, 474, 596, 887, 3026, 15808, 24896 ns/op
[info] Iteration 1: n = 18946, mean = 454 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 390, 421, 481, 564, 876, 2482, 16428, 20064 ns/op
[info] Iteration 2: n = 18795, mean = 463 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 389, 421, 483, 601, 901, 9236, 23631, 26080 ns/op
[info] Iteration 3: n = 18582, mean = 465 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 388, 422, 501, 655, 920, 2486, 21288, 22304 ns/op
[info] Iteration 4: n = 18658, mean = 462 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 389, 422, 495, 624, 901, 3652, 19609, 21632 ns/op
[info] Iteration 5: n = 18718, mean = 505 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 388, 422, 496, 614, 915, 6208, 137264, 776192 ns/op
[info]
[info] # Run progress: 37.50% complete, ETA 00:02:37
[info] # Fork: 2 of 2
[info] # Warmup Iteration 1: n = 23034, mean = 25367 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 388, 424, 1868, 5602, 37802, 105059, 1923702, 538968064 ns/op
[info] # Warmup Iteration 2: n = 15239, mean = 591 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 384, 418, 634, 1802, 2730, 14969, 73196, 111360 ns/op
[info] # Warmup Iteration 3: n = 16069, mean = 536 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 385, 419, 557, 1107, 2285, 6704, 36922, 39680 ns/op
[info] # Warmup Iteration 4: n = 17712, mean = 491 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 384, 420, 673, 749, 1064, 9609, 21260, 23136 ns/op
[info] # Warmup Iteration 5: n = 18524, mean = 467 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 385, 418, 493, 641, 987, 7998, 30344, 36864 ns/op
[info] Iteration 1: n = 19015, mean = 455 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 386, 417, 474, 559, 966, 4451, 19520, 21568 ns/op
[info] Iteration 2: n = 18384, mean = 468 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 385, 419, 546, 720, 969, 3349, 16286, 33472 ns/op
[info] Iteration 3: n = 18226, mean = 468 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 385, 419, 583, 718, 969, 2263, 15141, 15536 ns/op
[info] Iteration 4: n = 18013, mean = 482 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 385, 420, 621, 732, 1004, 5286, 38269, 69504 ns/op
[info] Iteration 5: n = 18283, mean = 471 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 386, 419, 575, 721, 906, 3908, 25073, 43072 ns/op
[info]
[info]
[info] Result "createListSet3_distinct":
[info] N = 185620
[info] mean = 469.036 ±(99.9%) 14.157 ns/op
[info]
[info] Histogram, ns/op:
[info] [ 0.000, 50000.000) = 185618
[info] [ 50000.000, 100000.000) = 1
[info] [100000.000, 150000.000) = 0
[info] [150000.000, 200000.000) = 0
[info] [200000.000, 250000.000) = 0
[info] [250000.000, 300000.000) = 0
[info] [300000.000, 350000.000) = 0
[info] [350000.000, 400000.000) = 0
[info] [400000.000, 450000.000) = 0
[info] [450000.000, 500000.000) = 0
[info] [500000.000, 550000.000) = 0
[info] [550000.000, 600000.000) = 0
[info] [600000.000, 650000.000) = 0
[info] [650000.000, 700000.000) = 0
[info] [700000.000, 750000.000) = 0
[info]
[info] Percentiles, ns/op:
[info] p(0.0000) = 385.000 ns/op
[info] p(50.0000) = 421.000 ns/op
[info] p(90.0000) = 511.000 ns/op
[info] p(95.0000) = 669.000 ns/op
[info] p(99.0000) = 935.000 ns/op
[info] p(99.9000) = 3411.708 ns/op
[info] p(99.9900) = 19426.090 ns/op
[info] p(99.9990) = 171118.668 ns/op
[info] p(99.9999) = 776192.000 ns/op
[info] p(100.0000) = 776192.000 ns/op
[info]
[info]
[info] # JMH 1.11.3 (released 115 days ago, please consider updating!)
[info] # VM version: JDK 1.8.0_25, VM 25.25-b02
[info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_25.jdk/Contents/Home/jre/bin/java
[info] # VM options: <none>
[info] # Warmup: 5 iterations, 1 s each
[info] # Measurement: 5 iterations, 1 s each
[info] # Timeout: 10 min per iteration
[info] # Threads: 1 thread, will synchronize iterations
[info] # Benchmark mode: Sampling time
[info] # Benchmark: net.ruippeixotog.jmh.ListSetBenchmark.createListSet3_eq
[info]
[info] # Run progress: 41.67% complete, ETA 00:02:26
[info] # Fork: 1 of 2
[info] # Warmup Iteration 1: n = 22179, mean = 27454 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 349, 380, 2006, 6392, 37261, 104801, 417992, 566231040 ns/op
[info] # Warmup Iteration 2: n = 16782, mean = 547 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 344, 374, 626, 1648, 2981, 14284, 45910, 60800 ns/op
[info] # Warmup Iteration 3: n = 17946, mean = 474 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 346, 375, 567, 688, 2208, 9889, 21569, 28384 ns/op
[info] # Warmup Iteration 4: n = 10602, mean = 434 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 348, 375, 471, 592, 983, 10867, 28285, 28544 ns/op
[info] # Warmup Iteration 5: n = 10531, mean = 441 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 348, 377, 571, 669, 855, 9916, 21790, 21856 ns/op
[info] Iteration 1: n = 10323, mean = 431 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 348, 376, 590, 678, 863, 1913, 42690, 43648 ns/op
[info] Iteration 2: n = 10683, mean = 409 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 348, 376, 449, 560, 747, 1836, 16481, 16832 ns/op
[info] Iteration 3: n = 10717, mean = 413 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 348, 376, 444, 582, 796, 4005, 15045, 15216 ns/op
[info] Iteration 4: n = 10410, mean = 430 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 349, 378, 515, 636, 865, 7696, 30657, 31232 ns/op
[info] Iteration 5: n = 10633, mean = 420 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 347, 376, 451, 589, 762, 9023, 18528, 18720 ns/op
[info]
[info] # Run progress: 45.83% complete, ETA 00:02:16
[info] # Fork: 2 of 2
[info] # Warmup Iteration 1: n = 21377, mean = 29399 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 350, 381, 2002, 6673, 33294, 94233, 548813, 589299712 ns/op
[info] # Warmup Iteration 2: n = 17116, mean = 760 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 344, 375, 603, 1858, 3183, 18212, 975383, 3244032 ns/op
[info] # Warmup Iteration 3: n = 17988, mean = 510 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 344, 375, 584, 1339, 2416, 8977, 35822, 50624 ns/op
[info] # Warmup Iteration 4: n = 19812, mean = 441 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 344, 373, 632, 695, 967, 7894, 27839, 31136 ns/op
[info] # Warmup Iteration 5: n = 10458, mean = 434 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 344, 372, 520, 611, 971, 9258, 63313, 64960 ns/op
[info] Iteration 1: n = 10520, mean = 419 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 343, 373, 504, 622, 891, 6006, 15593, 15696 ns/op
[info] Iteration 2: n = 10510, mean = 432 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 342, 375, 505, 623, 947, 9831, 34523, 35264 ns/op
[info] Iteration 3: n = 10806, mean = 410 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 343, 372, 442, 550, 779, 4175, 25216, 25792 ns/op
[info] Iteration 4: n = 10884, mean = 403 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 344, 373, 431, 504, 765, 2128, 21357, 22304 ns/op
[info] Iteration 5: n = 10497, mean = 413 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 344, 375, 464, 614, 890, 2162, 15397, 15456 ns/op
[info]
[info]
[info] Result "createListSet3_eq":
[info] N = 105983
[info] mean = 417.989 ±(99.9%) 4.127 ns/op
[info]
[info] Histogram, ns/op:
[info] [ 0.000, 5000.000) = 105890
[info] [ 5000.000, 10000.000) = 41
[info] [10000.000, 15000.000) = 30
[info] [15000.000, 20000.000) = 16
[info] [20000.000, 25000.000) = 2
[info] [25000.000, 30000.000) = 1
[info] [30000.000, 35000.000) = 1
[info] [35000.000, 40000.000) = 1
[info] [40000.000, 45000.000) = 1
[info]
[info] Percentiles, ns/op:
[info] p(0.0000) = 342.000 ns/op
[info] p(50.0000) = 375.000 ns/op
[info] p(90.0000) = 470.000 ns/op
[info] p(95.0000) = 606.000 ns/op
[info] p(99.0000) = 827.000 ns/op
[info] p(99.9000) = 3104.128 ns/op
[info] p(99.9900) = 17286.554 ns/op
[info] p(99.9990) = 43146.301 ns/op
[info] p(99.9999) = 43648.000 ns/op
[info] p(100.0000) = 43648.000 ns/op
[info]
[info]
[info] # JMH 1.11.3 (released 115 days ago, please consider updating!)
[info] # VM version: JDK 1.8.0_25, VM 25.25-b02
[info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_25.jdk/Contents/Home/jre/bin/java
[info] # VM options: <none>
[info] # Warmup: 5 iterations, 1 s each
[info] # Measurement: 5 iterations, 1 s each
[info] # Timeout: 10 min per iteration
[info] # Threads: 1 thread, will synchronize iterations
[info] # Benchmark mode: Sampling time
[info] # Benchmark: net.ruippeixotog.jmh.ListSetBenchmark.createListSet4_distinct
[info]
[info] # Run progress: 50.00% complete, ETA 00:02:05
[info] # Fork: 1 of 2
[info] # Warmup Iteration 1: n = 36814, mean = 16860 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 460, 498, 1377, 2825, 25659, 108894, 924823, 558891008 ns/op
[info] # Warmup Iteration 2: n = 24978, mean = 682 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 463, 499, 884, 1954, 2809, 11226, 43605, 85632 ns/op
[info] # Warmup Iteration 3: n = 12713, mean = 802 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 469, 517, 908, 982, 2279, 15811, 1214732, 1646592 ns/op
[info] # Warmup Iteration 4: n = 14407, mean = 606 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 467, 502, 599, 910, 2315, 11113, 28033, 30304 ns/op
[info] # Warmup Iteration 5: n = 15428, mean = 568 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 467, 503, 612, 760, 1012, 4799, 143162, 284160 ns/op
[info] Iteration 1: n = 14821, mean = 581 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 468, 505, 764, 856, 1196, 10563, 29887, 34624 ns/op
[info] Iteration 2: n = 14985, mean = 570 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 468, 504, 653, 880, 1106, 10961, 25460, 27040 ns/op
[info] Iteration 3: n = 14915, mean = 581 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 466, 506, 779, 903, 1144, 9783, 39931, 59328 ns/op
[info] Iteration 4: n = 15415, mean = 556 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 470, 504, 585, 786, 1085, 9048, 32223, 48064 ns/op
[info] Iteration 5: n = 15553, mean = 556 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 468, 504, 578, 729, 1090, 10100, 20672, 22432 ns/op
[info]
[info] # Run progress: 54.17% complete, ETA 00:01:55
[info] # Fork: 2 of 2
[info] # Warmup Iteration 1: n = 36329, mean = 16601 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 490, 521, 1392, 2706, 31216, 104534, 697898, 543162368 ns/op
[info] # Warmup Iteration 2: n = 24498, mean = 679 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 485, 515, 773, 1964, 2728, 11392, 39511, 49536 ns/op
[info] # Warmup Iteration 3: n = 15125, mean = 571 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 486, 515, 590, 725, 1163, 10048, 33836, 37248 ns/op
[info] # Warmup Iteration 4: n = 15205, mean = 552 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 486, 513, 581, 670, 1024, 7932, 19270, 21952 ns/op
[info] # Warmup Iteration 5: n = 14359, mean = 590 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 486, 517, 715, 893, 1266, 9715, 32088, 39552 ns/op
[info] Iteration 1: n = 14440, mean = 588 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 486, 517, 636, 853, 1182, 9945, 63677, 87808 ns/op
[info] Iteration 2: n = 14425, mean = 583 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 485, 517, 631, 851, 1251, 9548, 31980, 42560 ns/op
[info] Iteration 3: n = 14707, mean = 568 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 484, 516, 605, 811, 1170, 6977, 19521, 21600 ns/op
[info] Iteration 4: n = 14705, mean = 575 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 486, 516, 617, 820, 1184, 10466, 27783, 29696 ns/op
[info] Iteration 5: n = 14728, mean = 566 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 487, 516, 596, 810, 1171, 8239, 12895, 13296 ns/op
[info]
[info]
[info] Result "createListSet4_distinct":
[info] N = 148694
[info] mean = 572.208 ±(99.9%) 5.038 ns/op
[info]
[info] Histogram, ns/op:
[info] [ 0.000, 5000.000) = 148488
[info] [ 5000.000, 10000.000) = 82
[info] [10000.000, 15000.000) = 72
[info] [15000.000, 20000.000) = 33
[info] [20000.000, 25000.000) = 8
[info] [25000.000, 30000.000) = 4
[info] [30000.000, 35000.000) = 3
[info] [35000.000, 40000.000) = 0
[info] [40000.000, 45000.000) = 1
[info] [45000.000, 50000.000) = 1
[info] [50000.000, 55000.000) = 0
[info] [55000.000, 60000.000) = 1
[info] [60000.000, 65000.000) = 0
[info] [65000.000, 70000.000) = 0
[info] [70000.000, 75000.000) = 0
[info] [75000.000, 80000.000) = 0
[info] [80000.000, 85000.000) = 0
[info]
[info] Percentiles, ns/op:
[info] p(0.0000) = 466.000 ns/op
[info] p(50.0000) = 513.000 ns/op
[info] p(90.0000) = 640.000 ns/op
[info] p(95.0000) = 842.000 ns/op
[info] p(99.0000) = 1158.100 ns/op
[info] p(99.9000) = 9456.000 ns/op
[info] p(99.9900) = 21708.576 ns/op
[info] p(99.9990) = 73939.664 ns/op
[info] p(99.9999) = 87808.000 ns/op
[info] p(100.0000) = 87808.000 ns/op
[info]
[info]
[info] # JMH 1.11.3 (released 115 days ago, please consider updating!)
[info] # VM version: JDK 1.8.0_25, VM 25.25-b02
[info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_25.jdk/Contents/Home/jre/bin/java
[info] # VM options: <none>
[info] # Warmup: 5 iterations, 1 s each
[info] # Measurement: 5 iterations, 1 s each
[info] # Timeout: 10 min per iteration
[info] # Threads: 1 thread, will synchronize iterations
[info] # Benchmark mode: Sampling time
[info] # Benchmark: net.ruippeixotog.jmh.ListSetBenchmark.createListSet4_eq
[info]
[info] # Run progress: 58.33% complete, ETA 00:01:44
[info] # Fork: 1 of 2
[info] # Warmup Iteration 1: n = 24228, mean = 24659 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 362, 388, 1758, 5052, 30477, 114489, 515817, 555745280 ns/op
[info] # Warmup Iteration 2: n = 16817, mean = 543 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 359, 382, 568, 1678, 2715, 15306, 40815, 52160 ns/op
[info] # Warmup Iteration 3: n = 17047, mean = 510 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 360, 384, 571, 741, 2240, 7222, 90334, 176128 ns/op
[info] # Warmup Iteration 4: n = 18692, mean = 463 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 360, 383, 649, 733, 1130, 7536, 53365, 72448 ns/op
[info] # Warmup Iteration 5: n = 10345, mean = 440 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 358, 382, 450, 597, 1068, 11806, 84467, 86784 ns/op
[info] Iteration 1: n = 10450, mean = 425 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 359, 383, 448, 541, 882, 9702, 19208, 19296 ns/op
[info] Iteration 2: n = 10303, mean = 433 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 360, 384, 479, 626, 989, 5353, 26042, 26144 ns/op
[info] Iteration 3: n = 10413, mean = 426 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 360, 383, 467, 560, 849, 9072, 20832, 21088 ns/op
[info] Iteration 4: n = 10206, mean = 438 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 360, 385, 500, 638, 992, 2945, 69875, 70912 ns/op
[info] Iteration 5: n = 10205, mean = 430 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 360, 383, 529, 650, 945, 2048, 15770, 15808 ns/op
[info]
[info] # Run progress: 62.50% complete, ETA 00:01:34
[info] # Fork: 2 of 2
[info] # Warmup Iteration 1: n = 21671, mean = 27741 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 345, 380, 2112, 6771, 38461, 106434, 868214, 557842432 ns/op
[info] # Warmup Iteration 2: n = 16819, mean = 574 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 341, 371, 641, 1818, 2959, 17748, 74185, 139264 ns/op
[info] # Warmup Iteration 3: n = 18770, mean = 465 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 342, 369, 444, 651, 2229, 8393, 49193, 55424 ns/op
[info] # Warmup Iteration 4: n = 19978, mean = 434 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 341, 370, 609, 688, 977, 7259, 17239, 28288 ns/op
[info] # Warmup Iteration 5: n = 10905, mean = 410 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 342, 368, 428, 538, 917, 9978, 19230, 19584 ns/op
[info] Iteration 1: n = 10762, mean = 415 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 342, 370, 438, 551, 855, 9723, 30290, 31040 ns/op
[info] Iteration 2: n = 10939, mean = 404 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 342, 368, 426, 520, 789, 5047, 20913, 21376 ns/op
[info] Iteration 3: n = 10896, mean = 397 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 342, 370, 430, 508, 735, 1880, 14670, 15040 ns/op
[info] Iteration 4: n = 10762, mean = 412 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 342, 370, 442, 568, 796, 9276, 20211, 20480 ns/op
[info] Iteration 5: n = 10978, mean = 402 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 342, 369, 430, 528, 728, 2792, 27069, 28128 ns/op
[info]
[info]
[info] Result "createListSet4_eq":
[info] N = 105914
[info] mean = 417.861 ±(99.9%) 4.655 ns/op
[info]
[info] Histogram, ns/op:
[info] [ 0.000, 5000.000) = 105816
[info] [ 5000.000, 10000.000) = 40
[info] [10000.000, 15000.000) = 36
[info] [15000.000, 20000.000) = 12
[info] [20000.000, 25000.000) = 6
[info] [25000.000, 30000.000) = 2
[info] [30000.000, 35000.000) = 1
[info] [35000.000, 40000.000) = 0
[info] [40000.000, 45000.000) = 0
[info] [45000.000, 50000.000) = 0
[info] [50000.000, 55000.000) = 0
[info] [55000.000, 60000.000) = 0
[info] [60000.000, 65000.000) = 0
[info] [65000.000, 70000.000) = 0
[info] [70000.000, 75000.000) = 1
[info]
[info] Percentiles, ns/op:
[info] p(0.0000) = 342.000 ns/op
[info] p(50.0000) = 380.000 ns/op
[info] p(90.0000) = 454.000 ns/op
[info] p(95.0000) = 571.000 ns/op
[info] p(99.0000) = 849.850 ns/op
[info] p(99.9000) = 4469.440 ns/op
[info] p(99.9900) = 19779.664 ns/op
[info] p(99.9990) = 68553.571 ns/op
[info] p(99.9999) = 70912.000 ns/op
[info] p(100.0000) = 70912.000 ns/op
[info]
[info]
[info] # JMH 1.11.3 (released 115 days ago, please consider updating!)
[info] # VM version: JDK 1.8.0_25, VM 25.25-b02
[info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_25.jdk/Contents/Home/jre/bin/java
[info] # VM options: <none>
[info] # Warmup: 5 iterations, 1 s each
[info] # Measurement: 5 iterations, 1 s each
[info] # Timeout: 10 min per iteration
[info] # Threads: 1 thread, will synchronize iterations
[info] # Benchmark mode: Sampling time
[info] # Benchmark: net.ruippeixotog.jmh.ListSetBenchmark.createListSet5_distinct
[info]
[info] # Run progress: 66.67% complete, ETA 00:01:24
[info] # Fork: 1 of 2
[info] # Warmup Iteration 1: n = 35669, mean = 17274 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 510, 549, 1278, 2790, 26973, 97958, 1568276, 551550976 ns/op
[info] # Warmup Iteration 2: n = 22669, mean = 757 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 509, 551, 900, 2058, 2998, 15716, 52978, 55680 ns/op
[info] # Warmup Iteration 3: n = 14065, mean = 612 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 523, 554, 633, 787, 1172, 11378, 31702, 32288 ns/op
[info] # Warmup Iteration 4: n = 14071, mean = 604 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 524, 555, 641, 805, 1126, 9690, 20037, 21952 ns/op
[info] # Warmup Iteration 5: n = 14004, mean = 606 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 524, 555, 640, 789, 1128, 5156, 45771, 57536 ns/op
[info] Iteration 1: n = 13924, mean = 603 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 523, 555, 640, 832, 1160, 5748, 24661, 29760 ns/op
[info] Iteration 2: n = 14044, mean = 596 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 523, 554, 628, 714, 1133, 7638, 17443, 17728 ns/op
[info] Iteration 3: n = 14203, mean = 598 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 523, 553, 612, 702, 1118, 11120, 20844, 20992 ns/op
[info] Iteration 4: n = 14212, mean = 594 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 523, 552, 630, 713, 1084, 2923, 35306, 39040 ns/op
[info] Iteration 5: n = 13941, mean = 600 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 524, 556, 633, 809, 1123, 3949, 19507, 19936 ns/op
[info]
[info] # Run progress: 70.83% complete, ETA 00:01:13
[info] # Fork: 2 of 2
[info] # Warmup Iteration 1: n = 32265, mean = 19346 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 533, 565, 1857, 3575, 29836, 114748, 616842, 562036736 ns/op
[info] # Warmup Iteration 2: n = 22449, mean = 676 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 529, 560, 718, 1003, 2288, 12010, 51737, 89728 ns/op
[info] # Warmup Iteration 3: n = 13598, mean = 614 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 530, 561, 640, 782, 1274, 10103, 30240, 36160 ns/op
[info] # Warmup Iteration 4: n = 13792, mean = 606 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 530, 562, 642, 773, 1116, 9146, 16268, 16480 ns/op
[info] # Warmup Iteration 5: n = 13854, mean = 605 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 530, 561, 625, 758, 1145, 7090, 27127, 29952 ns/op
[info] Iteration 1: n = 13882, mean = 618 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 530, 560, 630, 752, 1157, 11202, 55484, 70656 ns/op
[info] Iteration 2: n = 13843, mean = 607 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 530, 561, 631, 764, 1147, 9292, 30774, 34624 ns/op
[info] Iteration 3: n = 13679, mean = 611 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 531, 562, 658, 807, 1124, 5750, 33060, 35392 ns/op
[info] Iteration 4: n = 13738, mean = 619 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 531, 562, 643, 779, 1129, 12038, 29279, 35584 ns/op
[info] Iteration 5: n = 13652, mean = 609 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 530, 562, 636, 809, 1158, 4913, 27523, 29440 ns/op
[info]
[info]
[info] Result "createListSet5_distinct":
[info] N = 139118
[info] mean = 605.334 ±(99.9%) 4.750 ns/op
[info]
[info] Histogram, ns/op:
[info] [ 0.000, 5000.000) = 138951
[info] [ 5000.000, 10000.000) = 44
[info] [10000.000, 15000.000) = 75
[info] [15000.000, 20000.000) = 33
[info] [20000.000, 25000.000) = 5
[info] [25000.000, 30000.000) = 3
[info] [30000.000, 35000.000) = 3
[info] [35000.000, 40000.000) = 3
[info] [40000.000, 45000.000) = 0
[info] [45000.000, 50000.000) = 0
[info] [50000.000, 55000.000) = 0
[info] [55000.000, 60000.000) = 0
[info] [60000.000, 65000.000) = 0
[info] [65000.000, 70000.000) = 0
[info] [70000.000, 75000.000) = 1
[info]
[info] Percentiles, ns/op:
[info] p(0.0000) = 523.000 ns/op
[info] p(50.0000) = 559.000 ns/op
[info] p(90.0000) = 635.000 ns/op
[info] p(95.0000) = 767.000 ns/op
[info] p(99.0000) = 1133.620 ns/op
[info] p(99.9000) = 9294.096 ns/op
[info] p(99.9900) = 21130.141 ns/op
[info] p(99.9990) = 58288.137 ns/op
[info] p(99.9999) = 70656.000 ns/op
[info] p(100.0000) = 70656.000 ns/op
[info]
[info]
[info] # JMH 1.11.3 (released 115 days ago, please consider updating!)
[info] # VM version: JDK 1.8.0_25, VM 25.25-b02
[info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_25.jdk/Contents/Home/jre/bin/java
[info] # VM options: <none>
[info] # Warmup: 5 iterations, 1 s each
[info] # Measurement: 5 iterations, 1 s each
[info] # Timeout: 10 min per iteration
[info] # Threads: 1 thread, will synchronize iterations
[info] # Benchmark mode: Sampling time
[info] # Benchmark: net.ruippeixotog.jmh.ListSetBenchmark.createListSet5_eq
[info]
[info] # Run progress: 75.00% complete, ETA 00:01:02
[info] # Fork: 1 of 2
[info] # Warmup Iteration 1: n = 24049, mean = 25031 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 370, 398, 1710, 4940, 32800, 117734, 2272625, 555745280 ns/op
[info] # Warmup Iteration 2: n = 16292, mean = 585 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 366, 391, 635, 1808, 2888, 20307, 61365, 72320 ns/op
[info] # Warmup Iteration 3: n = 17397, mean = 547 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 367, 394, 611, 1874, 2524, 9245, 34136, 55040 ns/op
[info] # Warmup Iteration 4: n = 17810, mean = 538 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 367, 396, 685, 740, 1046, 4827, 289774, 1110016 ns/op
[info] # Warmup Iteration 5: n = 10001, mean = 457 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 367, 393, 460, 611, 981, 11536, 29023, 29024 ns/op
[info] Iteration 1: n = 10037, mean = 440 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 369, 395, 464, 594, 851, 6124, 45710, 45824 ns/op
[info] Iteration 2: n = 10002, mean = 441 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 369, 396, 471, 614, 829, 8685, 20127, 20128 ns/op
[info] Iteration 3: n = 10105, mean = 439 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 370, 394, 464, 579, 889, 4881, 29835, 29856 ns/op
[info] Iteration 4: n = 10311, mean = 423 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 368, 392, 449, 511, 799, 2088, 21837, 22016 ns/op
[info] Iteration 5: n = 10057, mean = 437 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 369, 394, 482, 630, 909, 4326, 17680, 17696 ns/op
[info]
[info] # Run progress: 79.17% complete, ETA 00:00:52
[info] # Fork: 2 of 2
[info] # Warmup Iteration 1: n = 24472, mean = 24749 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 365, 393, 1649, 5275, 29594, 91065, 3431375, 557842432 ns/op
[info] # Warmup Iteration 2: n = 16361, mean = 556 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 363, 389, 595, 1730, 2814, 15170, 39192, 41024 ns/op
[info] # Warmup Iteration 3: n = 10328, mean = 440 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 364, 389, 446, 569, 977, 10652, 32213, 32640 ns/op
[info] # Warmup Iteration 4: n = 10312, mean = 435 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 366, 390, 458, 559, 873, 3379, 64415, 65536 ns/op
[info] # Warmup Iteration 5: n = 10477, mean = 420 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 366, 389, 440, 511, 836, 4737, 13003, 13040 ns/op
[info] Iteration 1: n = 10468, mean = 419 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 366, 388, 442, 502, 791, 8062, 12322, 12368 ns/op
[info] Iteration 2: n = 10450, mean = 424 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 367, 389, 447, 507, 783, 4485, 42759, 43968 ns/op
[info] Iteration 3: n = 10438, mean = 421 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 366, 389, 456, 529, 811, 3054, 11592, 11600 ns/op
[info] Iteration 4: n = 10314, mean = 420 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 365, 389, 461, 548, 799, 1784, 15816, 15904 ns/op
[info] Iteration 5: n = 10244, mean = 424 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 365, 389, 463, 550, 857, 3260, 12613, 12656 ns/op
[info]
[info]
[info] Result "createListSet5_eq":
[info] N = 102426
[info] mean = 428.659 ±(99.9%) 4.300 ns/op
[info]
[info] Histogram, ns/op:
[info] [ 0.000, 5000.000) = 102337
[info] [ 5000.000, 10000.000) = 34
[info] [10000.000, 15000.000) = 38
[info] [15000.000, 20000.000) = 10
[info] [20000.000, 25000.000) = 3
[info] [25000.000, 30000.000) = 2
[info] [30000.000, 35000.000) = 0
[info] [35000.000, 40000.000) = 0
[info] [40000.000, 45000.000) = 1
[info]
[info] Percentiles, ns/op:
[info] p(0.0000) = 365.000 ns/op
[info] p(50.0000) = 392.000 ns/op
[info] p(90.0000) = 458.000 ns/op
[info] p(95.0000) = 561.000 ns/op
[info] p(99.0000) = 827.730 ns/op
[info] p(99.9000) = 3092.176 ns/op
[info] p(99.9900) = 17418.570 ns/op
[info] p(99.9990) = 45778.955 ns/op
[info] p(99.9999) = 45824.000 ns/op
[info] p(100.0000) = 45824.000 ns/op
[info]
[info]
[info] # JMH 1.11.3 (released 115 days ago, please consider updating!)
[info] # VM version: JDK 1.8.0_25, VM 25.25-b02
[info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_25.jdk/Contents/Home/jre/bin/java
[info] # VM options: <none>
[info] # Warmup: 5 iterations, 1 s each
[info] # Measurement: 5 iterations, 1 s each
[info] # Timeout: 10 min per iteration
[info] # Threads: 1 thread, will synchronize iterations
[info] # Benchmark mode: Sampling time
[info] # Benchmark: net.ruippeixotog.jmh.ListSetBenchmark.createListSet6_distinct
[info]
[info] # Run progress: 83.33% complete, ETA 00:00:41
[info] # Fork: 1 of 2
[info] # Warmup Iteration 1: n = 28132, mean = 22839 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 575, 626, 2080, 4411, 29739, 115482, 2962004, 581959680 ns/op
[info] # Warmup Iteration 2: n = 20902, mean = 816 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 575, 613, 1000, 2124, 3112, 14267, 53705, 62848 ns/op
[info] # Warmup Iteration 3: n = 11537, mean = 795 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 575, 611, 911, 2108, 3145, 11589, 54898, 57280 ns/op
[info] # Warmup Iteration 4: n = 11300, mean = 656 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 575, 610, 703, 805, 1240, 3417, 20635, 21536 ns/op
[info] # Warmup Iteration 5: n = 12720, mean = 666 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 575, 608, 685, 811, 1310, 11165, 38510, 43456 ns/op
[info] Iteration 1: n = 12689, mean = 662 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 575, 609, 682, 804, 1250, 10052, 48070, 52288 ns/op
[info] Iteration 2: n = 12595, mean = 661 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 575, 610, 703, 813, 1264, 8017, 18660, 19424 ns/op
[info] Iteration 3: n = 11828, mean = 687 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 575, 612, 759, 1003, 1494, 10714, 23939, 23968 ns/op
[info] Iteration 4: n = 12257, mean = 685 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 575, 615, 744, 913, 1341, 11530, 26555, 26880 ns/op
[info] Iteration 5: n = 12623, mean = 661 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 576, 611, 704, 807, 1242, 9395, 20205, 21120 ns/op
[info]
[info] # Run progress: 87.50% complete, ETA 00:00:31
[info] # Fork: 2 of 2
[info] # Warmup Iteration 1: n = 30115, mean = 20321 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 577, 624, 2060, 3861, 29339, 128063, 622796, 550502400 ns/op
[info] # Warmup Iteration 2: n = 20702, mean = 854 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 575, 612, 1007, 2028, 3100, 19502, 154183, 368128 ns/op
[info] # Warmup Iteration 3: n = 12239, mean = 719 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 574, 610, 753, 993, 2578, 13231, 33702, 36992 ns/op
[info] # Warmup Iteration 4: n = 12743, mean = 655 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 575, 609, 705, 838, 1198, 7265, 16790, 17888 ns/op
[info] # Warmup Iteration 5: n = 12952, mean = 652 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 575, 608, 672, 743, 1181, 10049, 37721, 46528 ns/op
[info] Iteration 1: n = 12845, mean = 659 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 575, 611, 697, 789, 1180, 10289, 27187, 27296 ns/op
[info] Iteration 2: n = 12829, mean = 646 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 576, 609, 691, 781, 1145, 2718, 15270, 15392 ns/op
[info] Iteration 3: n = 12790, mean = 645 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 576, 611, 677, 754, 1206, 2422, 23658, 28512 ns/op
[info] Iteration 4: n = 12755, mean = 655 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 576, 610, 693, 791, 1225, 5768, 21242, 22944 ns/op
[info] Iteration 5: n = 12702, mean = 714 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 573, 610, 696, 786, 1232, 11284, 449457, 605184 ns/op
[info]
[info]
[info] Result "createListSet6_distinct":
[info] N = 125913
[info] mean = 667.359 ±(99.9%) 16.527 ns/op
[info]
[info] Histogram, ns/op:
[info] [ 0.000, 50000.000) = 125911
[info] [ 50000.000, 100000.000) = 1
[info] [100000.000, 150000.000) = 0
[info] [150000.000, 200000.000) = 0
[info] [200000.000, 250000.000) = 0
[info] [250000.000, 300000.000) = 0
[info] [300000.000, 350000.000) = 0
[info] [350000.000, 400000.000) = 0
[info] [400000.000, 450000.000) = 0
[info] [450000.000, 500000.000) = 0
[info] [500000.000, 550000.000) = 0
[info] [550000.000, 600000.000) = 0
[info] [600000.000, 650000.000) = 1
[info]
[info] Percentiles, ns/op:
[info] p(0.0000) = 573.000 ns/op
[info] p(50.0000) = 611.000 ns/op
[info] p(90.0000) = 699.000 ns/op
[info] p(95.0000) = 823.000 ns/op
[info] p(99.0000) = 1258.000 ns/op
[info] p(99.9000) = 9601.376 ns/op
[info] p(99.9900) = 25596.902 ns/op
[info] p(99.9990) = 461906.531 ns/op
[info] p(99.9999) = 605184.000 ns/op
[info] p(100.0000) = 605184.000 ns/op
[info]
[info]
[info] # JMH 1.11.3 (released 115 days ago, please consider updating!)
[info] # VM version: JDK 1.8.0_25, VM 25.25-b02
[info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_25.jdk/Contents/Home/jre/bin/java
[info] # VM options: <none>
[info] # Warmup: 5 iterations, 1 s each
[info] # Measurement: 5 iterations, 1 s each
[info] # Timeout: 10 min per iteration
[info] # Threads: 1 thread, will synchronize iterations
[info] # Benchmark mode: Sampling time
[info] # Benchmark: net.ruippeixotog.jmh.ListSetBenchmark.createListSet6_eq
[info]
[info] # Run progress: 91.67% complete, ETA 00:00:20
[info] # Fork: 1 of 2
[info] # Warmup Iteration 1: n = 23980, mean = 24598 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 376, 405, 1680, 5704, 34380, 125959, 861338, 544210944 ns/op
[info] # Warmup Iteration 2: n = 15702, mean = 607 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 373, 402, 682, 1830, 3144, 18730, 65784, 89472 ns/op
[info] # Warmup Iteration 3: n = 17398, mean = 485 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 373, 400, 481, 645, 2244, 9639, 42123, 45248 ns/op
[info] # Warmup Iteration 4: n = 18580, mean = 459 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 374, 400, 461, 626, 1280, 9403, 39761, 82048 ns/op
[info] # Warmup Iteration 5: n = 10190, mean = 446 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 373, 399, 453, 525, 960, 10903, 45463, 46016 ns/op
[info] Iteration 1: n = 10039, mean = 436 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 374, 400, 479, 587, 823, 3327, 11901, 11904 ns/op
[info] Iteration 2: n = 10069, mean = 435 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 375, 400, 459, 541, 803, 5219, 25743, 25792 ns/op
[info] Iteration 3: n = 10083, mean = 434 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 372, 402, 458, 535, 863, 4021, 15522, 15536 ns/op
[info] Iteration 4: n = 9997, mean = 433 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 374, 401, 461, 549, 805, 2006, 17536, 17536 ns/op
[info] Iteration 5: n = 9913, mean = 442 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 373, 402, 478, 581, 835, 8769, 20800, 20800 ns/op
[info]
[info] # Run progress: 95.83% complete, ETA 00:00:10
[info] # Fork: 2 of 2
[info] # Warmup Iteration 1: n = 23655, mean = 25978 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 374, 401, 1839, 5650, 37809, 117669, 3569848, 560988160 ns/op
[info] # Warmup Iteration 2: n = 16072, mean = 546 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 371, 396, 578, 1269, 2577, 12970, 60746, 65216 ns/op
[info] # Warmup Iteration 3: n = 17364, mean = 455 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 371, 397, 462, 622, 1845, 7567, 39058, 49664 ns/op
[info] # Warmup Iteration 4: n = 10171, mean = 439 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 371, 395, 446, 578, 1185, 7745, 17073, 17088 ns/op
[info] # Warmup Iteration 5: n = 10301, mean = 423 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 370, 394, 447, 502, 750, 1983, 25809, 26016 ns/op
[info] Iteration 1: n = 9953, mean = 450 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 372, 397, 479, 634, 909, 10607, 26656, 26656 ns/op
[info] Iteration 2: n = 9836, mean = 437 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 371, 396, 451, 514, 842, 10810, 23008, 23008 ns/op
[info] Iteration 3: n = 10296, mean = 429 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 371, 396, 450, 502, 746, 9898, 20341, 20416 ns/op
[info] Iteration 4: n = 10125, mean = 430 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 373, 398, 466, 558, 822, 1616, 14604, 14640 ns/op
[info] Iteration 5: n = 9916, mean = 454 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 371, 397, 484, 643, 921, 3130, 136704, 136704 ns/op
[info]
[info]
[info] Result "createListSet6_eq":
[info] N = 100227
[info] mean = 437.871 ±(99.9%) 6.177 ns/op
[info]
[info] Histogram, ns/op:
[info] [ 0.000, 12500.000) = 100196
[info] [ 12500.000, 25000.000) = 26
[info] [ 25000.000, 37500.000) = 4
[info] [ 37500.000, 50000.000) = 0
[info] [ 50000.000, 62500.000) = 0
[info] [ 62500.000, 75000.000) = 0
[info] [ 75000.000, 87500.000) = 0
[info] [ 87500.000, 100000.000) = 0
[info] [100000.000, 112500.000) = 0
[info] [112500.000, 125000.000) = 0
[info] [125000.000, 137500.000) = 1
[info] [137500.000, 150000.000) = 0
[info] [150000.000, 162500.000) = 0
[info] [162500.000, 175000.000) = 0
[info] [175000.000, 187500.000) = 0
[info]
[info] Percentiles, ns/op:
[info] p(0.0000) = 371.000 ns/op
[info] p(50.0000) = 399.000 ns/op
[info] p(90.0000) = 463.000 ns/op
[info] p(95.0000) = 566.000 ns/op
[info] p(99.0000) = 825.000 ns/op
[info] p(99.9000) = 6051.840 ns/op
[info] p(99.9900) = 18809.434 ns/op
[info] p(99.9990) = 136453.091 ns/op
[info] p(99.9999) = 136704.000 ns/op
[info] p(100.0000) = 136704.000 ns/op
[info]
[info]
[info] # Run complete. Total time: 00:04:11
[info]
[info] Benchmark Mode Cnt Score Error Units
[info] ListSetBenchmark.createListSet0 sample 134877 64.911 ± 1.543 ns/op
[info] ListSetBenchmark.createListSet1 sample 114980 385.070 ± 7.229 ns/op
[info] ListSetBenchmark.createListSet2_distinct sample 98853 443.781 ± 4.306 ns/op
[info] ListSetBenchmark.createListSet2_eq sample 111098 401.180 ± 4.617 ns/op
[info] ListSetBenchmark.createListSet3_distinct sample 185620 469.036 ± 14.157 ns/op
[info] ListSetBenchmark.createListSet3_eq sample 105983 417.989 ± 4.127 ns/op
[info] ListSetBenchmark.createListSet4_distinct sample 148694 572.208 ± 5.038 ns/op
[info] ListSetBenchmark.createListSet4_eq sample 105914 417.861 ± 4.655 ns/op
[info] ListSetBenchmark.createListSet5_distinct sample 139118 605.334 ± 4.750 ns/op
[info] ListSetBenchmark.createListSet5_eq sample 102426 428.659 ± 4.300 ns/op
[info] ListSetBenchmark.createListSet6_distinct sample 125913 667.359 ± 16.527 ns/op
[info] ListSetBenchmark.createListSet6_eq sample 100227 437.871 ± 6.177 ns/op
Raw
listset_default_builder_results.txt
[info] Running org.openjdk.jmh.Main -i 5 -wi 5 -f2 -t1 .*ListSetBenchmark
[info] # JMH 1.11.3 (released 115 days ago, please consider updating!)
[info] # VM version: JDK 1.8.0_25, VM 25.25-b02
[info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_25.jdk/Contents/Home/jre/bin/java
[info] # VM options: <none>
[info] # Warmup: 5 iterations, 1 s each
[info] # Measurement: 5 iterations, 1 s each
[info] # Timeout: 10 min per iteration
[info] # Threads: 1 thread, will synchronize iterations
[info] # Benchmark mode: Sampling time
[info] # Benchmark: net.ruippeixotog.jmh.ListSetBenchmark.createListSet0
[info]
[info] # Run progress: 0.00% complete, ETA 00:04:00
[info] # Fork: 1 of 2
[info] # Warmup Iteration 1: n = 30751, mean = 8120 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 19, 45, 57, 70, 518, 7740, 24293, 247463936 ns/op
[info] # Warmup Iteration 2: n = 21478, mean = 53 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 37, 47, 58, 67, 118, 434, 9256, 10640 ns/op
[info] # Warmup Iteration 3: n = 11112, mean = 53 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 39, 46, 59, 69, 133, 630, 8989, 9264 ns/op
[info] # Warmup Iteration 4: n = 10143, mean = 64 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 43, 57, 71, 85, 152, 442, 10654, 10672 ns/op
[info] # Warmup Iteration 5: n = 10387, mean = 61 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 40, 56, 65, 78, 145, 426, 10818, 11200 ns/op
[info] Iteration 1: n = 10335, mean = 61 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 40, 56, 66, 79, 159, 640, 8646, 8896 ns/op
[info] Iteration 2: n = 10347, mean = 60 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 40, 56, 66, 78, 128, 329, 9203, 9488 ns/op
[info] Iteration 3: n = 10342, mean = 65 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 40, 57, 69, 80, 166, 762, 15769, 15952 ns/op
[info] Iteration 4: n = 10421, mean = 61 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 40, 57, 65, 75, 142, 542, 9117, 9136 ns/op
[info] Iteration 5: n = 10473, mean = 62 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 40, 56, 64, 73, 139, 507, 10924, 11008 ns/op
[info]
[info] # Run progress: 4.17% complete, ETA 00:03:59
[info] # Fork: 2 of 2
[info] # Warmup Iteration 1: n = 34382, mean = 6650 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 3, 42, 55, 58, 340, 5953, 23725, 226492416 ns/op
[info] # Warmup Iteration 2: n = 22046, mean = 45 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 29, 40, 49, 57, 99, 529, 2360, 10528 ns/op
[info] # Warmup Iteration 3: n = 19333, mean = 61 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 41, 56, 61, 71, 122, 468, 14610, 14864 ns/op
[info] # Warmup Iteration 4: n = 18924, mean = 60 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 41, 57, 65, 82, 131, 368, 1759, 2928 ns/op
[info] # Warmup Iteration 5: n = 18569, mean = 63 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 41, 57, 72, 96, 147, 569, 2518, 5440 ns/op
[info] Iteration 1: n = 18999, mean = 62 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 41, 57, 66, 83, 150, 432, 11766, 14128 ns/op
[info] Iteration 2: n = 18635, mean = 61 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 41, 57, 66, 80, 123, 350, 9900, 10384 ns/op
[info] Iteration 3: n = 18772, mean = 64 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 41, 57, 67, 82, 141, 612, 16487, 22144 ns/op
[info] Iteration 4: n = 19051, mean = 61 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 40, 57, 64, 76, 141, 635, 7973, 13216 ns/op
[info] Iteration 5: n = 19220, mean = 62 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 40, 56, 62, 71, 136, 542, 12723, 37184 ns/op
[info]
[info]
[info] Result "createListSet0":
[info] N = 146595
[info] mean = 62.053 ±(99.9%) 1.513 ns/op
[info]
[info] Histogram, ns/op:
[info] [ 0.000, 2500.000) = 146570
[info] [ 2500.000, 5000.000) = 0
[info] [ 5000.000, 7500.000) = 2
[info] [ 7500.000, 10000.000) = 11
[info] [10000.000, 12500.000) = 6
[info] [12500.000, 15000.000) = 2
[info] [15000.000, 17500.000) = 2
[info] [17500.000, 20000.000) = 0
[info] [20000.000, 22500.000) = 1
[info] [22500.000, 25000.000) = 0
[info] [25000.000, 27500.000) = 0
[info] [27500.000, 30000.000) = 0
[info] [30000.000, 32500.000) = 0
[info] [32500.000, 35000.000) = 0
[info] [35000.000, 37500.000) = 1
[info]
[info] Percentiles, ns/op:
[info] p(0.0000) = 40.000 ns/op
[info] p(50.0000) = 57.000 ns/op
[info] p(90.0000) = 65.000 ns/op
[info] p(95.0000) = 79.000 ns/op
[info] p(99.0000) = 141.000 ns/op
[info] p(99.9000) = 538.808 ns/op
[info] p(99.9900) = 9466.893 ns/op
[info] p(99.9990) = 30175.962 ns/op
[info] p(99.9999) = 37184.000 ns/op
[info] p(100.0000) = 37184.000 ns/op
[info]
[info]
[info] # JMH 1.11.3 (released 115 days ago, please consider updating!)
[info] # VM version: JDK 1.8.0_25, VM 25.25-b02
[info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_25.jdk/Contents/Home/jre/bin/java
[info] # VM options: <none>
[info] # Warmup: 5 iterations, 1 s each
[info] # Measurement: 5 iterations, 1 s each
[info] # Timeout: 10 min per iteration
[info] # Threads: 1 thread, will synchronize iterations
[info] # Benchmark mode: Sampling time
[info] # Benchmark: net.ruippeixotog.jmh.ListSetBenchmark.createListSet1
[info]
[info] # Run progress: 8.33% complete, ETA 00:03:48
[info] # Fork: 1 of 2
[info] # Warmup Iteration 1: n = 25235, mean = 20142 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 84, 92, 136, 842, 5781, 24203, 463862, 499122176 ns/op
[info] # Warmup Iteration 2: n = 23897, mean = 129 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 86, 103, 129, 159, 1330, 2268, 11304, 17856 ns/op
[info] # Warmup Iteration 3: n = 14912, mean = 115 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 87, 105, 119, 145, 212, 891, 12478, 12816 ns/op
[info] # Warmup Iteration 4: n = 14845, mean = 114 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 85, 105, 128, 153, 211, 611, 12756, 14400 ns/op
[info] # Warmup Iteration 5: n = 13599, mean = 133 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 90, 106, 169, 194, 288, 1137, 31730, 34944 ns/op
[info] Iteration 1: n = 13542, mean = 153 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 87, 106, 174, 203, 328, 1085, 196045, 287744 ns/op
[info] Iteration 2: n = 14876, mean = 116 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 87, 105, 120, 145, 211, 830, 26565, 34368 ns/op
[info] Iteration 3: n = 15016, mean = 112 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 87, 105, 116, 136, 201, 640, 10760, 10864 ns/op
[info] Iteration 4: n = 14970, mean = 110 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 86, 105, 119, 142, 201, 503, 8977, 9152 ns/op
[info] Iteration 5: n = 14925, mean = 115 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 86, 104, 116, 136, 207, 792, 33415, 49856 ns/op
[info]
[info] # Run progress: 12.50% complete, ETA 00:03:39
[info] # Fork: 2 of 2
[info] # Warmup Iteration 1: n = 38409, mean = 14993 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 40, 93, 145, 595, 7399, 44541, 281064, 558891008 ns/op
[info] # Warmup Iteration 2: n = 23836, mean = 150 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 86, 99, 141, 172, 1613, 8410, 35636, 38336 ns/op
[info] # Warmup Iteration 3: n = 14027, mean = 113 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 85, 100, 118, 144, 238, 895, 36198, 49088 ns/op
[info] # Warmup Iteration 4: n = 14987, mean = 108 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 85, 100, 119, 141, 202, 610, 12422, 14976 ns/op
[info] # Warmup Iteration 5: n = 14806, mean = 110 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 86, 100, 120, 147, 211, 626, 12288, 13472 ns/op
[info] Iteration 1: n = 14919, mean = 110 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 86, 100, 117, 144, 218, 543, 15016, 16480 ns/op
[info] Iteration 2: n = 14867, mean = 111 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 85, 100, 121, 149, 218, 635, 15881, 17376 ns/op
[info] Iteration 3: n = 14883, mean = 111 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 86, 101, 119, 143, 214, 731, 14084, 14928 ns/op
[info] Iteration 4: n = 15031, mean = 108 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 86, 100, 113, 129, 210, 753, 16076, 17920 ns/op
[info] Iteration 5: n = 14884, mean = 107 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 86, 100, 116, 140, 204, 659, 8097, 12624 ns/op
[info]
[info]
[info] Result "createListSet1":
[info] N = 147913
[info] mean = 114.764 ±(99.9%) 6.772 ns/op
[info]
[info] Histogram, ns/op:
[info] [ 0.000, 25000.000) = 147909
[info] [ 25000.000, 50000.000) = 3
[info] [ 50000.000, 75000.000) = 0
[info] [ 75000.000, 100000.000) = 0
[info] [100000.000, 125000.000) = 0
[info] [125000.000, 150000.000) = 0
[info] [150000.000, 175000.000) = 0
[info] [175000.000, 200000.000) = 0
[info] [200000.000, 225000.000) = 0
[info] [225000.000, 250000.000) = 0
[info] [250000.000, 275000.000) = 0
[info]
[info] Percentiles, ns/op:
[info] p(0.0000) = 85.000 ns/op
[info] p(50.0000) = 103.000 ns/op
[info] p(90.0000) = 121.000 ns/op
[info] p(95.0000) = 152.000 ns/op
[info] p(99.0000) = 224.000 ns/op
[info] p(99.9000) = 727.258 ns/op
[info] p(99.9900) = 13825.478 ns/op
[info] p(99.9990) = 173762.344 ns/op
[info] p(99.9999) = 287744.000 ns/op
[info] p(100.0000) = 287744.000 ns/op
[info]
[info]
[info] # JMH 1.11.3 (released 115 days ago, please consider updating!)
[info] # VM version: JDK 1.8.0_25, VM 25.25-b02
[info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_25.jdk/Contents/Home/jre/bin/java
[info] # VM options: <none>
[info] # Warmup: 5 iterations, 1 s each
[info] # Measurement: 5 iterations, 1 s each
[info] # Timeout: 10 min per iteration
[info] # Threads: 1 thread, will synchronize iterations
[info] # Benchmark mode: Sampling time
[info] # Benchmark: net.ruippeixotog.jmh.ListSetBenchmark.createListSet2_distinct
[info]
[info] # Run progress: 16.67% complete, ETA 00:03:28
[info] # Fork: 1 of 2
[info] # Warmup Iteration 1: n = 22070, mean = 22850 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 94, 107, 341, 1889, 9685, 41770, 225866, 492306432 ns/op
[info] # Warmup Iteration 2: n = 21121, mean = 121 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 95, 108, 123, 145, 220, 1068, 18388, 20384 ns/op
[info] # Warmup Iteration 3: n = 12663, mean = 131 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 97, 120, 134, 156, 241, 1016, 14406, 15872 ns/op
[info] # Warmup Iteration 4: n = 12734, mean = 128 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 97, 119, 133, 156, 241, 627, 14149, 15296 ns/op
[info] # Warmup Iteration 5: n = 12541, mean = 130 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 96, 120, 139, 173, 249, 803, 12912, 14752 ns/op
[info] Iteration 1: n = 12571, mean = 129 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 96, 120, 133, 161, 245, 629, 12477, 12720 ns/op
[info] Iteration 2: n = 11853, mean = 148 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 95, 120, 175, 211, 319, 1399, 36098, 36608 ns/op
[info] Iteration 3: n = 11837, mean = 137 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 96, 121, 170, 201, 291, 874, 8661, 8912 ns/op
[info] Iteration 4: n = 12354, mean = 131 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 96, 120, 141, 172, 253, 811, 16202, 17920 ns/op
[info] Iteration 5: n = 12234, mean = 134 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 96, 120, 149, 177, 263, 858, 18089, 20256 ns/op
[info]
[info] # Run progress: 20.83% complete, ETA 00:03:18
[info] # Fork: 2 of 2
[info] # Warmup Iteration 1: n = 37594, mean = 13685 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 101, 110, 176, 872, 7624, 37762, 176451, 497549312 ns/op
[info] # Warmup Iteration 2: n = 19842, mean = 121 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 94, 109, 126, 167, 256, 1112, 9018, 9648 ns/op
[info] # Warmup Iteration 3: n = 12558, mean = 133 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 97, 115, 130, 153, 255, 2406, 20694, 21472 ns/op
[info] # Warmup Iteration 4: n = 12660, mean = 124 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 97, 115, 132, 157, 230, 838, 11650, 12416 ns/op
[info] # Warmup Iteration 5: n = 12563, mean = 124 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 97, 116, 131, 157, 244, 778, 13426, 16800 ns/op
[info] Iteration 1: n = 12422, mean = 127 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 96, 116, 132, 165, 273, 979, 10209, 10608 ns/op
[info] Iteration 2: n = 12380, mean = 126 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 96, 115, 131, 167, 249, 925, 10503, 10800 ns/op
[info] Iteration 3: n = 12488, mean = 130 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 96, 115, 132, 159, 264, 909, 40860, 49984 ns/op
[info] Iteration 4: n = 12519, mean = 125 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 95, 115, 133, 152, 230, 603, 15647, 17344 ns/op
[info] Iteration 5: n = 12571, mean = 124 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 96, 115, 128, 149, 249, 876, 16206, 17984 ns/op
[info]
[info]
[info] Result "createListSet2_distinct":
[info] N = 123229
[info] mean = 130.837 ±(99.9%) 2.646 ns/op
[info]
[info] Histogram, ns/op:
[info] [ 0.000, 5000.000) = 123191
[info] [ 5000.000, 10000.000) = 16
[info] [10000.000, 15000.000) = 14
[info] [15000.000, 20000.000) = 4
[info] [20000.000, 25000.000) = 1
[info] [25000.000, 30000.000) = 0
[info] [30000.000, 35000.000) = 1
[info] [35000.000, 40000.000) = 1
[info] [40000.000, 45000.000) = 0
[info]
[info] Percentiles, ns/op:
[info] p(0.0000) = 95.000 ns/op
[info] p(50.0000) = 118.000 ns/op
[info] p(90.0000) = 140.000 ns/op
[info] p(95.0000) = 175.000 ns/op
[info] p(99.0000) = 266.000 ns/op
[info] p(99.9000) = 860.000 ns/op
[info] p(99.9900) = 12415.088 ns/op
[info] p(99.9990) = 46876.755 ns/op
[info] p(99.9999) = 49984.000 ns/op
[info] p(100.0000) = 49984.000 ns/op
[info]
[info]
[info] # JMH 1.11.3 (released 115 days ago, please consider updating!)
[info] # VM version: JDK 1.8.0_25, VM 25.25-b02
[info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_25.jdk/Contents/Home/jre/bin/java
[info] # VM options: <none>
[info] # Warmup: 5 iterations, 1 s each
[info] # Measurement: 5 iterations, 1 s each
[info] # Timeout: 10 min per iteration
[info] # Threads: 1 thread, will synchronize iterations
[info] # Benchmark mode: Sampling time
[info] # Benchmark: net.ruippeixotog.jmh.ListSetBenchmark.createListSet2_eq
[info]
[info] # Run progress: 25.00% complete, ETA 00:03:08
[info] # Fork: 1 of 2
[info] # Warmup Iteration 1: n = 23341, mean = 21462 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 39, 103, 176, 1536, 8460, 36915, 670140, 489684992 ns/op
[info] # Warmup Iteration 2: n = 22792, mean = 115 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 91, 105, 122, 148, 229, 945, 15233, 37824 ns/op
[info] # Warmup Iteration 3: n = 13262, mean = 141 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 92, 107, 134, 162, 1497, 2783, 19001, 19168 ns/op
[info] # Warmup Iteration 4: n = 13770, mean = 114 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 92, 106, 127, 155, 224, 514, 6036, 8768 ns/op
[info] # Warmup Iteration 5: n = 13573, mean = 117 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 92, 106, 130, 167, 250, 906, 10323, 10672 ns/op
[info] Iteration 1: n = 13597, mean = 115 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 92, 106, 128, 160, 230, 530, 11998, 13760 ns/op
[info] Iteration 2: n = 13580, mean = 116 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 92, 107, 130, 158, 225, 459, 12730, 14512 ns/op
[info] Iteration 3: n = 13757, mean = 115 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 92, 107, 122, 144, 225, 607, 10894, 11760 ns/op
[info] Iteration 4: n = 13740, mean = 114 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 92, 106, 125, 145, 214, 558, 11837, 17248 ns/op
[info] Iteration 5: n = 13813, mean = 118 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 92, 106, 121, 141, 220, 938, 12199, 13120 ns/op
[info]
[info] # Run progress: 29.17% complete, ETA 00:02:57
[info] # Fork: 2 of 2
[info] # Warmup Iteration 1: n = 23511, mean = 21404 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 48, 105, 180, 1723, 9278, 33104, 105808, 491782144 ns/op
[info] # Warmup Iteration 2: n = 22987, mean = 163 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 91, 105, 137, 183, 1708, 2764, 17194, 40832 ns/op
[info] # Warmup Iteration 3: n = 12752, mean = 128 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 91, 107, 151, 188, 308, 1202, 15603, 16480 ns/op
[info] # Warmup Iteration 4: n = 12446, mean = 141 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 93, 116, 191, 226, 345, 1341, 13758, 15328 ns/op
[info] # Warmup Iteration 5: n = 13334, mean = 131 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 93, 113, 145, 172, 264, 1141, 35211, 45952 ns/op
[info] Iteration 1: n = 13656, mean = 122 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 93, 113, 134, 164, 233, 578, 9830, 10064 ns/op
[info] Iteration 2: n = 12802, mean = 134 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 92, 114, 174, 204, 300, 879, 18099, 18144 ns/op
[info] Iteration 3: n = 13277, mean = 129 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 93, 114, 148, 178, 262, 923, 14504, 15904 ns/op
[info] Iteration 4: n = 13710, mean = 122 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 93, 113, 131, 154, 222, 698, 14610, 17056 ns/op
[info] Iteration 5: n = 13830, mean = 121 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 92, 113, 127, 147, 218, 467, 13358, 13744 ns/op
[info]
[info]
[info] Result "createListSet2_eq":
[info] N = 135762
[info] mean = 120.509 ±(99.9%) 1.630 ns/op
[info]
[info] Histogram, ns/op:
[info] [ 0.000, 1250.000) = 135707
[info] [ 1250.000, 2500.000) = 19
[info] [ 2500.000, 3750.000) = 4
[info] [ 3750.000, 5000.000) = 1
[info] [ 5000.000, 6250.000) = 0
[info] [ 6250.000, 7500.000) = 0
[info] [ 7500.000, 8750.000) = 3
[info] [ 8750.000, 10000.000) = 13
[info] [10000.000, 11250.000) = 3
[info] [11250.000, 12500.000) = 2
[info] [12500.000, 13750.000) = 3
[info] [13750.000, 15000.000) = 2
[info] [15000.000, 16250.000) = 1
[info] [16250.000, 17500.000) = 2
[info] [17500.000, 18750.000) = 2
[info]
[info] Percentiles, ns/op:
[info] p(0.0000) = 92.000 ns/op
[info] p(50.0000) = 112.000 ns/op
[info] p(90.0000) = 132.000 ns/op
[info] p(95.0000) = 164.000 ns/op
[info] p(99.0000) = 238.000 ns/op
[info] p(99.9000) = 699.081 ns/op
[info] p(99.9900) = 10565.688 ns/op
[info] p(99.9990) = 18086.779 ns/op
[info] p(99.9999) = 18144.000 ns/op
[info] p(100.0000) = 18144.000 ns/op
[info]
[info]
[info] # JMH 1.11.3 (released 115 days ago, please consider updating!)
[info] # VM version: JDK 1.8.0_25, VM 25.25-b02
[info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_25.jdk/Contents/Home/jre/bin/java
[info] # VM options: <none>
[info] # Warmup: 5 iterations, 1 s each
[info] # Measurement: 5 iterations, 1 s each
[info] # Timeout: 10 min per iteration
[info] # Threads: 1 thread, will synchronize iterations
[info] # Benchmark mode: Sampling time
[info] # Benchmark: net.ruippeixotog.jmh.ListSetBenchmark.createListSet3_distinct
[info]
[info] # Run progress: 33.33% complete, ETA 00:02:47
[info] # Fork: 1 of 2
[info] # Warmup Iteration 1: n = 36093, mean = 14034 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 102, 118, 175, 849, 8305, 31819, 90188, 491257856 ns/op
[info] # Warmup Iteration 2: n = 18012, mean = 134 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 102, 119, 140, 154, 259, 1222, 14633, 20992 ns/op
[info] # Warmup Iteration 3: n = 10868, mean = 138 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 103, 120, 142, 180, 288, 1272, 35573, 37568 ns/op
[info] # Warmup Iteration 4: n = 11097, mean = 129 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 103, 119, 140, 171, 272, 944, 8178, 8848 ns/op
[info] # Warmup Iteration 5: n = 10978, mean = 132 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 103, 120, 145, 182, 289, 781, 11208, 11376 ns/op
[info] Iteration 1: n = 10993, mean = 134 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 103, 120, 141, 192, 290, 795, 15831, 16416 ns/op
[info] Iteration 2: n = 10871, mean = 141 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 103, 121, 149, 193, 300, 1073, 53296, 57024 ns/op
[info] Iteration 3: n = 9733, mean = 159 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 103, 126, 232, 274, 401, 1794, 11200, 11200 ns/op
[info] Iteration 4: n = 10871, mean = 134 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 102, 121, 150, 189, 278, 899, 10109, 10112 ns/op
[info] Iteration 5: n = 10526, mean = 142 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 103, 121, 170, 210, 310, 1283, 15711, 15888 ns/op
[info]
[info] # Run progress: 37.50% complete, ETA 00:02:36
[info] # Fork: 2 of 2
[info] # Warmup Iteration 1: n = 29210, mean = 18227 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 96, 120, 259, 1751, 11838, 45807, 139892, 514850816 ns/op
[info] # Warmup Iteration 2: n = 17099, mean = 291 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 102, 119, 202, 1602, 2228, 13494, 50943, 58304 ns/op
[info] # Warmup Iteration 3: n = 18990, mean = 129 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 102, 116, 143, 169, 259, 774, 12638, 15472 ns/op
[info] # Warmup Iteration 4: n = 10937, mean = 139 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 103, 122, 144, 172, 276, 900, 23289, 24000 ns/op
[info] # Warmup Iteration 5: n = 10710, mean = 140 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 104, 124, 160, 197, 293, 1064, 16589, 16608 ns/op
[info] Iteration 1: n = 10723, mean = 138 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 103, 122, 160, 192, 281, 729, 25097, 26272 ns/op
[info] Iteration 2: n = 10685, mean = 135 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 104, 123, 166, 202, 287, 784, 1606, 1612 ns/op
[info] Iteration 3: n = 10250, mean = 148 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 104, 125, 201, 242, 354, 1217, 15048, 15136 ns/op
[info] Iteration 4: n = 10976, mean = 134 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 105, 122, 146, 182, 273, 921, 15143, 15840 ns/op
[info] Iteration 5: n = 11001, mean = 133 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 103, 121, 147, 175, 254, 631, 10589, 10704 ns/op
[info]
[info]
[info] Result "createListSet3_distinct":
[info] N = 106629
[info] mean = 139.546 ±(99.9%) 2.753 ns/op
[info]
[info] Histogram, ns/op:
[info] [ 0.000, 5000.000) = 106593
[info] [ 5000.000, 10000.000) = 18
[info] [10000.000, 15000.000) = 12
[info] [15000.000, 20000.000) = 4
[info] [20000.000, 25000.000) = 0
[info] [25000.000, 30000.000) = 1
[info] [30000.000, 35000.000) = 0
[info] [35000.000, 40000.000) = 0
[info] [40000.000, 45000.000) = 0
[info] [45000.000, 50000.000) = 0
[info] [50000.000, 55000.000) = 0
[info]
[info] Percentiles, ns/op:
[info] p(0.0000) = 102.000 ns/op
[info] p(50.0000) = 122.000 ns/op
[info] p(90.0000) = 167.000 ns/op
[info] p(95.0000) = 210.000 ns/op
[info] p(99.0000) = 309.000 ns/op
[info] p(99.9000) = 941.110 ns/op
[info] p(99.9900) = 11340.192 ns/op
[info] p(99.9990) = 54985.142 ns/op
[info] p(99.9999) = 57024.000 ns/op
[info] p(100.0000) = 57024.000 ns/op
[info]
[info]
[info] # JMH 1.11.3 (released 115 days ago, please consider updating!)
[info] # VM version: JDK 1.8.0_25, VM 25.25-b02
[info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_25.jdk/Contents/Home/jre/bin/java
[info] # VM options: <none>
[info] # Warmup: 5 iterations, 1 s each
[info] # Measurement: 5 iterations, 1 s each
[info] # Timeout: 10 min per iteration
[info] # Threads: 1 thread, will synchronize iterations
[info] # Benchmark mode: Sampling time
[info] # Benchmark: net.ruippeixotog.jmh.ListSetBenchmark.createListSet3_eq
[info]
[info] # Run progress: 41.67% complete, ETA 00:02:26
[info] # Fork: 1 of 2
[info] # Warmup Iteration 1: n = 21729, mean = 23282 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 86, 109, 263, 1754, 7942, 28651, 1277118, 494403584 ns/op
[info] # Warmup Iteration 2: n = 21215, mean = 121 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 96, 113, 130, 156, 219, 807, 16083, 18688 ns/op
[info] # Warmup Iteration 3: n = 12370, mean = 126 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 96, 114, 134, 163, 246, 848, 17001, 18048 ns/op
[info] # Warmup Iteration 4: n = 12234, mean = 130 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 99, 118, 142, 175, 253, 979, 18790, 21440 ns/op
[info] # Warmup Iteration 5: n = 12303, mean = 131 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 99, 119, 142, 181, 261, 837, 14089, 15184 ns/op
[info] Iteration 1: n = 12356, mean = 130 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 97, 119, 137, 177, 262, 837, 9832, 9968 ns/op
[info] Iteration 2: n = 12434, mean = 126 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 98, 118, 140, 163, 235, 630, 10374, 10608 ns/op
[info] Iteration 3: n = 11756, mean = 134 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 99, 119, 175, 205, 289, 889, 9196, 10096 ns/op
[info] Iteration 4: n = 12481, mean = 127 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 97, 117, 135, 160, 234, 680, 14592, 16224 ns/op
[info] Iteration 5: n = 12342, mean = 125 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 97, 116, 136, 166, 246, 694, 10712, 11248 ns/op
[info]
[info] # Run progress: 45.83% complete, ETA 00:02:15
[info] # Fork: 2 of 2
[info] # Warmup Iteration 1: n = 40629, mean = 12500 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 68, 109, 159, 508, 5630, 24979, 96229, 494403584 ns/op
[info] # Warmup Iteration 2: n = 21449, mean = 171 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 95, 109, 138, 175, 1767, 3605, 17144, 19872 ns/op
[info] # Warmup Iteration 3: n = 12114, mean = 130 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 96, 114, 135, 175, 273, 997, 33289, 39296 ns/op
[info] # Warmup Iteration 4: n = 12215, mean = 134 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 96, 116, 159, 196, 282, 1149, 16894, 18720 ns/op
[info] # Warmup Iteration 5: n = 12442, mean = 131 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 96, 116, 146, 186, 267, 763, 20072, 23136 ns/op
[info] Iteration 1: n = 12814, mean = 122 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 97, 114, 129, 153, 243, 792, 4285, 4792 ns/op
[info] Iteration 2: n = 12120, mean = 135 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 96, 116, 168, 202, 294, 1330, 18632, 21120 ns/op
[info] Iteration 3: n = 11695, mean = 139 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 96, 117, 190, 222, 328, 1158, 14682, 14880 ns/op
[info] Iteration 4: n = 12534, mean = 124 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 96, 115, 136, 170, 236, 691, 5570, 6888 ns/op
[info] Iteration 5: n = 12575, mean = 125 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 96, 114, 133, 155, 229, 994, 11048, 11168 ns/op
[info]
[info]
[info] Result "createListSet3_eq":
[info] N = 123107
[info] mean = 128.581 ±(99.9%) 1.574 ns/op
[info]
[info] Histogram, ns/op:
[info] [ 0.000, 2500.000) = 123071
[info] [ 2500.000, 5000.000) = 7
[info] [ 5000.000, 7500.000) = 4
[info] [ 7500.000, 10000.000) = 16
[info] [10000.000, 12500.000) = 5
[info] [12500.000, 15000.000) = 2
[info] [15000.000, 17500.000) = 1
[info] [17500.000, 20000.000) = 0
[info] [20000.000, 22500.000) = 1
[info] [22500.000, 25000.000) = 0
[info] [25000.000, 27500.000) = 0
[info]
[info] Percentiles, ns/op:
[info] p(0.0000) = 96.000 ns/op
[info] p(50.0000) = 117.000 ns/op
[info] p(90.0000) = 144.000 ns/op
[info] p(95.0000) = 182.000 ns/op
[info] p(99.0000) = 264.000 ns/op
[info] p(99.9000) = 852.000 ns/op
[info] p(99.9900) = 9618.163 ns/op
[info] p(99.9990) = 19988.632 ns/op
[info] p(99.9999) = 21120.000 ns/op
[info] p(100.0000) = 21120.000 ns/op
[info]
[info]
[info] # JMH 1.11.3 (released 115 days ago, please consider updating!)
[info] # VM version: JDK 1.8.0_25, VM 25.25-b02
[info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_25.jdk/Contents/Home/jre/bin/java
[info] # VM options: <none>
[info] # Warmup: 5 iterations, 1 s each
[info] # Measurement: 5 iterations, 1 s each
[info] # Timeout: 10 min per iteration
[info] # Threads: 1 thread, will synchronize iterations
[info] # Benchmark mode: Sampling time
[info] # Benchmark: net.ruippeixotog.jmh.ListSetBenchmark.createListSet4_distinct
[info]
[info] # Run progress: 50.00% complete, ETA 00:02:05
[info] # Fork: 1 of 2
[info] # Warmup Iteration 1: n = 30692, mean = 16621 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 98, 130, 197, 1271, 9510, 33831, 136544, 494927872 ns/op
[info] # Warmup Iteration 2: n = 29964, mean = 198 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 122, 134, 161, 214, 1840, 3890, 18754, 51968 ns/op
[info] # Warmup Iteration 3: n = 15203, mean = 163 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 122, 142, 179, 226, 327, 1631, 20960, 23808 ns/op
[info] # Warmup Iteration 4: n = 18527, mean = 157 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 124, 143, 163, 194, 281, 1012, 16577, 19552 ns/op
[info] # Warmup Iteration 5: n = 18754, mean = 153 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 124, 142, 159, 180, 271, 1004, 12652, 15888 ns/op
[info] Iteration 1: n = 18693, mean = 154 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 123, 142, 159, 183, 268, 815, 18524, 35328 ns/op
[info] Iteration 2: n = 18706, mean = 157 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 124, 143, 159, 184, 275, 1357, 19678, 32160 ns/op
[info] Iteration 3: n = 18394, mean = 157 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 123, 143, 163, 208, 304, 846, 14414, 14736 ns/op
[info] Iteration 4: n = 17708, mean = 163 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 124, 144, 195, 237, 330, 980, 19191, 20992 ns/op
[info] Iteration 5: n = 18177, mean = 157 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 123, 142, 168, 213, 290, 801, 25915, 27616 ns/op
[info]
[info] # Run progress: 54.17% complete, ETA 00:01:55
[info] # Fork: 2 of 2
[info] # Warmup Iteration 1: n = 31453, mean = 16202 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 123, 131, 190, 960, 8967, 32250, 92154, 491782144 ns/op
[info] # Warmup Iteration 2: n = 15504, mean = 175 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 123, 131, 153, 190, 1486, 10294, 37861, 39552 ns/op
[info] # Warmup Iteration 3: n = 18479, mean = 152 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 124, 139, 159, 203, 293, 933, 16930, 36224 ns/op
[info] # Warmup Iteration 4: n = 18537, mean = 153 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 123, 139, 159, 199, 280, 898, 18089, 20384 ns/op
[info] # Warmup Iteration 5: n = 18597, mean = 150 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 124, 138, 158, 195, 266, 647, 24160, 31424 ns/op
[info] Iteration 1: n = 18964, mean = 147 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 124, 138, 152, 170, 256, 871, 13877, 13920 ns/op
[info] Iteration 2: n = 18888, mean = 146 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 124, 138, 156, 174, 253, 757, 9456, 9584 ns/op
[info] Iteration 3: n = 18510, mean = 149 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 124, 138, 160, 199, 291, 820, 10784, 11696 ns/op
[info] Iteration 4: n = 18648, mean = 149 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 123, 138, 152, 174, 274, 1009, 21211, 21792 ns/op
[info] Iteration 5: n = 17956, mean = 151 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 123, 139, 168, 218, 311, 828, 8763, 11328 ns/op
[info]
[info]
[info] Result "createListSet4_distinct":
[info] N = 184644
[info] mean = 153.048 ±(99.9%) 2.011 ns/op
[info]
[info] Histogram, ns/op:
[info] [ 0.000, 2500.000) = 184571
[info] [ 2500.000, 5000.000) = 5
[info] [ 5000.000, 7500.000) = 7
[info] [ 7500.000, 10000.000) = 22
[info] [10000.000, 12500.000) = 18
[info] [12500.000, 15000.000) = 9
[info] [15000.000, 17500.000) = 3
[info] [17500.000, 20000.000) = 2
[info] [20000.000, 22500.000) = 3
[info] [22500.000, 25000.000) = 0
[info] [25000.000, 27500.000) = 1
[info] [27500.000, 30000.000) = 1
[info] [30000.000, 32500.000) = 1
[info] [32500.000, 35000.000) = 0
[info] [35000.000, 37500.000) = 1
[info]
[info] Percentiles, ns/op:
[info] p(0.0000) = 123.000 ns/op
[info] p(50.0000) = 141.000 ns/op
[info] p(90.0000) = 160.000 ns/op
[info] p(95.0000) = 201.000 ns/op
[info] p(99.0000) = 291.000 ns/op
[info] p(99.9000) = 878.355 ns/op
[info] p(99.9900) = 13897.704 ns/op
[info] p(99.9990) = 32646.446 ns/op
[info] p(99.9999) = 35328.000 ns/op
[info] p(100.0000) = 35328.000 ns/op
[info]
[info]
[info] # JMH 1.11.3 (released 115 days ago, please consider updating!)
[info] # VM version: JDK 1.8.0_25, VM 25.25-b02
[info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_25.jdk/Contents/Home/jre/bin/java
[info] # VM options: <none>
[info] # Warmup: 5 iterations, 1 s each
[info] # Measurement: 5 iterations, 1 s each
[info] # Timeout: 10 min per iteration
[info] # Threads: 1 thread, will synchronize iterations
[info] # Benchmark mode: Sampling time
[info] # Benchmark: net.ruippeixotog.jmh.ListSetBenchmark.createListSet4_eq
[info]
[info] # Run progress: 58.33% complete, ETA 00:01:44
[info] # Fork: 1 of 2
[info] # Warmup Iteration 1: n = 37967, mean = 13485 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 80, 116, 161, 365, 5035, 21218, 91710, 495976448 ns/op
[info] # Warmup Iteration 2: n = 19517, mean = 180 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 101, 113, 162, 194, 1752, 9509, 47938, 55552 ns/op
[info] # Warmup Iteration 3: n = 10503, mean = 132 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 102, 123, 144, 165, 261, 919, 6717, 6968 ns/op
[info] # Warmup Iteration 4: n = 11566, mean = 135 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 103, 124, 143, 173, 256, 848, 12332, 12896 ns/op
[info] # Warmup Iteration 5: n = 11418, mean = 138 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 103, 124, 150, 190, 269, 608, 15398, 15920 ns/op
[info] Iteration 1: n = 11587, mean = 136 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 104, 125, 147, 170, 246, 842, 10875, 10992 ns/op
[info] Iteration 2: n = 11654, mean = 131 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 102, 125, 143, 164, 236, 427, 2958, 3368 ns/op
[info] Iteration 3: n = 11420, mean = 136 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 103, 126, 148, 182, 260, 753, 10495, 10672 ns/op
[info] Iteration 4: n = 11412, mean = 137 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 103, 125, 146, 189, 265, 835, 22845, 25088 ns/op
[info] Iteration 5: n = 11223, mean = 139 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 103, 127, 160, 194, 278, 1044, 12946, 14176 ns/op
[info]
[info] # Run progress: 62.50% complete, ETA 00:01:34
[info] # Fork: 2 of 2
[info] # Warmup Iteration 1: n = 37747, mean = 13466 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 75, 115, 167, 532, 6640, 25826, 83347, 494927872 ns/op
[info] # Warmup Iteration 2: n = 20056, mean = 138 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 101, 118, 141, 173, 256, 1325, 26141, 28224 ns/op
[info] # Warmup Iteration 3: n = 10707, mean = 134 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 101, 121, 139, 161, 280, 993, 18344, 18496 ns/op
[info] # Warmup Iteration 4: n = 11361, mean = 138 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 104, 125, 160, 202, 288, 824, 10431, 10688 ns/op
[info] # Warmup Iteration 5: n = 11043, mean = 145 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 104, 125, 192, 229, 323, 932, 13308, 13680 ns/op
[info] Iteration 1: n = 11624, mean = 132 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 103, 122, 140, 174, 263, 703, 8577, 8864 ns/op
[info] Iteration 2: n = 11536, mean = 134 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 103, 122, 144, 175, 265, 621, 12638, 12768 ns/op
[info] Iteration 3: n = 10418, mean = 160 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 103, 130, 217, 256, 369, 1114, 41098, 42432 ns/op
[info] Iteration 4: n = 11189, mean = 137 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 104, 124, 161, 200, 281, 893, 8834, 9440 ns/op
[info] Iteration 5: n = 10764, mean = 154 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 104, 129, 202, 233, 331, 1218, 23667, 23936 ns/op
[info]
[info]
[info] Result "createListSet4_eq":
[info] N = 112827
[info] mean = 139.300 ±(99.9%) 2.309 ns/op
[info]
[info] Histogram, ns/op:
[info] [ 0.000, 5000.000) = 112801
[info] [ 5000.000, 10000.000) = 12
[info] [10000.000, 15000.000) = 8
[info] [15000.000, 20000.000) = 2
[info] [20000.000, 25000.000) = 2
[info] [25000.000, 30000.000) = 1
[info] [30000.000, 35000.000) = 0
[info] [35000.000, 40000.000) = 0
[info] [40000.000, 45000.000) = 1
[info]
[info] Percentiles, ns/op:
[info] p(0.0000) = 102.000 ns/op
[info] p(50.0000) = 125.000 ns/op
[info] p(90.0000) = 160.000 ns/op
[info] p(95.0000) = 200.000 ns/op
[info] p(99.0000) = 287.000 ns/op
[info] p(99.9000) = 812.548 ns/op
[info] p(99.9900) = 10649.376 ns/op
[info] p(99.9990) = 40207.112 ns/op
[info] p(99.9999) = 42432.000 ns/op
[info] p(100.0000) = 42432.000 ns/op
[info]
[info]
[info] # JMH 1.11.3 (released 115 days ago, please consider updating!)
[info] # VM version: JDK 1.8.0_25, VM 25.25-b02
[info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_25.jdk/Contents/Home/jre/bin/java
[info] # VM options: <none>
[info] # Warmup: 5 iterations, 1 s each
[info] # Measurement: 5 iterations, 1 s each
[info] # Timeout: 10 min per iteration
[info] # Threads: 1 thread, will synchronize iterations
[info] # Benchmark mode: Sampling time
[info] # Benchmark: net.ruippeixotog.jmh.ListSetBenchmark.createListSet5_distinct
[info]
[info] # Run progress: 66.67% complete, ETA 00:01:23
[info] # Fork: 1 of 2
[info] # Warmup Iteration 1: n = 25831, mean = 20320 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 132, 146, 256, 1823, 12795, 38816, 327520, 508559360 ns/op
[info] # Warmup Iteration 2: n = 26848, mean = 161 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 132, 148, 167, 200, 318, 1124, 12960, 25312 ns/op
[info] # Warmup Iteration 3: n = 16205, mean = 166 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 133, 150, 170, 213, 329, 1473, 12805, 14304 ns/op
[info] # Warmup Iteration 4: n = 15787, mean = 179 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 131, 157, 211, 261, 358, 1695, 17097, 17856 ns/op
[info] # Warmup Iteration 5: n = 16252, mean = 173 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 132, 156, 180, 219, 332, 1219, 16328, 19520 ns/op
[info] Iteration 1: n = 15696, mean = 176 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 132, 156, 214, 261, 379, 1080, 17554, 18848 ns/op
[info] Iteration 2: n = 16552, mean = 166 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 132, 154, 177, 202, 292, 875, 11281, 12288 ns/op
[info] Iteration 3: n = 16287, mean = 200 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 132, 155, 178, 220, 323, 1238, 193781, 491520 ns/op
[info] Iteration 4: n = 15990, mean = 177 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 132, 157, 191, 241, 346, 1157, 27949, 44800 ns/op
[info] Iteration 5: n = 16276, mean = 170 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 132, 154, 180, 220, 314, 800, 16555, 17248 ns/op
[info]
[info] # Run progress: 70.83% complete, ETA 00:01:13
[info] # Fork: 2 of 2
[info] # Warmup Iteration 1: n = 26677, mean = 19146 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 133, 146, 248, 1796, 12788, 44281, 667722, 492306432 ns/op
[info] # Warmup Iteration 2: n = 24302, mean = 204 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 132, 150, 233, 276, 1660, 4340, 25699, 61568 ns/op
[info] # Warmup Iteration 3: n = 13647, mean = 166 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 132, 149, 183, 240, 340, 965, 15507, 16272 ns/op
[info] # Warmup Iteration 4: n = 16540, mean = 161 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 132, 147, 170, 199, 299, 978, 15006, 18240 ns/op
[info] # Warmup Iteration 5: n = 16438, mean = 163 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 132, 147, 171, 211, 320, 868, 15787, 19104 ns/op
[info] Iteration 1: n = 16593, mean = 161 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 133, 147, 169, 197, 309, 1003, 17736, 20416 ns/op
[info] Iteration 2: n = 16614, mean = 158 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 133, 146, 166, 189, 295, 905, 12661, 16704 ns/op
[info] Iteration 3: n = 16614, mean = 157 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 132, 146, 165, 192, 289, 769, 13694, 14160 ns/op
[info] Iteration 4: n = 15921, mean = 170 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 132, 148, 199, 250, 352, 1405, 19960, 32192 ns/op
[info] Iteration 5: n = 15667, mean = 172 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 133, 150, 217, 260, 376, 1239, 13837, 15488 ns/op
[info]
[info]
[info] Result "createListSet5_distinct":
[info] N = 162210
[info] mean = 170.634 ±(99.9%) 10.238 ns/op
[info]
[info] Histogram, ns/op:
[info] [ 0.000, 50000.000) = 162209
[info] [ 50000.000, 100000.000) = 0
[info] [100000.000, 150000.000) = 0
[info] [150000.000, 200000.000) = 0
[info] [200000.000, 250000.000) = 0
[info] [250000.000, 300000.000) = 0
[info] [300000.000, 350000.000) = 0
[info] [350000.000, 400000.000) = 0
[info] [400000.000, 450000.000) = 0
[info]
[info] Percentiles, ns/op:
[info] p(0.0000) = 132.000 ns/op
[info] p(50.0000) = 152.000 ns/op
[info] p(90.0000) = 180.000 ns/op
[info] p(95.0000) = 229.000 ns/op
[info] p(99.0000) = 332.000 ns/op
[info] p(99.9000) = 1010.890 ns/op
[info] p(99.9900) = 15500.462 ns/op
[info] p(99.9990) = 213611.021 ns/op
[info] p(99.9999) = 491520.000 ns/op
[info] p(100.0000) = 491520.000 ns/op
[info]
[info]
[info] # JMH 1.11.3 (released 115 days ago, please consider updating!)
[info] # VM version: JDK 1.8.0_25, VM 25.25-b02
[info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_25.jdk/Contents/Home/jre/bin/java
[info] # VM options: <none>
[info] # Warmup: 5 iterations, 1 s each
[info] # Measurement: 5 iterations, 1 s each
[info] # Timeout: 10 min per iteration
[info] # Threads: 1 thread, will synchronize iterations
[info] # Benchmark mode: Sampling time
[info] # Benchmark: net.ruippeixotog.jmh.ListSetBenchmark.createListSet5_eq
[info]
[info] # Run progress: 75.00% complete, ETA 00:01:02
[info] # Fork: 1 of 2
[info] # Warmup Iteration 1: n = 35353, mean = 14303 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 104, 120, 178, 799, 5860, 26130, 82393, 492830720 ns/op
[info] # Warmup Iteration 2: n = 18130, mean = 146 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 103, 120, 165, 200, 338, 2781, 27416, 30304 ns/op
[info] # Warmup Iteration 3: n = 10003, mean = 142 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 104, 126, 147, 173, 275, 1024, 26650, 26656 ns/op
[info] # Warmup Iteration 4: n = 10954, mean = 141 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 106, 125, 152, 179, 263, 543, 46127, 49984 ns/op
[info] # Warmup Iteration 5: n = 10914, mean = 145 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 105, 125, 154, 190, 290, 1054, 39632, 41664 ns/op
[info] Iteration 1: n = 10878, mean = 139 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 105, 125, 153, 183, 264, 798, 17641, 17856 ns/op
[info] Iteration 2: n = 10892, mean = 139 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 105, 126, 154, 191, 271, 587, 15794, 16288 ns/op
[info] Iteration 3: n = 10467, mean = 145 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 106, 129, 183, 220, 320, 845, 9827, 9952 ns/op
[info] Iteration 4: n = 10575, mean = 146 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 106, 127, 175, 208, 302, 1164, 14435, 14640 ns/op
[info] Iteration 5: n = 10239, mean = 156 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 106, 129, 203, 243, 341, 1263, 26729, 26976 ns/op
[info]
[info] # Run progress: 79.17% complete, ETA 00:00:52
[info] # Fork: 2 of 2
[info] # Warmup Iteration 1: n = 35711, mean = 14228 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 20, 120, 174, 784, 6150, 30965, 147604, 493879296 ns/op
[info] # Warmup Iteration 2: n = 18839, mean = 164 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 103, 121, 145, 181, 1629, 3316, 39913, 42176 ns/op
[info] # Warmup Iteration 3: n = 10365, mean = 132 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 105, 120, 134, 146, 253, 787, 10571, 10576 ns/op
[info] # Warmup Iteration 4: n = 10944, mean = 142 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 115, 129, 156, 184, 263, 815, 14322, 14528 ns/op
[info] # Warmup Iteration 5: n = 10890, mean = 138 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 105, 129, 153, 179, 267, 684, 10158, 11008 ns/op
[info] Iteration 1: n = 10813, mean = 142 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 107, 129, 161, 188, 276, 569, 13030, 13232 ns/op
[info] Iteration 2: n = 11003, mean = 138 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 106, 129, 149, 181, 267, 684, 10312, 10832 ns/op
[info] Iteration 3: n = 9889, mean = 164 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 115, 137, 219, 256, 383, 1492, 14944, 14944 ns/op
[info] Iteration 4: n = 10750, mean = 149 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 103, 130, 165, 204, 304, 882, 22409, 22560 ns/op
[info] Iteration 5: n = 10829, mean = 141 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 106, 130, 155, 188, 280, 784, 11084, 11344 ns/op
[info]
[info]
[info] Result "createListSet5_eq":
[info] N = 106335
[info] mean = 145.685 ±(99.9%) 2.477 ns/op
[info]
[info] Histogram, ns/op:
[info] [ 0.000, 2500.000) = 106289
[info] [ 2500.000, 5000.000) = 3
[info] [ 5000.000, 7500.000) = 8
[info] [ 7500.000, 10000.000) = 14
[info] [10000.000, 12500.000) = 10
[info] [12500.000, 15000.000) = 4
[info] [15000.000, 17500.000) = 3
[info] [17500.000, 20000.000) = 1
[info] [20000.000, 22500.000) = 1
[info] [22500.000, 25000.000) = 1
[info] [25000.000, 27500.000) = 1
[info]
[info] Percentiles, ns/op:
[info] p(0.0000) = 103.000 ns/op
[info] p(50.0000) = 129.000 ns/op
[info] p(90.0000) = 171.000 ns/op
[info] p(95.0000) = 210.000 ns/op
[info] p(99.0000) = 304.000 ns/op
[info] p(99.9000) = 901.664 ns/op
[info] p(99.9900) = 13706.854 ns/op
[info] p(99.9990) = 26696.202 ns/op
[info] p(99.9999) = 26976.000 ns/op
[info] p(100.0000) = 26976.000 ns/op
[info]
[info]
[info] # JMH 1.11.3 (released 115 days ago, please consider updating!)
[info] # VM version: JDK 1.8.0_25, VM 25.25-b02
[info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_25.jdk/Contents/Home/jre/bin/java
[info] # VM options: <none>
[info] # Warmup: 5 iterations, 1 s each
[info] # Measurement: 5 iterations, 1 s each
[info] # Timeout: 10 min per iteration
[info] # Threads: 1 thread, will synchronize iterations
[info] # Benchmark mode: Sampling time
[info] # Benchmark: net.ruippeixotog.jmh.ListSetBenchmark.createListSet6_distinct
[info]
[info] # Run progress: 83.33% complete, ETA 00:00:41
[info] # Fork: 1 of 2
[info] # Warmup Iteration 1: n = 22198, mean = 23536 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 144, 163, 735, 2232, 13344, 46720, 644618, 505413632 ns/op
[info] # Warmup Iteration 2: n = 22541, mean = 184 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 143, 160, 203, 243, 366, 2172, 14068, 15424 ns/op
[info] # Warmup Iteration 3: n = 12339, mean = 179 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 144, 160, 183, 232, 363, 1781, 22955, 26560 ns/op
[info] # Warmup Iteration 4: n = 14280, mean = 180 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 144, 161, 194, 238, 366, 1204, 21250, 25408 ns/op
[info] # Warmup Iteration 5: n = 12581, mean = 217 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 144, 170, 297, 341, 461, 7584, 19718, 20928 ns/op
[info] Iteration 1: n = 13179, mean = 199 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 144, 166, 267, 310, 422, 1393, 17655, 17920 ns/op
[info] Iteration 2: n = 13246, mean = 194 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 144, 165, 265, 307, 421, 1475, 11845, 12048 ns/op
[info] Iteration 3: n = 13963, mean = 185 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 144, 161, 203, 266, 384, 1044, 18081, 18144 ns/op
[info] Iteration 4: n = 13705, mean = 183 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 144, 162, 204, 266, 396, 1238, 18891, 20160 ns/op
[info] Iteration 5: n = 14081, mean = 185 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 144, 163, 197, 259, 373, 1002, 23573, 23808 ns/op
[info]
[info] # Run progress: 87.50% complete, ETA 00:00:31
[info] # Fork: 2 of 2
[info] # Warmup Iteration 1: n = 22712, mean = 22806 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 145, 164, 796, 2177, 12862, 56422, 338536, 500170752 ns/op
[info] # Warmup Iteration 2: n = 21150, mean = 474 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 145, 165, 304, 1680, 2826, 13603, 50566, 2674688 ns/op
[info] # Warmup Iteration 3: n = 11278, mean = 199 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 145, 166, 239, 285, 410, 9071, 23370, 24000 ns/op
[info] # Warmup Iteration 4: n = 13449, mean = 193 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 146, 168, 242, 292, 404, 1255, 16266, 17376 ns/op
[info] # Warmup Iteration 5: n = 13743, mean = 191 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 145, 166, 219, 271, 380, 1150, 38996, 49024 ns/op
[info] Iteration 1: n = 13259, mean = 198 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 146, 169, 260, 303, 406, 1373, 18083, 18688 ns/op
[info] Iteration 2: n = 13422, mean = 201 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 146, 168, 250, 296, 410, 8411, 27471, 31360 ns/op
[info] Iteration 3: n = 13851, mean = 184 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 146, 165, 199, 255, 359, 1349, 18682, 22016 ns/op
[info] Iteration 4: n = 13645, mean = 189 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 146, 166, 226, 283, 374, 1074, 16127, 16448 ns/op
[info] Iteration 5: n = 13291, mean = 194 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 146, 168, 260, 302, 412, 980, 18498, 22880 ns/op
[info]
[info]
[info] Result "createListSet6_distinct":
[info] N = 135642
[info] mean = 191.056 ±(99.9%) 2.910 ns/op
[info]
[info] Histogram, ns/op:
[info] [ 0.000, 2500.000) = 135546
[info] [ 2500.000, 5000.000) = 4
[info] [ 5000.000, 7500.000) = 12
[info] [ 7500.000, 10000.000) = 33
[info] [10000.000, 12500.000) = 24
[info] [12500.000, 15000.000) = 5
[info] [15000.000, 17500.000) = 6
[info] [17500.000, 20000.000) = 5
[info] [20000.000, 22500.000) = 3
[info] [22500.000, 25000.000) = 3
[info] [25000.000, 27500.000) = 0
[info] [27500.000, 30000.000) = 0
[info] [30000.000, 32500.000) = 1
[info] [32500.000, 35000.000) = 0
[info] [35000.000, 37500.000) = 0
[info]
[info] Percentiles, ns/op:
[info] p(0.0000) = 144.000 ns/op
[info] p(50.0000) = 165.000 ns/op
[info] p(90.0000) = 239.000 ns/op
[info] p(95.0000) = 289.000 ns/op
[info] p(99.0000) = 399.000 ns/op
[info] p(99.9000) = 1322.000 ns/op
[info] p(99.9900) = 16943.539 ns/op
[info] p(99.9990) = 28668.241 ns/op
[info] p(99.9999) = 31360.000 ns/op
[info] p(100.0000) = 31360.000 ns/op
[info]
[info]
[info] # JMH 1.11.3 (released 115 days ago, please consider updating!)
[info] # VM version: JDK 1.8.0_25, VM 25.25-b02
[info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_25.jdk/Contents/Home/jre/bin/java
[info] # VM options: <none>
[info] # Warmup: 5 iterations, 1 s each
[info] # Measurement: 5 iterations, 1 s each
[info] # Timeout: 10 min per iteration
[info] # Threads: 1 thread, will synchronize iterations
[info] # Benchmark mode: Sampling time
[info] # Benchmark: net.ruippeixotog.jmh.ListSetBenchmark.createListSet6_eq
[info]
[info] # Run progress: 91.67% complete, ETA 00:00:20
[info] # Fork: 1 of 2
[info] # Warmup Iteration 1: n = 31570, mean = 16392 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 91, 126, 207, 1109, 6606, 27369, 100036, 504365056 ns/op
[info] # Warmup Iteration 2: n = 18605, mean = 139 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 107, 125, 146, 163, 242, 2008, 10436, 10656 ns/op
[info] # Warmup Iteration 3: n = 18826, mean = 142 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 108, 132, 149, 175, 261, 819, 10373, 10768 ns/op
[info] # Warmup Iteration 4: n = 10272, mean = 148 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 118, 134, 152, 180, 279, 973, 11180, 11184 ns/op
[info] # Warmup Iteration 5: n = 10072, mean = 148 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 109, 135, 174, 217, 306, 1006, 4034, 4052 ns/op
[info] Iteration 1: n = 10070, mean = 153 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 118, 136, 173, 211, 314, 1000, 13384, 13392 ns/op
[info] Iteration 2: n = 10232, mean = 147 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 108, 135, 160, 195, 290, 728, 13214, 13248 ns/op
[info] Iteration 3: n = 10353, mean = 147 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 108, 135, 157, 190, 287, 859, 14502, 14640 ns/op
[info] Iteration 4: n = 10181, mean = 152 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 109, 136, 170, 206, 321, 1103, 23456, 23680 ns/op
[info] Iteration 5: n = 9940, mean = 156 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 119, 137, 190, 232, 340, 1380, 16768, 16768 ns/op
[info]
[info] # Run progress: 95.83% complete, ETA 00:00:10
[info] # Fork: 2 of 2
[info] # Warmup Iteration 1: n = 30411, mean = 17635 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 110, 129, 206, 1261, 7950, 29321, 96950, 522190848 ns/op
[info] # Warmup Iteration 2: n = 17515, mean = 214 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 107, 126, 184, 231, 1899, 11327, 90423, 139776 ns/op
[info] # Warmup Iteration 3: n = 17507, mean = 141 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 109, 130, 154, 193, 271, 445, 9841, 12208 ns/op
[info] # Warmup Iteration 4: n = 10135, mean = 143 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 109, 130, 149, 178, 293, 1229, 14641, 14720 ns/op
[info] # Warmup Iteration 5: n = 10196, mean = 144 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 118, 130, 153, 192, 283, 1233, 10103, 10112 ns/op
[info] Iteration 1: n = 10224, mean = 140 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 119, 130, 152, 187, 281, 659, 9301, 9472 ns/op
[info] Iteration 2: n = 10147, mean = 144 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 110, 130, 152, 188, 278, 587, 45634, 46208 ns/op
[info] Iteration 3: n = 10214, mean = 140 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 118, 130, 150, 184, 286, 917, 11757, 11952 ns/op
[info] Iteration 4: n = 10171, mean = 141 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 108, 130, 151, 191, 283, 826, 10560, 10592 ns/op
[info] Iteration 5: n = 10065, mean = 146 ns/op, p{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 1.00} = 118, 131, 158, 201, 288, 921, 26495, 26560 ns/op
[info]
[info]
[info] Result "createListSet6_eq":
[info] N = 101597
[info] mean = 146.617 ±(99.9%) 2.665 ns/op
[info]
[info] Histogram, ns/op:
[info] [ 0.000, 5000.000) = 101568
[info] [ 5000.000, 10000.000) = 12
[info] [10000.000, 15000.000) = 12
[info] [15000.000, 20000.000) = 2
[info] [20000.000, 25000.000) = 1
[info] [25000.000, 30000.000) = 1
[info] [30000.000, 35000.000) = 0
[info] [35000.000, 40000.000) = 0
[info] [40000.000, 45000.000) = 0
[info]
[info] Percentiles, ns/op:
[info] p(0.0000) = 108.000 ns/op
[info] p(50.0000) = 133.000 ns/op
[info] p(90.0000) = 160.000 ns/op
[info] p(95.0000) = 200.000 ns/op
[info] p(99.0000) = 297.000 ns/op
[info] p(99.9000) = 879.402 ns/op
[info] p(99.9900) = 11923.875 ns/op
[info] p(99.9990) = 45894.025 ns/op
[info] p(99.9999) = 46208.000 ns/op
[info] p(100.0000) = 46208.000 ns/op
[info]
[info]
[info] # Run complete. Total time: 00:04:11
[info]
[info] Benchmark Mode Cnt Score Error Units
[info] ListSetBenchmark.createListSet0 sample 146595 62.053 ± 1.513 ns/op
[info] ListSetBenchmark.createListSet1 sample 147913 114.764 ± 6.772 ns/op
[info] ListSetBenchmark.createListSet2_distinct sample 123229 130.837 ± 2.646 ns/op
[info] ListSetBenchmark.createListSet2_eq sample 135762 120.509 ± 1.630 ns/op
[info] ListSetBenchmark.createListSet3_distinct sample 106629 139.546 ± 2.753 ns/op
[info] ListSetBenchmark.createListSet3_eq sample 123107 128.581 ± 1.574 ns/op
[info] ListSetBenchmark.createListSet4_distinct sample 184644 153.048 ± 2.011 ns/op
[info] ListSetBenchmark.createListSet4_eq sample 112827 139.300 ± 2.309 ns/op
[info] ListSetBenchmark.createListSet5_distinct sample 162210 170.634 ± 10.238 ns/op
[info] ListSetBenchmark.createListSet5_eq sample 106335 145.685 ± 2.477 ns/op
[info] ListSetBenchmark.createListSet6_distinct sample 135642 191.056 ± 2.910 ns/op
[info] ListSetBenchmark.createListSet6_eq sample 101597 146.617 ± 2.665 ns/op
Raw
ListSetBenchmark.scala
package net.ruippeixotog.jmh
import java.util.concurrent.TimeUnit
import scala.collection.immutable.ListSet
import org.openjdk.jmh.annotations.{Benchmark, BenchmarkMode, Mode, OutputTimeUnit}
import org.openjdk.jmh.infra.Blackhole
class ListSetBenchmark {
@Benchmark @BenchmarkMode(Array(Mode.SampleTime)) @OutputTimeUnit(TimeUnit.NANOSECONDS)
def createListSet0(blackhole: Blackhole) {
blackhole.consume(ListSet())
}
@Benchmark @BenchmarkMode(Array(Mode.SampleTime)) @OutputTimeUnit(TimeUnit.NANOSECONDS)
def createListSet1(blackhole: Blackhole) {
blackhole.consume(ListSet(0))
}
@Benchmark @BenchmarkMode(Array(Mode.SampleTime)) @OutputTimeUnit(TimeUnit.NANOSECONDS)
def createListSet2_distinct(blackhole: Blackhole) {
blackhole.consume(ListSet(0, 1))
}
@Benchmark @BenchmarkMode(Array(Mode.SampleTime)) @OutputTimeUnit(TimeUnit.NANOSECONDS)
def createListSet2_eq(blackhole: Blackhole) {
blackhole.consume(ListSet(0, 0))
}
@Benchmark @BenchmarkMode(Array(Mode.SampleTime)) @OutputTimeUnit(TimeUnit.NANOSECONDS)
def createListSet3_distinct(blackhole: Blackhole) {
blackhole.consume(ListSet(0, 1, 2))
}
@Benchmark @BenchmarkMode(Array(Mode.SampleTime)) @OutputTimeUnit(TimeUnit.NANOSECONDS)
def createListSet3_eq(blackhole: Blackhole) {
blackhole.consume(ListSet(0, 0, 0))
}
@Benchmark @BenchmarkMode(Array(Mode.SampleTime)) @OutputTimeUnit(TimeUnit.NANOSECONDS)
def createListSet4_distinct(blackhole: Blackhole) {
blackhole.consume(ListSet(0, 1, 2, 3))
}
@Benchmark @BenchmarkMode(Array(Mode.SampleTime)) @OutputTimeUnit(TimeUnit.NANOSECONDS)
def createListSet4_eq(blackhole: Blackhole) {
blackhole.consume(ListSet(0, 0, 0, 0))
}
@Benchmark @BenchmarkMode(Array(Mode.SampleTime)) @OutputTimeUnit(TimeUnit.NANOSECONDS)
def createListSet5_distinct(blackhole: Blackhole) {
blackhole.consume(ListSet(0, 1, 2, 3, 4))
}
@Benchmark @BenchmarkMode(Array(Mode.SampleTime)) @OutputTimeUnit(TimeUnit.NANOSECONDS)
def createListSet5_eq(blackhole: Blackhole) {
blackhole.consume(ListSet(0, 0, 0, 0, 0))
}
@Benchmark @BenchmarkMode(Array(Mode.SampleTime)) @OutputTimeUnit(TimeUnit.NANOSECONDS)
def createListSet6_distinct(blackhole: Blackhole) {
blackhole.consume(ListSet(0, 1, 2, 3, 4, 5))
}
@Benchmark @BenchmarkMode(Array(Mode.SampleTime)) @OutputTimeUnit(TimeUnit.NANOSECONDS)
def createListSet6_eq(blackhole: Blackhole) {
blackhole.consume(ListSet(0, 0, 0, 0, 0, 0))
}
}
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