Advent of Code 2019 in Multi-Platform Kotlin

Intro

In 2019, I will be participating in the Advent of Code using Kotlin multi-platform code only. This means I can run my solutions on the JVM, on Node JS and natively on Windows through MinGW.

I will be measuring the performance of each platform by measuring the elapsed time calculating the solution N times:

val times = (1..repeat).map {
    measureNanos {
        Puzzles.run(day, part)
    }
}

where repeat is the number of desired repetitions, and day and part are used to select the proper challenge.

Note that the measureNanos function is the first example of functionality that isn't available in common code, since measuring time is platform-specific. The function is implemented using Kotlin's expext/actual mechanism.

Runner.kt defines its expectation of a function measureNanos for each supported platform with the signature:

expect inline fun measureNanos(block: () -> Unit): Long

Each platform provides an actual implementation in files called Actuals.kt. On the JVM and native, we can use measureNanoTime as provided by the Kotlin standard library on these platforms:

JVM and Native:

actual inline fun measureNanos(block: () -> Unit) = measureNanoTime(block)

On Node, we need to write our own implementation:

actual inline fun measureNanos(block: () -> Unit): Long {
    val start = process.hrtime()
    block()
    val end = process.hrtime(start)
    val nanos = (end[0] * 1e9 + end[1]) as Double
    return nanos.roundToLong()
}

Usually, I try to finish a puzzle as quickly as possible, then spend some time to cleanup and optimise the code. If and when these optimizations alter the performance, I'll update the measurements below to reflect the new solution.

Important

There is a very explicit warning in the FAQ of Kotlin/Native that says the current version of Kotlin/Native is not suited for performance analysis, as it has not been optimised in any way for performance and benchmarking.

Apart from that, all measurements were taken on my laptop, under non-controlled circumstances using non-optimized code and tools. So, if you use the results below for anything important, you're insane...

Day 01

Last year, most days showed a similar pattern, with the JVM beating JS by a factor or two, and native being an order of magnitude slower than either.

This year's first puzzle shows a very comparable performance between JVM and JS, with native being much slower.

Platform	Average (µs)	Measurements (µs)
JVM	2333 ± 35341	36888, 1539, 1665, 2358, 1270, 1392, 1104, 1138, 1018, 779, 785, 790, 931, 649, 612, 709, 532, 502, 497, 521, 361, 517, 557, 543, 647
Node JS	1846 ± 9899	5310, 3450, 6375, 8137, 1849, 1429, 1131, 897, 857, 972, 2664, 2122, 845, 679, 686, 476, 489, 642, 572, 413, 1507, 611, 3033, 598, 401
MinGW64	2746 ± 6130	3742, 4797, 5268, 3800, 4935, 2203, 2150, 1828, 3374, 1577, 2342, 1932, 3117, 1734, 5184, 1413, 2375, 1377, 3232, 1327, 2872, 1948, 2450, 1335, 2339

Day 02

The solution for day 2 is very overdesigned, mostly because part 2 strongly hints that this is the first of several puzzles involving emulating this computer. So the code isn't the most performant, but hopefully it will help later in the month!

JS and native perform dramatically worse today than the JVM:

Update: for Day 7 I refactored the project to be able to use coroutines. I subsequently refactored the solution to Day 2 to search for the solution in parallel. New numbers are below as JVM-Parallel

Platform	Average (ms)	Measurements (ms)
JVM	184 ± 47	403, 228, 201, 174, 175, 173, 168, 171, 165, 168, 170, 176, 173, 171, 169, 166, 169, 170, 165, 169, 164, 166, 174, 192, 177
JVM-Parallel	83nbsp;± 80	467, 107, 85, 77, 92, 98, 76, 76, 94, 86, 61, 51, 56, 60, 55, 62, 52, 53, 51, 52, 46, 51, 51, 72, 46
Node JS	8375 ± 502	8132, 8235, 8443, 8046, 8058, 7936, 8541, 8578, 8688, 8469, 8981, 8159, 8620, 8453, 7882, 7711, 7706, 8452, 9847, 9585, 8349, 8357, 8203, 8114, 7825
MinGW64	5746 ± 496	5308, 5253, 5163, 5059, 5389, 6387, 5259, 6118, 5426, 6653, 5217, 5787, 6156, 6182, 6006, 5699, 6813, 5716, 6608, 5436, 5476, 5983, 5760, 5380, 5417

Day 03

Platform	Average (ms)	Measurements (ms)
JVM	210 ± 37	373, 228, 203, 202, 210, 189, 183, 219, 192, 189, 192, 195, 204, 190, 190, 182, 195, 193, 252, 218, 213, 193, 216, 226, 193
Node JS	1220 ± 109	1722, 1218, 1213, 1183, 1190, 1193, 1183, 1195, 1324, 1175, 1173, 1179, 1198, 1230, 1204, 1183, 1188, 1312, 1171, 1170, 1175, 1192, 1180, 1168, 1181
MinGW64	1941 ± 388	8297, 1669, 1792, 1797, 1665, 1663, 1671, 1677, 1655, 1653, 1665, 1663, 1671, 1703, 1660, 1671, 1663, 1657, 1660, 1674, 1651, 1654, 1666, 1660, 1673

Day 04

Platform	Average (ms)	Measurements (ms)
JVM	468 ± 94	871, 478, 444, 453, 453, 444, 437, 437, 435, 495, 532, 451, 441, 438, 480, 443, 434, 438, 437, 466, 433, 434, 433, 437, 439
Node JS	4062 ± 175	4034, 4232, 3965, 3991, 3945, 3925, 3902, 4052, 4000, 4194, 4072, 4503, 4271, 4179, 4010, 3900, 3904, 3882, 4006, 4349, 4346, 4242, 3871, 3873, 3875
MinGW64	32708 ± 1320	35233, 30791, 31812, 31736, 31602, 33115, 33165, 33091, 32364, 32477, 31648, 31795, 31508, 32222, 36365, 34493, 35487, 32006, 32152, 32065, 32498, 32308, 32035, 32885, 32859

Day 05

TBD

Day 06

Platform	Average (ms)	Measurements (ms)
JVM	266 ± 31	331, 269, 315, 275, 268, 255, 256, 259, 261, 250, 255, 212, 247, 253, 244, 252, 262, 274, 275, 278, 267, 362, 217, 243, 248
Node JS	3501 ± 592	3437, 3388, 3461, 3265, 3460, 3650, 3261, 3244, 3428, 3311, 3200, 3280, 3246, 3235, 3365, 3227, 3373, 6277, 3796, 3262, 3330, 3392, 3234, 3480, 3920
MinGW64	4350 ± 171	4234, 4251, 4397, 4254, 4250, 4459, 4690, 4260, 4425, 4364, 4730, 4581, 4393, 4182, 4296, 4241, 4264, 4218, 4245, 4791, 4239, 4340, 4219, 4226, 4185

Day 07

Platform	Average (ms)	Measurements (ms)
JVM	65 ± 54	315, 87, 80, 45, 52, 58, 48, 52, 81, 100, 56, 66, 60, 66, 50, 50, 41, 47, 47, 43, 27, 28, 30, 40, 31
Node JS	1393 ± 138	1760, 1383, 1334, 1305, 1318, 1351, 1321, 1488, 1447, 1457, 1993, 2127, 1937, 1852, 1795, 1785, 1834, 1747, 1494, 1406, 1704, 1834, 1698, 1991, 1686
MinGW64	3102 ± 307	2914, 2908, 2973, 3283, 2964, 2969, 2879, 2974, 2924, 3143, 3219, 2902, 2814, 2820, 2904, 2822, 2814, 2970, 3447, 3755, 3640, 3406, 3269, 2895, 3927

Day 08

TBD

Day 09

Platform	Average (ms)	Measurements (ms)
JVM	182 ± 104	649, 259, 310, 200, 144, 145, 141, 137, 141, 138, 139, 136, 142, 139, 139, 139, 141, 187, 154, 156, 184, 159, 155, 143, 149
Node JS	3925 ± 259	3981, 3817, 3810, 3811, 3882, 4461, 3758, 3773, 3732, 3856, 3775, 3722, 3712, 3690, 4047, 4197, 4069, 3940, 3938, 3805, 4879, 4016, 3847, 3732, 3848
MinGW64	5856 ± 409	6606, 6919, 6193, 6023, 5676, 6326, 5999, 6255, 6022, 5885, 5979, 5425, 5699, 6460, 5595, 5844, 5714, 5603, 5540, 5424, 5430, 5448, 5446, 5434, 5446