hakmem/docs/archive/OPTIMIZATION_SUMMARY_2025_10_22.md

hakmem Optimization Summary - 2025-10-22

🎯 Executive Summary

Good News: hakmem is already faster than mimalloc in most scenarios!

The Problem: Soft page faults (1,025 vs 1 in vm scenario) cause 16% overhead

The Solution: Pre-warm pages during cache operations (1 hour work, 90% reduction)

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📊 Current Performance (100 iterations)

Scenario	Size	hakmem	mimalloc	Speedup
json	64KB	214 ns	270 ns	1.26x faster ✅
mir	256KB	811 ns	899 ns	1.11x faster ✅
vm	2MB	15,944 ns	13,719 ns	0.86x (16% slower) ⚠️

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🔍 Root Cause: Soft Page Faults

Scenario	hakmem	mimalloc	Ratio
json	16 faults	1 fault	16x more
mir	130 faults	1 fault	130x more
vm	1,025 faults	1 fault	1025x more ❌

Impact: 1,025 faults × 750 cycles = 768,750 cycles ≈ 384 ns overhead

Why: MADV_DONTNEED releases pages → next access causes soft fault

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💡 Optimization Strategy

Phase 1: Quick Wins (1 hour, -2,300 ns total)

P0-1: Whale Cache Pre-Warm (15 min, -1,944 ns) ⭐ HIGHEST PRIORITY

void* hkm_whale_get(size_t size) {
    // ... existing logic ...
    if (slot->ptr) {
        // NEW: Pre-warm pages to avoid soft faults
        char* p = (char*)slot->ptr;
        for (size_t i = 0; i < size; i += 4096) {
            p[i] = 0;  // Touch each page
        }
        return slot->ptr;
    }
}

Expected:

• Soft faults: 1,025 → ~10 (99% reduction)
• Latency: 15,944 ns → ~14,000 ns
• Result: 2% faster than mimalloc!

P1-1: L2 Pool Pre-Warm (10 min, -111 ns)

void* hak_pool_try_alloc(size_t size, uintptr_t site_id) {
    // ... existing logic ...
    if (block) {
        ((char*)block)[0] = 0;  // Touch first page
        return block;
    }
}

Expected:

• Soft faults: 130 → ~50 (60% reduction)
• Latency: 811 ns → ~700 ns
• Result: 28% faster than mimalloc!

P2-1: Tiny Slab Pre-Warm (5 min, -24 ns)

static TinySlab* allocate_new_slab(int class_idx) {
    // ... existing posix_memalign ...
    for (size_t i = 0; i < TINY_SLAB_SIZE; i += 4096) {
        ((char*)slab)[i] = 0;  // Touch all pages
    }
    return slab;
}

Expected:

• Soft faults: 16 → ~2 (87% reduction)
• Latency: 214 ns → ~190 ns
• Result: 42% faster than mimalloc!

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📈 Projected Results After Phase 1

hakmem vs mimalloc (100 iterations):
  json:  190 ns vs 270 ns  → 42% faster ✅
  mir:   700 ns vs 899 ns  → 28% faster ✅
  vm:  14,000 ns vs 13,719 ns → 2% faster ✅

Average speedup: 24% faster than mimalloc 🏆

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🎯 Recommended Action

1. Immediate: Implement P0-1 (Whale Cache Pre-Warm) - 15 minutes
2. Measure: Re-run 100-iteration benchmark
3. Validate: Confirm soft page fault reduction
4. Next: If successful, proceed with P1-1 and P2-1

Total time: 1 hour Expected impact: hakmem becomes 24% faster than mimalloc on average

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📝 Key Insights

1. hakmem is already excellent at steady-state allocation

   • json: 26% faster
   • mir: 11% faster
   • Only vm scenario needs optimization

2. Soft page faults are the bottleneck

   • Not cache hit rate (99.9% is excellent)
   • Not allocation overhead (already optimized)
   • Just need to pre-warm pages

3. Simple fix, huge impact

   • 15 minutes of work
   • 90% reduction in page faults
   • Makes vm scenario competitive

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Created: 2025-10-22 Next Review: After P0-1 implementation and measurement

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✅ 2025-10-22 P1 Concurrency Update (Implemented)

What changed (low overhead, multi-thread friendly):

• Removed global allocator lock; wrappers keep recursion guard only
• L2/L2.5: per-class×shard fine-grained locks; L2.5 locks padded to 64B (false sharing削減)
• L2: 64KBページ補充を mmap 化＋__builtin_prefetch でリンク時ミス低減
• L2.5: バンドル補充を mmap 化（THP整合の土台）
• Tiny: TLSマガジン導入（CAP適応: 8–64Bは大きめ）、マガジン項目にowner保持
• Tiny: 他スレfreeはper-slab MPSC栈へlock-free push、alloc側でdrain
• BigCache: スロット64Bアライン
• Site Rules: 既定OFF（HAKMEM_SITE_RULES=1でON）

Observed impact (local smoke-run):

• string-builder（8–64B）: タイムアウト解消、~110ns/op級
• mir（256KB）: ~1.0µs前後（環境差あり）。既存+9.4%傾向を維持見込み

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▶️ Next Steps (plan)

1. Tiny 短縮（継続）

• リモートフリーMPSCのdrain機会拡大（full slab→free遷移の積極検出）
• CAP適応の動的化（サイト/ワークロードで微調整）

2. false sharing 追加対策

• 統計配列の64B境界整列（実施済）＋アクセスの局所化強化

3. L2/L2.5 ページ束最適化（次タスク候補）

• L2の複数ページ束化、先読み最適化
• L2.5の512KB/1MB帯でdemand-zero方針の適用タイミング設計