병목 경로 최적화: COW, 인라이닝 및 특수화

// Measure → Identify → Optimise → Re-measure
// A 10ms function called once is less important than
// a 0.1ms function called 10,000 times per frame

var a = [1, 2, 3, 4, 5]
var b = a         // no copy yet, shared buffer
b.append(6)       // copy happens here (b mutates)
print(a.count)    // 5 — a unchanged

struct Matrix {
  private class Storage { var data: [[Double]] }
  private var storage = Storage()
  private mutating func ensureUnique() {
    if !isKnownUniquelyReferenced(&storage) {
      storage = Storage(data: storage.data)  // copy on write
    }
  }
  mutating func set(row: Int, col: Int, value: Double) {
    ensureUnique()
    storage.data[row][col] = value
  }
}

@inline(__always)
func clamp(_ value: Float, _ min: Float, _ max: Float) -> Float {
  return Swift.max(min, Swift.min(max, value))
}

@inline(never)
func handleUnexpectedError(_ error: Error) {
  // Large error-handling block — never inline to keep hot path small
}

@_specialize(where T == Float)
@_specialize(where T == Double)
func normalize<T: FloatingPoint>(_ values: [T]) -> [T] {
  let max = values.max()!
  return values.map { $0 / max }
}

// Xcode: Build Settings → Swift Compiler → Code Generation
// Optimization Level: Optimize for Speed (-O)
// Compilation Mode: Whole Module
// Enables cross-file inlining and dead code elimination

// HOT PATH:
for _ in 0..<1_000_000 {
  let obj = HeavyClass()  // alloc + dealloc + ARC overhead
}
// BETTER: allocate once outside the loop
let obj = HeavyClass()
for _ in 0..<1_000_000 { obj.process() }

var nums = ContiguousArray<Float>(repeating: 0, count: 10_000)
// Faster iteration than Array<Float> for bridged-to-ObjC types

// Dynamic dispatch (slower for hot path):
func process(_ drawable: any Drawable) { drawable.draw() }
// Static dispatch after specialization:
func process<T: Drawable>(_ drawable: T) { drawable.draw() }
// Compiler may devirtualize and inline

func testNormalizePerformance() {
  let data = Array(0..<100_000).map { Float($0) }
  measure {
    _ = normalize(data)
  }
}