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Copy pathfactorial_output.txt
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86 lines (74 loc) · 4.65 KB
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====== PART 1: Performance and comparison of indivudual input values ======
======================== across factorial functions =======================
$`0`
Unit: microseconds
expr min lq mean median uq max neval cld
factorial_loop(.) 1.185 1.185 1.46130 1.185 1.580 12.238 100 a
factorial_reduce(.) 1.185 1.185 1.39421 1.185 1.185 11.843 100 a
factorial_func(.) 1.185 1.185 2.00609 1.185 1.185 50.529 100 a
factorial_mem(.) 1.185 1.185 1.39812 1.185 1.185 15.001 100 a
$`1`
Unit: microseconds
expr min lq mean median uq max neval cld
factorial_loop(.) 1.185 1.580 1.68240 1.580 1.7770 3.554 100 a
factorial_reduce(.) 6.316 6.711 7.65104 6.712 7.1065 36.713 100 c
factorial_func(.) 1.974 1.975 2.32978 2.369 2.3690 5.133 100 ab
factorial_mem(.) 1.974 2.369 3.11531 2.370 2.7640 36.713 100 b
$`6`
Unit: microseconds
expr min lq mean median uq max neval cld
factorial_loop(.) 2.369 2.764 3.04432 3.1585 3.159 6.317 100 a
factorial_reduce(.) 11.448 11.844 13.53674 12.2380 12.633 61.581 100 c
factorial_func(.) 5.922 6.316 6.57728 6.3170 6.711 25.659 100 b
factorial_mem(.) 1.974 2.369 3.00085 2.3700 2.764 23.291 100 a
$`11`
Unit: microseconds
expr min lq mean median uq max neval cld
factorial_loop(.) 3.948 4.343 4.62333 4.738 4.738 8.290 100 b
factorial_reduce(.) 16.185 16.975 17.74873 17.370 18.159 45.002 100 d
factorial_func(.) 9.869 10.264 10.80504 10.265 10.659 33.555 100 c
factorial_mem(.) 1.974 2.369 2.92582 2.764 2.764 28.028 100 a
$`13`
Unit: microseconds
expr min lq mean median uq max neval cld
factorial_loop(.) 4.737 5.9220 6.34048 6.3170 7.1060 9.869 100 b
factorial_reduce(.) 69.082 72.4375 79.59831 80.3325 81.7145 183.165 100 d
factorial_func(.) 11.843 13.0275 13.67487 13.8170 14.2120 16.185 100 c
factorial_mem(.) 1.975 3.1580 3.49826 3.1590 3.7510 14.212 100 a
$`45`
Unit: microseconds
expr min lq mean median uq max neval cld
factorial_loop(.) 15.396 16.186 17.17639 16.9750 17.7650 21.317 100 a
factorial_reduce(.) 102.636 104.610 121.60785 106.9785 118.0315 1129.383 100 c
factorial_func(.) 37.897 39.870 42.07334 41.2520 43.0280 88.820 100 b
factorial_mem(.) 1.974 2.764 4.24042 3.1590 3.5540 96.320 100 a
$`63`
Unit: microseconds
expr min lq mean median uq max neval cld
factorial_loop(.) 20.528 21.712 23.93054 22.502 24.2785 43.028 100 b
factorial_reduce(.) 118.821 122.373 141.68082 128.887 140.9270 692.394 100 d
factorial_func(.) 51.713 54.082 58.70415 56.055 59.8055 140.927 100 c
factorial_mem(.) 1.974 2.764 4.42986 3.159 3.9485 61.187 100 a
====== PART 2: Performance and comparison of ranges of input values =======
======================== across factorial functions =======================
$`range 1:10`
Unit: microseconds
expr min lq mean median uq max neval cld
map_dbl(x, factorial_loop) 35.134 36.3175 37.97577 36.712 37.1080 61.582 100 a
map_dbl(x, factorial_reduce) 121.584 123.9530 129.60154 125.137 126.7155 217.904 100 c
map_dbl(x, factorial_func) 67.503 68.6875 71.58100 69.872 71.2535 97.899 100 b
map_dbl(x, factorial_mem) 32.371 33.9490 35.58362 34.345 35.1340 74.214 100 a
$`range 1:50`
Unit: microseconds
expr min lq mean median uq max neval cld
map_dbl(x, factorial_loop) 2317.190 2455.550 2631.7430 2562.9220 2644.833 5885.739 100 b
map_dbl(x, factorial_reduce) 3545.655 3761.584 4787.9326 3891.8520 4070.872 85894.281 100 c
map_dbl(x, factorial_func) 1162.937 1205.570 1323.8735 1230.2420 1293.995 6598.265 100 ab
map_dbl(x, factorial_mem) 150.006 158.296 172.4517 166.5855 180.994 329.618 100 a
$`range 1:100`
Unit: microseconds
expr min lq mean median uq max neval cld
map_dbl(x, factorial_loop) 6080.352 6291.9385 6561.7071 6434.4435 6706.032 8420.041 100 c
map_dbl(x, factorial_reduce) 10429.325 10701.7025 11260.0659 11111.0595 11635.091 14098.535 100 d
map_dbl(x, factorial_func) 4425.950 4557.9940 4778.3994 4641.2870 4819.516 6439.575 100 b
map_dbl(x, factorial_mem) 292.116 305.3405 325.9625 319.5515 336.131 569.232 100 a