ファンエンデボアスソートで配列を並び替える
ファンエンデボアスソートを使用する
ファンエンデボアスソート (van emde boas sort) は、整数宇宙 [0, U) 上のファン・エムデ・ボアス木(vEB 木)へキーを挿入し、最小値から順に後続(successor)をたどって取り出す非比較ソートである。
vEB 木は最小値・最大値を定数時間で返し、挿入・後続探索を \(O(\log \log U)\) で行う。そのため n 個のキーを整列する全体の時間は \(O(n \log \log U)\) になる。宇宙サイズ U が入力長 n に近い整数データでは、
比較ソートの \(\Omega(n \log n)\) より漸近的に有利になりうる。
構造の要点は、宇宙を高位桁と低位桁に再帰的に分割することである。U = 2^{2k}(またはそれに近い 2 の冪)のとき、各ノードは次を持つ。
- min / max: その部分宇宙に含まれる最小・最大キー(木全体から切り離して保持する)。
- cluster: 低位桁用の部分木を \(\sqrt{U}\) 本(実際には上側平方根本)。キー
xの高位high(x)がクラスタ番号、低位low(x)がクラスタ内の位置になる。 - summary: 「どのクラスタが空でないか」を表す、宇宙サイズ \(\sqrt{U}\) の vEB 木。
空でないクラスタだけを遅延確保すれば、疎なキー集合でも全宇宙分の配列を一気に確保しなくてよい。重複キーは出現回数を別配列で数え、vEB 木にはユニークなオフセットだけを入れる。
- 値域の正規化: 最小値
minを引き、オフセット0 … max-minへ写す。宇宙サイズUは値域幅以上の最小の 2 の冪(ただし 2 以上)とする。 - 集計と挿入: 各オフセットの出現回数を数え、回数が正のキーだけを vEB 木へ挿入する。
- 昇順取り出し: 木の最小値から始め、
successorで次のキーへ進みながら、出現回数ぶん出力配列へ書き戻す。
procedure veb_insert(V, x)
if V.min = NIL then
V.min = V.max = x; return
if x < V.min then swap x with V.min
if V.u > 2 then
h = high(x); l = low(x)
if V.cluster[h] is empty then
veb_insert(V.summary, h)
V.cluster[h].min = V.cluster[h].max = l
else
veb_insert(V.cluster[h], l)
if x > V.max then V.max = x
procedure veb_successor(V, x)
if V.u = 2 then
if x = 0 and V.max = 1 then return 1 else return NIL
if V.min != NIL and x < V.min then return V.min
h = high(x); l = low(x)
if low-part of cluster h has a key > l then
return index(h, veb_successor(V.cluster[h], l))
succ = veb_successor(V.summary, h)
if succ = NIL then return NIL
return index(succ, V.cluster[succ].min)
procedure van_emde_boas_sort(A)
if length(A) <= 1 then return
minVal = minimum(A); maxVal = maximum(A)
span = maxVal - minVal + 1
count[0..span-1] = 0
for each x in A
count[x - minVal] = count[x - minVal] + 1
U = next_power_of_two(max(span, 2))
V = empty vEB tree with universe U
for v from 0 to span - 1
if count[v] > 0 then veb_insert(V, v)
idx = 0; cur = V.min
while cur != NIL
repeat count[cur] times
A[idx] = minVal + cur; idx = idx + 1
cur = veb_successor(V, cur)
キー同士の大小比較は行わず、ビット分割と再帰的な summary 操作で順序を決める。同値は集計配列側でまとめて出力するため、入力を左から数えた実装では安定ソートになる。一方で補助構造は宇宙サイズに依存し、U が極端に大きいとメモリと定数倍が膨らむ。
類似アルゴリズムとの相違点
カウンティングソート や 鳩の巣ソート は値域 k に対し
\(O(n + k)\) で直接バケットを走査する。本アルゴリズムはバケット配列を線形走査する代わりに、vEB 木の
\(O(\log \log U)\) 操作で「次に小さいキー」だけをたどる。二分木ソート が比較で二分探索木を育てるのに対し、
ここでは宇宙のビット分割が順序を決める。トライソート の桁トライとも「桁で空間を割る」点は近いが、
summary による空クラスタのスキップが vEB 木の特徴である。
時間計算量および空間計算量を計測する
| Size | Average time (s) | Maximum time (s) | Average memory (KiB) | Maximum memory (KiB) |
|---|---|---|---|---|
| 256 | 0.000015 | 0.000102 | 27 | 27 |
| 512 | 0.000029 | 0.000072 | 55 | 55 |
| 1024 | 0.000060 | 0.000177 | 111 | 111 |
| 2048 | 0.000118 | 0.000192 | 222 | 222 |
| 4096 | 0.000225 | 0.000517 | 429 | 429 |
| 8192 | 0.000509 | 0.000777 | 859 | 859 |
| 16384 | 0.000929 | 0.001338 | 1734 | 1734 |
| 32768 | 0.001878 | 0.002643 | 3469 | 3469 |
| 65536 | 0.004236 | 0.005690 | 7185 | 7185 |
| 131072 | 0.008490 | 0.016143 | 14371 | 14371 |
| 262144 | 0.017631 | 0.026202 | 28695 | 28695 |
計測に使用したコードを表示する
#!/usr/bin/env swift
import Foundation
// This standalone Swift driver creates the same temporary Docker build
// context as the former shell wrapper. The benchmark program itself remains
// embedded below so readers can copy one complete, reproducible file.
struct BenchmarkError: Error, CustomStringConvertible {
let message: String
var description: String { message }
init(_ message: String) {
self.message = message
}
}
func runCommand(_ executable: String, _ arguments: [String]) throws {
let process = Process()
process.executableURL = URL(fileURLWithPath: "/usr/bin/env")
process.arguments = [executable] + arguments
process.standardInput = FileHandle.standardInput
process.standardOutput = FileHandle.standardOutput
process.standardError = FileHandle.standardError
do {
try process.run()
} catch {
throw BenchmarkError("Could not start \(executable): \(error)")
}
process.waitUntilExit()
guard process.terminationStatus == 0 else {
throw BenchmarkError(
"Command failed (\(process.terminationStatus)): " +
"\(executable) \(arguments.joined(separator: " "))"
)
}
}
do {
// The UUID avoids collisions when two benchmark copies are run at once.
let workdir = FileManager.default.temporaryDirectory
.appendingPathComponent("swift-sort-benchmark-\(UUID().uuidString)")
try FileManager.default.createDirectory(at: workdir, withIntermediateDirectories: true)
defer { try? FileManager.default.removeItem(at: workdir) }
// A raw Swift string is used so the nested main.swift keeps its own
// interpolation expressions such as \(seed) until Docker compiles it.
let dockerfile = #"""
FROM swift:6.0
WORKDIR /app
RUN cat > alloc_track.c <<'ALLOC'
#define _GNU_SOURCE
#include <dlfcn.h>
#include <malloc.h>
#include <stdatomic.h>
#include <stddef.h>
#include <stdint.h>
#include <stdlib.h>
#include <string.h>
static atomic_size_t live_bytes = 0;
static atomic_size_t peak_bytes = 0;
static void *(*real_malloc)(size_t) = NULL;
static void *(*real_calloc)(size_t, size_t) = NULL;
static void *(*real_realloc)(void *, size_t) = NULL;
static void (*real_free)(void *) = NULL;
static void init_reals(void) {
if (real_malloc) {
return;
}
real_malloc = (void *(*)(size_t))dlsym(RTLD_NEXT, "malloc");
real_calloc = (void *(*)(size_t, size_t))dlsym(RTLD_NEXT, "calloc");
real_realloc = (void *(*)(void *, size_t))dlsym(RTLD_NEXT, "realloc");
real_free = (void (*)(void *))dlsym(RTLD_NEXT, "free");
}
static void record_alloc(size_t size) {
size_t live = atomic_fetch_add(&live_bytes, size) + size;
size_t peak = atomic_load(&peak_bytes);
while (live > peak) {
if (atomic_compare_exchange_weak(&peak_bytes, &peak, live)) {
break;
}
}
}
void alloc_track_reset_peak(void) {
atomic_store(&peak_bytes, atomic_load(&live_bytes));
}
size_t alloc_track_live(void) { return atomic_load(&live_bytes); }
size_t alloc_track_peak(void) { return atomic_load(&peak_bytes); }
void *malloc(size_t size) {
init_reals();
void *p = real_malloc(size);
if (p) {
record_alloc(malloc_usable_size(p));
}
return p;
}
void *calloc(size_t nmemb, size_t size) {
init_reals();
void *p = real_calloc(nmemb, size);
if (p) {
record_alloc(malloc_usable_size(p));
}
return p;
}
void *realloc(void *ptr, size_t size) {
init_reals();
size_t old_size = 0;
if (ptr) {
old_size = malloc_usable_size(ptr);
}
void *p = real_realloc(ptr, size);
if (p) {
atomic_fetch_sub(&live_bytes, old_size);
record_alloc(malloc_usable_size(p));
} else if (size == 0) {
atomic_fetch_sub(&live_bytes, old_size);
}
return p;
}
void free(void *ptr) {
init_reals();
if (ptr) {
atomic_fetch_sub(&live_bytes, malloc_usable_size(ptr));
real_free(ptr);
}
}
ALLOC
RUN cat > main.swift <<'SWIFT'
import Foundation
#if canImport(Glibc)
import Glibc
#elseif canImport(Darwin)
import Darwin
#endif
@_silgen_name("alloc_track_live") func alloc_track_live() -> Int
@_silgen_name("alloc_track_peak") func alloc_track_peak() -> Int
@_silgen_name("alloc_track_reset_peak") func alloc_track_reset_peak()
extension UnsafeMutableBufferPointer where Element == Int {
func swapAt(_ i: Int, _ j: Int) {
let t = self[i]; self[i] = self[j]; self[j] = t
}
}
let MIN_POWER: Int = 8
let MAX_POWER: Int = 18
let RUNS: Int = 8192
final class VebTree {
var universe: Int
var min: Int?
var max: Int?
var summary: VebTree?
var cluster: [VebTree?]
init(universe: Int) {
assert(universe > 0 && (universe & (universe - 1)) == 0)
assert(universe >= 2)
self.universe = universe
self.min = nil
self.max = nil
if universe == 2 {
self.summary = nil
self.cluster = []
return
}
let lower = VebTree.lower_sqrt(universe)
let upper = universe / lower
self.summary = VebTree(universe: upper)
self.cluster = Array(repeating: nil, count: upper)
}
static func lower_sqrt(_ universe: Int) -> Int {
1 << (universe.trailingZeroBitCount / 2)
}
func high(_ x: Int) -> Int {
x / VebTree.lower_sqrt(universe)
}
func low(_ x: Int) -> Int {
x % VebTree.lower_sqrt(universe)
}
func index(_ high: Int, _ low: Int) -> Int {
high * VebTree.lower_sqrt(universe) + low
}
func minimum() -> Int? {
min
}
func maximum() -> Int? {
max
}
func cluster_mut(_ i: Int) -> VebTree {
let lower = VebTree.lower_sqrt(universe)
if cluster[i] == nil {
cluster[i] = VebTree(universe: lower)
}
return cluster[i]!
}
func empty_insert(_ x: Int) {
min = x
max = x
}
func insert(_ x: Int) {
var x = x
if min == nil {
empty_insert(x)
return
}
if x < min! {
let old_min = min!
min = x
x = old_min
}
if universe > 2 {
let h = high(x)
let l = low(x)
if cluster[h]?.minimum() == nil {
summary!.insert(h)
cluster_mut(h).empty_insert(l)
} else {
cluster_mut(h).insert(l)
}
}
if x > max! {
max = x
}
}
func successor(_ x: Int) -> Int? {
if universe == 2 {
if x == 0 && max == 1 {
return 1
} else {
return nil
}
}
if let min = min {
if x < min {
return min
}
} else {
return nil
}
let h = high(x)
let l = low(x)
let max_low = cluster[h]?.maximum()
if let m = max_low, l < m {
let offset = cluster[h]!.successor(l)!
return index(h, offset)
}
guard let succ_cluster = summary!.successor(h) else {
return nil
}
let offset = cluster[succ_cluster]!.minimum()!
return index(succ_cluster, offset)
}
}
func next_power_of_two(_ n: Int) -> Int {
if n <= 1 {
return 1
}
var v = UInt(bitPattern: n - 1)
v |= v >> 1
v |= v >> 2
v |= v >> 4
v |= v >> 8
v |= v >> 16
v |= v >> 32
return Int(bitPattern: v &+ 1)
}
func van_emde_boas_sort(_ a: inout [Int]) {
a.withUnsafeMutableBufferPointer { van_emde_boas_sort($0) }
}
func van_emde_boas_sort(_ a: UnsafeMutableBufferPointer<Int>) {
if a.count <= 1 {
return
}
var min = a[0]
var max = a[0]
for i in 1..<a.count {
if a[i] < min { min = a[i] }
if a[i] > max { max = a[i] }
}
let span = max - min + 1
var count = [Int](repeating: 0, count: span)
for i in 0..<a.count {
count[a[i] - min] += 1
}
let universe = Swift.max(next_power_of_two(span), 2)
let tree = VebTree(universe: universe)
for offset in 0..<count.count {
if count[offset] > 0 {
tree.insert(offset)
}
}
var idx = 0
var cur = tree.minimum()
while let v = cur {
let value = min + v
for _ in 0..<count[v] {
a[idx] = value
idx += 1
}
cur = tree.successor(v)
}
}
func benchmark_sort(_ array: inout [Int]) {
van_emde_boas_sort(&array)
}
func is_non_decreasing(_ a: [Int]) -> Bool {
guard a.count >= 2 else { return true }
for i in 1..<a.count {
if a[i - 1] > a[i] { return false }
}
return true
}
func same_multiset(_ a: [Int], _ b: [Int]) -> Bool {
if a.count != b.count {
return false
}
var left = a
var right = b
left.sort()
right.sort()
return left == right
}
func check_correctness_case(_ label: String, _ input: [Int]) {
var input = input
let original = input
benchmark_sort(&input)
if !is_non_decreasing(input) {
fatalError("correctness case \(label): output is not sorted")
}
if !same_multiset(input, original) {
fatalError("correctness case \(label): elements were lost or added")
}
}
// Skip cases larger than the algorithm's measured size cap (MAX_POWER). That
// cap exists because larger inputs are impractically slow; forcing them here
// would stall the published measurement script before any table rows print.
func check_correctness_case_within_limit(_ label: String, _ input: [Int]) {
if input.count > (1 << MAX_POWER) {
return
}
check_correctness_case(label, input)
}
func few_unique_values(_ size: Int, _ unique: Int, _ seed: UInt64) -> [Int] {
var state = seed
var result = [Int]()
result.reserveCapacity(size)
for _ in 0..<size {
state ^= state << 13
state ^= state >> 7
state ^= state << 17
result.append(Int(state % UInt64(unique)) + 1)
}
return result
}
func run_correctness_checks() {
check_correctness_case("empty", [])
check_correctness_case("single", [42])
check_correctness_case("duplicates", [3, 1, 3, 2, 1, 2])
check_correctness_case("sorted", [1, 2, 3, 4, 5])
check_correctness_case("reverse", [5, 4, 3, 2, 1])
check_correctness_case("all_equal", [7, 7, 7, 7])
check_correctness_case("skewed_range", [1_000_000, 2, 1_000_001, 1, 999_999])
// Static-buffer Grail skips the in-buffer build when key collection is sparse
// (ideal_buffer = false). Exercising that path catches regressions in buffer gating.
check_correctness_case(
"few_keys_len16",
[2, 2, 2, 2, 2, 2, 2, 2, 4, 3, 1, 2, 3, 4, 1, 4]
)
// Seed 0 is a fixed point of the xorshift below, so it would degenerate into
// yet another all-equal case instead of a 4-value mix. Start at 1.
for seed in 1...32 {
check_correctness_case(
"few_keys_len32_seed_\(seed)",
few_unique_values(32, 4, UInt64(seed))
)
}
// Small-input cutoffs (insertion sort below 32 elements, etc.) hide duplicate-key
// bugs in the recursive path, so repeat the duplicate cases at the smallest
// benchmark size, which every algorithm must handle within reasonable time.
check_correctness_case("all_equal_len256", [Int](repeating: 7, count: 256))
for seed in 1...4 {
check_correctness_case(
"few_keys_len256_seed_\(seed)",
few_unique_values(256, 4, UInt64(seed))
)
}
// Blit's equal-key second sweep used to copy the whole range into a fixed
// 512-element swap; lengths above that must still sort without panicking.
// Respect MAX_POWER so algorithms with a low measured-size cap (slow,
// sleep) do not hang here for minutes or months.
check_correctness_case_within_limit("all_equal_len600", [Int](repeating: 7, count: 600))
for seed in 1...4 {
check_correctness_case_within_limit(
"few_keys_len2048_seed_\(seed)",
few_unique_values(2048, 4, UInt64(seed))
)
}
}
func shuffled(_ size: Int, seed: UInt64) -> [Int] {
guard size > 0 else { return [] }
var v = Array(1...size)
var state = seed
if size > 1 {
for i in stride(from: size - 1, through: 1, by: -1) {
state ^= state << 13
state ^= state >> 7
state ^= state << 17
let j = Int(state % UInt64(i + 1))
v.swapAt(i, j)
}
}
return v
}
func micros(_ d: Duration) -> UInt64 {
let c = d.components
let fromSeconds = UInt64(c.seconds) * 1_000_000
let fromAttos = UInt64(max(0, c.attoseconds / 1_000_000_000_000))
return fromSeconds + fromAttos
}
func padLeft(_ value: String, _ width: Int) -> String {
if value.count >= width {
return value
}
return String(repeating: " ", count: width - value.count) + value
}
func formatSeconds(_ micros: UInt64) -> String {
let whole = micros / 1_000_000
let frac = micros % 1_000_000
let fracStr = padLeft(String(frac), 6).replacingOccurrences(of: " ", with: "0")
return "\(whole).\(fracStr)"
}
func input_array(_ size: Int, seed: UInt64) -> [Int] {
shuffled(size, seed: seed)
}
/// Peak heap growth during `benchmark_sort`, in bytes (explicit buffers such as swap).
/// Kept in bytes so the parent can average before rounding; converting to KiB here
/// would truncate sub-KiB buffers to 0 in every run and hide them from the average.
func run_once(size: Int, seed: Int) -> (UInt64, Int) {
var array = input_array(size, seed: UInt64(seed))
let baseBytes = alloc_track_live()
alloc_track_reset_peak()
let start = ContinuousClock.now
benchmark_sort(&array)
let elapsed = ContinuousClock.now - start
let peakBytes = alloc_track_peak()
let auxBytes = max(0, peakBytes - baseBytes)
let expected: [Int] = size > 0 ? Array(1...size) : []
if array != expected {
fatalError("sort failed with seed \(seed) for size \(size)")
}
return (micros(elapsed), auxBytes)
}
func run_child(_ args: [String]) {
let size = Int(args[2])!
let seed = Int(args[3])!
let (elapsedUs, mem) = run_once(size: size, seed: seed)
print("\(elapsedUs) \(mem)")
}
let args = CommandLine.arguments
if args.count > 1 && args[1] == "--run-once" {
run_child(args)
} else {
run_correctness_checks()
let tableHeader =
"| \(padLeft("Size", 10)) | " +
"\(padLeft("Average time (s)", 16)) | " +
"\(padLeft("Maximum time (s)", 16)) | " +
"\(padLeft("Average memory (KiB)", 20)) | " +
"\(padLeft("Maximum memory (KiB)", 20)) |"
print(tableHeader)
print("|-----------:|-----------------:|-----------------:|---------------------:|---------------------:|")
for power in MIN_POWER...MAX_POWER {
let size = 1 << power
var totalTime: UInt64 = 0
var maxTime: UInt64 = 0
var totalMem = 0
var maxMem = 0
for seed in 1...RUNS {
let process = Process()
process.executableURL = URL(fileURLWithPath: args[0])
process.arguments = ["--run-once", "\(size)", "\(seed)"]
let stdout = Pipe()
let stderr = Pipe()
process.standardOutput = stdout
process.standardError = stderr
do {
try process.run()
} catch {
fatalError("failed to run benchmark child process: \(error)")
}
process.waitUntilExit()
if process.terminationStatus != 0 {
let err = String(data: stderr.fileHandleForReading.readDataToEndOfFile(), encoding: .utf8) ?? ""
fatalError("benchmark child process failed: \(err)")
}
let data = stdout.fileHandleForReading.readDataToEndOfFile()
let stdoutText = String(data: data, encoding: .utf8) ?? ""
let fields = stdoutText.split(whereSeparator: \.isWhitespace)
guard fields.count >= 2,
let elapsedUs = UInt64(fields[0]),
let auxMem = Int(fields[1]) else {
fatalError("invalid child process output: \(stdoutText)")
}
totalTime += elapsedUs
if elapsedUs > maxTime {
maxTime = elapsedUs
}
totalMem += auxMem
if auxMem > maxMem {
maxMem = auxMem
}
}
let avgTime = totalTime / UInt64(RUNS)
// Memory is summed in bytes and converted to KiB once, after averaging.
let avgMemKb = totalMem / RUNS / 1024
let maxMemKb = maxMem / 1024
let tableRow =
"| \(padLeft(String(size), 10)) | " +
"\(padLeft(formatSeconds(avgTime), 16)) | " +
"\(padLeft(formatSeconds(maxTime), 16)) | " +
"\(padLeft(String(avgMemKb), 20)) | " +
"\(padLeft(String(maxMemKb), 20)) |"
print(tableRow)
}
}
SWIFT
RUN clang -O2 -fPIC -shared alloc_track.c -o liballoc_track.so -ldl
RUN swiftc -Ounchecked -whole-module-optimization \
main.swift \
-o swift-benchmark \
-L. -lalloc_track \
-Xlinker -rpath -Xlinker /app
ENV LD_PRELOAD=/app/liballoc_track.so
CMD ["./swift-benchmark"]
"""#
try dockerfile.write(
to: workdir.appendingPathComponent("Dockerfile"),
atomically: true,
encoding: .utf8
)
// Keeping build and run as separate child processes preserves Docker's
// normal output and the original image tag used by the benchmark skill.
try runCommand("docker", ["build", "-t", "swift-benchmark", workdir.path])
try runCommand("docker", ["run", "--rm", "--init", "swift-benchmark"])
} catch {
fputs("\(error)\n", stderr)
exit(1)
}