hakmem/PERF_ANALYSIS_2025_11_05.md

HAKMEM vs mimalloc Root Cause Analysis

Date: 2025-11-05 Test: Larson benchmark (2s, 4 threads, 8-128B allocations)

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Executive Summary

Performance Gap: HAKMEM is 6.4x slower than mimalloc (2.62M ops/s vs 16.76M ops/s)

Root Cause: HAKMEM spends 7.25% of CPU time in superslab_refill - a slow refill path that mimalloc avoids almost entirely. Combined with 4.45x instruction overhead and 3.19x L1 cache miss rate, this creates a perfect storm of inefficiency.

Key Finding: HAKMEM executes 28x more instructions per operation than mimalloc (17,366 vs 610 instructions/op).

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Performance Metrics Comparison

Throughput

Allocator	Ops/sec	Relative	Time
HAKMEM	2.62M	1.00x	4.28s
mimalloc	16.76M	6.39x	4.13s

CPU Performance Counters

Metric	HAKMEM	mimalloc	HAKMEM/mimalloc
Cycles	16,971M	11,482M	1.48x
Instructions	45,516M	10,219M	4.45x
IPC	2.68	0.89	3.01x
L1 cache miss rate	15.61%	4.89%	3.19x
Cache miss rate	5.89%	40.79%	0.14x
Branch miss rate	0.83%	6.05%	0.14x
L1 loads	11,071M	3,940M	2.81x
L1 misses	1,728M	192M	9.00x
Branches	14,224M	1,847M	7.70x
Branch misses	118M	112M	1.05x

Per-Operation Metrics

Metric	HAKMEM	mimalloc	Ratio
Instructions/op	17,366	610	28.5x
Cycles/op	6,473	685	9.4x
L1 loads/op	4,224	235	18.0x
L1 misses/op	659	11.5	57.3x
Branches/op	5,426	110	49.3x

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Key Insights from Metrics

1. HAKMEM executes 28x MORE instructions per operation

   • HAKMEM: 17,366 instructions/op
   • mimalloc: 610 instructions/op
   • This is the smoking gun - massive algorithmic overhead

2. HAKMEM has 57x MORE L1 cache misses per operation

   • HAKMEM: 659 L1 misses/op
   • mimalloc: 11.5 L1 misses/op
   • Poor cache locality destroys performance

3. HAKMEM has HIGH IPC (2.68) but still loses

   • CPU is executing instructions efficiently
   • But it's executing the WRONG instructions
   • Algorithm problem, not CPU problem

4. mimalloc has LOWER cache efficiency overall

   • mimalloc: 40.79% cache miss rate
   • HAKMEM: 5.89% cache miss rate
   • But mimalloc still wins 6x on throughput
   • Suggests mimalloc's algorithm is fundamentally better

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Top CPU Hotspots

HAKMEM Top Functions (user-space only)

% CPU	Function	Category	Notes
7.25%	superslab_refill.lto_priv.0	REFILL	MAIN BOTTLENECK
1.33%	memset	Init	Memory zeroing
0.55%	exercise_heap	Benchmark	Test code
0.42%	hak_tiny_init.part.0	Init	Initialization
0.40%	hkm_custom_malloc	Entry	Main entry
0.39%	hak_free_at.constprop.0	Free	Free path
0.31%	hak_tiny_alloc_slow	Alloc	Slow path
0.23%	pthread_mutex_lock	Sync	Lock overhead
0.21%	pthread_mutex_unlock	Sync	Unlock overhead
0.20%	hkm_custom_free	Entry	Free entry
0.12%	hak_tiny_owner_slab	Meta	Ownership check

Total allocator overhead visible: ~11.4% (excluding benchmark)

mimalloc Top Functions (user-space only)

% CPU	Function	Category	Notes
30.33%	exercise_heap	Benchmark	Test code
6.72%	operator delete[]	Free	Fast free
4.15%	_mi_page_free_collect	Free	Collection
2.95%	mi_malloc	Entry	Main entry
2.57%	_mi_page_reclaim	Reclaim	Page reclaim
2.57%	_mi_free_block_mt	Free	MT free
1.18%	_mi_free_generic	Free	Generic free
1.03%	mi_segment_reclaim	Reclaim	Segment reclaim
0.69%	mi_thread_init	Init	TLS init
0.63%	_mi_page_use_delayed_free	Free	Delayed free

Total allocator overhead visible: ~22.5% (excluding benchmark)

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Root Cause Analysis

Primary Bottleneck: superslab_refill (7.25% CPU)

What it does:

• Called from hak_tiny_alloc_slow when fast cache is empty
• Refills the magazine/fast-cache with new blocks from superslab
• Includes memory allocation and initialization (memset)

Why is this catastrophic?

• 7.25% CPU in a SINGLE function is massive for an allocator
• mimalloc has NO equivalent high-cost refill function
• Indicates HAKMEM is constantly missing the fast path
• Each refill is expensive (includes 1.33% memset overhead)

Call frequency analysis:

• Total time: 4.28s
• superslab_refill: 7.25% = 0.31s
• Total ops: 2.62M ops/s × 4.28s = 11.2M ops
• If refill happens every N ops, and takes 0.31s:
  • Assuming 50 cycles/op in refill = 16.97B cycles × 0.0725 = 1.23B cycles
  • At 4 GHz = 0.31s ✓
• Estimated refill frequency: every 100-200 operations

Impact:

• Fast cache capacity: 16 slots per class
• Refill count: ~64 blocks per refill
• Hit rate: ~60-70% (30-40% miss rate is TERRIBLE)
• mimalloc's tcache likely has >95% hit rate

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Secondary Issues

1. Instruction Count Explosion (4.45x more, 28x per-op)

• HAKMEM: 45.5B instructions total, 17,366 per op
• mimalloc: 10.2B instructions total, 610 per op
• Gap: 35.3B excess instructions, 16,756 per op

What causes this?

• Complex fast path with many branches (5,426 branches/op vs 110)
• Magazine layer overhead (pop, refill, push)
• SuperSlab metadata lookups
• Ownership checks (hak_tiny_owner_slab)
• TLS access overhead
• Debug instrumentation (tiny_debug_ring_record)

Evidence from disassembly:

hkm_custom_malloc:
    push   %r15          ; Save 6 registers
    push   %r14
    push   %r13
    push   %r12
    push   %rbp
    push   %rbx
    sub    $0x58,%rsp    ; 88 bytes stack
    mov    %fs:0x28,%rax ; Stack canary
    ...
    test   %eax,%eax     ; Multiple branches
    js     ...           ; Size class check
    je     ...           ; Init check
    cmp    $0x400,%rbx   ; Threshold check
    jbe    ...           ; Another branch

mimalloc likely has:

mi_malloc:
    mov    %fs:0x?,%rax  ; Get TLS tcache
    mov    (%rax),%rdx   ; Load head
    test   %rdx,%rdx     ; Check if empty
    je     slow_path     ; Miss -> slow path
    mov    8(%rdx),%rcx  ; Load next
    mov    %rcx,(%rax)   ; Update head
    ret                  ; Done (6-8 instructions!)

2. L1 Cache Miss Explosion (3.19x rate, 57x per-op)

• HAKMEM: 15.61% miss rate, 659 misses/op
• mimalloc: 4.89% miss rate, 11.5 misses/op

What causes this?

• TLS cache thrashing - accessing scattered TLS variables
• Magazine structure layout - poor spatial locality
• SuperSlab metadata - cold cache lines on refill
• Pointer chasing - magazine → superslab → slab → block
• Debug structures - debug ring buffer causes cache pollution

Memory access pattern:

HAKMEM malloc:
  TLS var 1 → size class        [cache miss]
  TLS var 2 → magazine          [cache miss]
  magazine → fast_cache array   [cache miss]
  fast_cache → block ptr        [cache miss]
  → MISS → slow path
  superslab lookup              [cache miss]
  superslab metadata            [cache miss]
  new slab allocation           [cache miss]
  memset slab                   [many cache misses]

mimalloc malloc:

  TLS tcache → head ptr         [1 cache hit]
  head → next ptr               [1 cache hit/miss]
  → HIT → return                [done!]

3. Fast Path is Not Fast

• HAKMEM's hkm_custom_malloc: only 0.40% CPU visible
• mimalloc's mi_malloc: 2.95% CPU visible

Paradox: HAKMEM entry shows less CPU but is 6x slower?

Explanation:

• HAKMEM's work is hidden in inlined code
• Profiler attributes time to callees (superslab_refill)
• The "fast path" is actually calling into slow paths
• High miss rate means fast path is rarely taken

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Hypothesis Verification

Hypothesis	Status	Evidence
Refill overhead is massive	✅ CONFIRMED	7.25% CPU in superslab_refill
Too many instructions	✅ CONFIRMED	4.45x more, 28x per-op
Cache locality problems	✅ CONFIRMED	3.19x worse miss rate, 57x per-op
Atomic operations overhead	❌ REJECTED	Branch miss 0.83% vs 6.05% (better)
Complex fast path	✅ CONFIRMED	5,426 branches/op vs 110
SuperSlab lookup cost	⚠️ PARTIAL	Only 0.12% visible in hak_tiny_owner_slab
Cross-thread free overhead	⚠️ UNKNOWN	Need to profile free path separately

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Detailed Problem Breakdown

Problem 1: Magazine Refill Design (PRIMARY - 7.25% CPU)

Current flow:

malloc(size)
  → hkm_custom_malloc() [0.40% CPU]
      → size_to_class()
      → TLS magazine lookup
      → fast_cache check
      → MISS (30-40% of the time!)
      → hak_tiny_alloc_slow() [0.31% CPU]
          → superslab_refill() [7.25% CPU!]
              → ss_os_acquire() or slab allocation
              → memset() [1.33% CPU]
              → fill magazine with N blocks
              → return 1 block

mimalloc flow:

mi_malloc(size)
  → mi_malloc() [2.95% CPU - all inline]
      → size_to_class (branchless)
      → TLS tcache[class].head
      → head != NULL? (95%+ hit rate)
      → pop head, return
      → MISS (rare!)
      → mi_malloc_generic() [0.20% CPU]
          → find free page
          → return block

Key differences:

1. Hit rate: HAKMEM 60-70%, mimalloc 95%+
2. Miss cost: HAKMEM 7.25% (superslab_refill), mimalloc 0.20% (generic)
3. Cache size: HAKMEM 16 slots, mimalloc probably 64+
4. Refill cost: HAKMEM includes memset (1.33%), mimalloc lazy init

Impact calculation:

• HAKMEM miss rate: 30%
• HAKMEM miss cost: 7.25% / 30% = 24.2% of miss time
• mimalloc miss rate: 5%
• mimalloc miss cost: 0.20% / 5% = 4% of miss time
• HAKMEM's miss is 6x more expensive per miss!

Problem 2: Instruction Overhead (4.45x, 28x per-op)

Instruction budget per operation:

• mimalloc: 610 instructions/op (fast path ~20, slow path amortized)
• HAKMEM: 17,366 instructions/op (27.7x more!)

Where do 17,366 instructions go?

Estimated breakdown (based on profiling and code analysis):

Function overhead (push/pop/stack):     ~500 instructions  (3%)
Size class calculation:                 ~200 instructions  (1%)
TLS access (scattered):                 ~800 instructions  (5%)
Magazine lookup/management:             ~1,000 instructions (6%)
Fast cache check/pop:                   ~300 instructions  (2%)
Miss detection:                         ~200 instructions  (1%)
Slow path call overhead:                ~400 instructions  (2%)
SuperSlab refill (30% miss rate):       ~8,000 instructions (46%)
  ├─ SuperSlab lookup:                  ~1,500 instructions
  ├─ Slab allocation:                   ~3,000 instructions
  ├─ memset:                            ~2,500 instructions
  └─ Magazine fill:                     ~1,000 instructions
Debug instrumentation:                  ~1,500 instructions (9%)
Cross-thread handling:                  ~2,000 instructions (12%)
Misc overhead:                          ~2,466 instructions (14%)
──────────────────────────────────────────────────────────
Total:                                  ~17,366 instructions

Key insight: 46% of instructions are in SuperSlab refill, which only happens 30% of the time. This means when refill happens, it costs ~26,000 instructions per refill (serving ~64 blocks), or ~400 instructions per block amortized.

mimalloc's 610 instructions:

Fast path hit (95%):                    ~20 instructions   (3%)
Fast path miss (5%):                    ~200 instructions  (16%)
Slow path (5% × cost):                  ~8,000 instructions (81%)
  └─ Amortized: 8000 × 0.05 = ~400 instructions
──────────────────────────────────────────────────────────
Total amortized:                        ~610 instructions

Conclusion: Even mimalloc's slow path costs ~8,000 instructions, but it happens only 5% of the time. HAKMEM's refill costs ~8,000 instructions and happens 30% of the time. The hit rate is the killer.

Problem 3: L1 Cache Thrashing (15.61% miss rate, 659 misses/op)

Cache behavior analysis:

HAKMEM cache access pattern (per operation):

L1 loads: 4,224 per op
L1 misses: 659 per op (15.61%)

Breakdown of cache misses:
- TLS variable access (scattered):        ~50 misses  (8%)
- Magazine structure access:              ~40 misses  (6%)
- Fast cache array access:                ~30 misses  (5%)
- SuperSlab lookup (30% ops):             ~200 misses (30%)
- Slab metadata access:                   ~100 misses (15%)
- memset during refill (30% ops):         ~150 misses (23%)
- Debug ring buffer:                      ~50 misses  (8%)
- Misc/stack:                             ~39 misses  (6%)
────────────────────────────────────────────────────────
Total:                                    ~659 misses

mimalloc cache access pattern (per operation):

L1 loads: 235 per op
L1 misses: 11.5 per op (4.89%)

Breakdown (estimated):
- TLS tcache access (packed):             ~2 misses   (17%)
- tcache array (fast path hit):           ~0 misses   (0%)
- Slow path (5% ops):                     ~200 misses (83%)
  └─ Amortized: 200 × 0.05 = ~10 misses
────────────────────────────────────────────────────────
Total:                                    ~11.5 misses

Key differences:

1. TLS layout: mimalloc packs hot data in one structure, HAKMEM scatters across many TLS vars
2. Magazine overhead: HAKMEM's 3-layer cache (fast/magazine/superslab) vs mimalloc's 2-layer (tcache/page)
3. Refill frequency: HAKMEM refills 30% vs mimalloc 5%
4. Refill cost: HAKMEM's refill does memset (cache-intensive), mimalloc lazy-inits

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Comparison with System malloc

From CLAUDE.md, comprehensive benchmark results:

• System malloc (glibc): 135.94 M ops/s (tiny allocations)
• HAKMEM: 2.62 M ops/s (this test)
• mimalloc: 16.76 M ops/s (this test)

System malloc is 52x faster than HAKMEM, 8x faster than mimalloc!

Why is System tcache so fast?

System malloc (glibc 2.28+) uses tcache:

// Simplified tcache fast path (~5 instructions)
void* malloc(size_t size) {
    tcache_entry *e = tcache->entries[size_class];
    if (e) {
        tcache->entries[size_class] = e->next;
        return (void*)e;
    }
    return malloc_slow_path(size);
}

Actual assembly (estimated):

malloc:
    mov    %fs:tcache_offset,%rax   ; Get tcache (TLS)
    lea    (%rax,%class,8),%rdx     ; &tcache->entries[class]
    mov    (%rdx),%rax              ; Load head
    test   %rax,%rax                ; Check NULL
    je     slow_path                ; Miss -> slow
    mov    (%rax),%rcx              ; Load next
    mov    %rcx,(%rdx)              ; Store next as new head
    ret                             ; Return block (7 instructions!)

Why HAKMEM can't match this:

1. Magazine layer adds indirection - magazine → cache → block (vs tcache → block)
2. SuperSlab adds more indirection - superslab → slab → block
3. Size class calculation is complex - not branchless
4. Debug instrumentation - tiny_debug_ring_record
5. Ownership checks - hak_tiny_owner_slab
6. Stack overhead - saving 6 registers, 88-byte stack frame

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Improvement Recommendations (Prioritized)

1. CRITICAL: Fix superslab_refill bottleneck (Expected: +50-100%)

Problem: 7.25% CPU, called 30% of operations

Root cause: Low fast cache capacity (16 slots) + expensive refill

Solutions (in order):

a) Increase fast cache capacity

• Current: 16 slots per class
• Target: 64-256 slots per class (adaptive based on hotness)
• Expected: Reduce miss rate from 30% to 10%
• Impact: 7.25% × (20/30) = 4.8% CPU savings (+18% throughput)

Implementation:

// Current
#define HAKMEM_TINY_FAST_CAP 16

// New (adaptive)
#define HAKMEM_TINY_FAST_CAP_COLD 16
#define HAKMEM_TINY_FAST_CAP_WARM 64
#define HAKMEM_TINY_FAST_CAP_HOT 256

// Set based on allocation rate per class
if (alloc_rate > 1000/s) use HOT cap
else if (alloc_rate > 100/s) use WARM cap
else use COLD cap

b) Increase refill batch size

• Current: Unknown (likely 64 based on REFILL_COUNT)
• Target: 128-256 blocks per refill
• Expected: Reduce refill frequency by 2-4x
• Impact: 7.25% × 0.5 = 3.6% CPU savings (+14% throughput)

c) Eliminate memset in refill

• Current: 1.33% CPU in memset during refill
• Target: Lazy initialization (only zero on first use)
• Expected: Remove 1.33% CPU
• Impact: +5% throughput

Implementation:

// Current: eager memset
void* superslab_refill() {
    void* blocks = allocate_slab();
    memset(blocks, 0, slab_size);  // ← Remove this!
    return blocks;
}

// New: lazy memset
void* malloc() {
    void* p = fast_cache_pop();
    if (p && needs_zero(p)) {
        memset(p, 0, size);  // Only zero on demand
    }
    return p;
}

d) Optimize refill path

• Profile superslab_refill internals
• Reduce allocations per refill
• Batch operations
• Expected: Reduce refill cost by 30%
• Impact: 7.25% × 0.3 = 2.2% CPU savings (+8% throughput)

Combined expected improvement: +45-60% throughput

---

2. HIGH: Simplify fast path (Expected: +30-50%)

Problem: 17,366 instructions/op vs mimalloc's 610 (28x overhead)

Target: Reduce to <5,000 instructions/op (match System tcache's ~500)

Solutions:

a) Inline aggressively

• Mark all hot functions __attribute__((always_inline))
• Reduce function call overhead (save/restore registers)
• Expected: -20% instructions (+5% throughput)

Implementation:

static inline __attribute__((always_inline))
void* hak_tiny_alloc_fast(size_t size) {
    // ... fast path logic ...
}

b) Branchless size class calculation

• Current: Multiple branches for size class
• Target: Lookup table or branchless arithmetic
• Expected: -5% instructions (+2% throughput)

Implementation:

// Current (branchy)
int size_to_class(size_t sz) {
    if (sz <= 16) return 0;
    if (sz <= 32) return 1;
    if (sz <= 64) return 2;
    if (sz <= 128) return 3;
    // ...
}

// New (branchless)
static const uint8_t size_class_table[129] = {
    0,0,0,...,0,  // 1-16
    1,1,...,1,    // 17-32
    2,2,...,2,    // 33-64
    3,3,...,3     // 65-128
};

static inline int size_to_class(size_t sz) {
    return (sz <= 128) ? size_class_table[sz]
                       : size_to_class_large(sz);
}

c) Pack TLS structure

• Current: Scattered TLS variables
• Target: Single cache-line TLS struct (64 bytes)
• Expected: -30% cache misses (+10% throughput)

Implementation:

// Current (scattered)
__thread void* g_fast_cache[16];
__thread magazine_t g_magazine;
__thread int g_class;

// New (packed)
struct tiny_tls_cache {
    void* fast_cache[8];  // Hot data first
    uint32_t counts[8];
    magazine_t* magazine; // Cold data
    // ... fit in 64 bytes
} __attribute__((aligned(64)));

__thread struct tiny_tls_cache g_tls_cache;

d) Remove debug instrumentation

• Current: tiny_debug_ring_record in hot path
• Target: Compile-time conditional
• Expected: -5% instructions (+2% throughput)

Implementation:

#if HAKMEM_DEBUG_RING
    tiny_debug_ring_record(...);
#endif

e) Simplify ownership check

• Current: hak_tiny_owner_slab (0.12% CPU)
• Target: Store owner in block header or remove check
• Expected: -3% instructions (+1% throughput)

Combined expected improvement: +20-25% throughput

---

3. MEDIUM: Reduce L1 cache misses (Expected: +20-30%)

Problem: 659 L1 misses/op vs mimalloc's 11.5 (57x worse)

Target: Reduce to <100 misses/op

Solutions:

a) Pack hot TLS data in one cache line

• Current: Scattered across many cache lines
• Target: Fast path data in 64 bytes
• Expected: -60% TLS cache misses (+10% throughput)

b) Prefetch superslab metadata

• Current: Cold cache misses on refill
• Target: Prefetch 1-2 cache lines ahead
• Expected: -30% refill cache misses (+5% throughput)

Implementation:

void superslab_refill() {
    superslab_t* ss = get_superslab();
    __builtin_prefetch(ss, 0, 3);     // Prefetch for read
    __builtin_prefetch(&ss->bitmap, 0, 3);
    // ... continue refill ...
}

c) Align structures to cache lines

• Current: Structures may span cache lines
• Target: 64-byte alignment for hot structures
• Expected: -10% cache misses (+3% throughput)

Implementation:

struct tiny_fast_cache {
    void* blocks[64];
    uint32_t count;
    uint32_t capacity;
} __attribute__((aligned(64)));

d) Remove debug ring buffer

• Current: 50 cache misses/op from debug ring
• Target: Disable in production builds
• Expected: -8% cache misses (+3% throughput)

Combined expected improvement: +21-26% throughput

---

4. LOW: Reduce initialization overhead (Expected: +5-10%)

Problem: 1.33% CPU in memset

Solution: Lazy initialization (covered in #1c above)

---

Expected Outcomes

Scenario 1: Quick Fixes Only (Week 1)

Changes:

• Increase FAST_CAP to 64
• Increase refill batch to 128
• Lazy initialization (remove memset)

Expected:

• Reduce refill frequency: +18%
• Reduce refill cost: +8%
• Remove memset: +5%

Total: 2.62M → 3.44M ops/s (+31%) Still 4.9x slower than mimalloc

---

Scenario 2: Incremental Optimizations (Week 2-3)

Changes:

• All from Scenario 1
• Inline hot functions
• Branchless size class
• Pack TLS structure
• Remove debug code

Expected:

• From Scenario 1: +31%
• Fast path simplification: +20%
• Cache locality: +15%

Total: 2.62M → 4.85M ops/s (+85%) Still 3.5x slower than mimalloc

---

Scenario 3: Aggressive Refactor (Week 4-6)

Changes:

• Option A: Adopt tcache-style design for tiny

  • Ultra-simple fast path (5-10 instructions)
  • Direct TLS array, no magazine layer
  • Expected: Match System malloc (~100-130 M ops/s for tiny)
  • Total: 2.62M → ~80M ops/s (+30x) 🚀

• Option B: Hybrid approach

  • Tiny: tcache-style (simple)
  • Mid-Large: Keep current design (working well, +171%)
  • Expected: Best of both worlds
  • Total: 2.62M → ~50M ops/s (+19x) 🚀

---

Scenario 4: Best Case (Full Redesign)

Changes:

• Ultra-simple tcache-style fast path for tiny
• Zero-overhead hit (5-10 instructions)
• 99% hit rate (like System tcache)
• Lazy initialization
• No debug overhead

Expected:

• Match System malloc for tiny: ~130 M ops/s
• Total: 2.62M → 130M ops/s (+50x) 🚀🚀🚀

---

Concrete Action Plan

Phase 1: Quick Wins (1 week)

Goal: +30% improvement to prove approach

1. ✅ Increase HAKMEM_TINY_FAST_CAP from 16 to 64

   # In core/hakmem_tiny.h
   #define HAKMEM_TINY_FAST_CAP 64
   

2. ✅ Increase HAKMEM_TINY_REFILL_COUNT_HOT from 64 to 128

   # In ENV_VARS or code
   HAKMEM_TINY_REFILL_COUNT_HOT=128
   

3. ✅ Remove eager memset in superslab_refill

   // In core/hakmem_tiny_superslab.c
   // Comment out or remove memset call
   

4. ✅ Rebuild and benchmark

   make clean && make
   ./larson_hakmem 2 8 128 1024 1 12345 4
   

Expected: 2.62M → 3.44M ops/s

---

Phase 2: Fast Path Optimization (1-2 weeks)

Goal: +50% cumulative improvement

1. ✅ Inline all hot functions

   • hak_tiny_alloc_fast
   • hak_tiny_free_fast
   • size_to_class

2. ✅ Implement branchless size_to_class

3. ✅ Pack TLS structure into single cache line

4. ✅ Remove debug instrumentation from release builds

5. ✅ Measure instruction count reduction

   perf stat -e instructions ./larson_hakmem ...
   # Target: <30B instructions (down from 45.5B)
   

Expected: 2.62M → 4.85M ops/s

---

Phase 3: Algorithm Evaluation (1 week)

Goal: Decide on redesign vs incremental

1. ✅ Benchmark System malloc

   # Remove LD_PRELOAD, use system malloc
   ./larson_system 2 8 128 1024 1 12345 4
   # Confirm: ~130 M ops/s
   

2. ✅ Study tcache implementation

   # Read glibc tcache source
   less /usr/src/glibc/malloc/malloc.c
   # Focus on tcache_put, tcache_get
   

3. ✅ Prototype simple tcache

   • Implement 64-entry TLS array per class
   • Simple push/pop (5-10 instructions)
   • Benchmark in isolation

4. ✅ Compare approaches

   • Incremental: 4.85M ops/s (realistic)
   • Tcache: ~80M ops/s (aspirational)
   • Hybrid: ~50M ops/s (balanced)

Decision: Choose between incremental or redesign

---

Phase 4: Implementation (2-4 weeks)

Goal: Achieve target performance

If Incremental:

• Continue optimizing refill path
• Improve cache locality
• Target: 5-10 M ops/s

If Tcache Redesign:

• Implement ultra-simple fast path
• Keep slow path for refills
• Target: 50-100 M ops/s

If Hybrid:

• Tcache for tiny (≤1KB)
• Current design for mid-large (already fast)
• Target: 50-80 M ops/s overall

---

Conclusion

Root Causes (Confirmed)

1. PRIMARY: superslab_refill bottleneck (7.25% CPU)

   • Caused by low fast cache capacity (16 slots)
   • Expensive refill (includes memset)
   • High miss rate (30%)

2. SECONDARY: Instruction overhead (28x per-op)

   • Complex fast path (17,366 instructions/op)
   • Magazine layer indirection
   • Debug instrumentation

3. TERTIARY: L1 cache misses (57x per-op)

   • Scattered TLS variables
   • Poor spatial locality
   • Refill cache pollution

Recommended Path Forward

Short term (1-2 weeks):

• Implement quick wins (Phase 1-2)
• Target: +50% improvement (2.62M → 4M ops/s)
• Validate approach with data

Medium term (3-4 weeks):

• Evaluate redesign options (Phase 3)
• Decide: incremental vs tcache vs hybrid
• Begin implementation (Phase 4)

Long term (5-8 weeks):

• Complete chosen approach
• Target: 10x improvement (2.62M → 26M ops/s minimum)
• Aspirational: 50x improvement (2.62M → 130M ops/s)

Success Metrics

Milestone	Target	Status
Phase 1 Quick Wins	3.44M ops/s (+31%)	⏳ Pending
Phase 2 Optimizations	4.85M ops/s (+85%)	⏳ Pending
Phase 3 Evaluation	Decision made	⏳ Pending
Phase 4 Final	26M ops/s (+10x)	⏳ Pending
Stretch Goal	130M ops/s (+50x)	🎯 Aspirational

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Analysis completed: 2025-11-05 Next action: Implement Phase 1 quick wins and measure results