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b7021061b8cc1e44ab2159460ad2e2b6fc0f5155
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5 Commits

Author SHA1 Message Date
Moe Charm (CI)
b7021061b8 Fix: CRITICAL double-allocation bug in trc_linear_carve() 
Root Cause:
trc_linear_carve() used meta->used as cursor, but meta->used decrements
on free, causing already-allocated blocks to be re-carved.

Evidence:
- [LINEAR_CARVE] used=61 batch=1 → block 61 created
- (blocks freed, used decrements 62→59)
- [LINEAR_CARVE] used=59 batch=3 → blocks 59,60,61 RE-CREATED!
- Result: double-allocation → memory corruption → SEGV

Fix Implementation:
1. Added TinySlabMeta.carved (monotonic counter, never decrements)
2. Changed trc_linear_carve() to use carved instead of used
3. carved tracks carve progress, used tracks active count

Files Modified:
- core/superslab/superslab_types.h: Add carved field
- core/tiny_refill_opt.h: Use carved in trc_linear_carve()
- core/hakmem_tiny_superslab.c: Initialize carved=0
- core/tiny_alloc_fast.inc.h: Add next pointer validation
- core/hakmem_tiny_free.inc: Add drain/free validation

Test Results:
✅ bench_random_mixed: 950,037 ops/s (no crash)
✅ Fail-fast mode: 651,627 ops/s (with diagnostic logs)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-11-08 01:18:37 +09:00
Moe Charm (CI)
1da8754d45 CRITICAL FIX: TLS 未初期化による 4T SEGV を完全解消 
**問題:**
- Larson 4T で 100% SEGV (1T は 2.09M ops/s で完走)
- System/mimalloc は 4T で 33.52M ops/s 正常動作
- SS OFF + Remote OFF でも 4T で SEGV

**根本原因: (Task agent ultrathink 調査結果)**
```
CRASH: mov (%r15),%r13
R15 = 0x6261  ← ASCII "ba" (ゴミ値、未初期化TLS)
```

Worker スレッドの TLS 変数が未初期化:
- `__thread void* g_tls_sll_head[TINY_NUM_CLASSES];`  ← 初期化なし
- pthread_create() で生成されたスレッドでゼロ初期化されない
- NULL チェックが通過 (0x6261 != NULL) → dereference → SEGV

**修正内容:**
全 TLS 配列に明示的初期化子 `= {0}` を追加:

1. **core/hakmem_tiny.c:**
   - `g_tls_sll_head[TINY_NUM_CLASSES] = {0}`
   - `g_tls_sll_count[TINY_NUM_CLASSES] = {0}`
   - `g_tls_live_ss[TINY_NUM_CLASSES] = {0}`
   - `g_tls_bcur[TINY_NUM_CLASSES] = {0}`
   - `g_tls_bend[TINY_NUM_CLASSES] = {0}`

2. **core/tiny_fastcache.c:**
   - `g_tiny_fast_cache[TINY_FAST_CLASS_COUNT] = {0}`
   - `g_tiny_fast_count[TINY_FAST_CLASS_COUNT] = {0}`
   - `g_tiny_fast_free_head[TINY_FAST_CLASS_COUNT] = {0}`
   - `g_tiny_fast_free_count[TINY_FAST_CLASS_COUNT] = {0}`

3. **core/hakmem_tiny_magazine.c:**
   - `g_tls_mags[TINY_NUM_CLASSES] = {0}`

4. **core/tiny_sticky.c:**
   - `g_tls_sticky_ss[TINY_NUM_CLASSES][TINY_STICKY_RING] = {0}`
   - `g_tls_sticky_idx[TINY_NUM_CLASSES][TINY_STICKY_RING] = {0}`
   - `g_tls_sticky_pos[TINY_NUM_CLASSES] = {0}`

**効果:**
```
Before: 1T: 2.09M ✅  |  4T: SEGV 💀
After:  1T: 2.41M ✅  |  4T: 4.19M ✅  (+15% 1T, SEGV解消)
```

**テスト:**
```bash
# 1 thread: 完走
./larson_hakmem 2 8 128 1024 1 12345 1
→ Throughput = 2,407,597 ops/s ✅

# 4 threads: 完走（以前は SEGV）
./larson_hakmem 2 8 128 1024 1 12345 4
→ Throughput = 4,192,155 ops/s ✅
```

**調査協力:** Task agent (ultrathink mode) による完璧な根本原因特定

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-11-07 01:27:04 +09:00
Moe Charm (CI)
5ea6c1237b Tiny: add per-class refill count tuning infrastructure (ChatGPT) 
External AI (ChatGPT Pro) implemented hierarchical refill count tuning:
- Move getenv() from hot path to init (performance hygiene)
- Add per-class granularity: global → hot/mid → per-class precedence
- Environment variables:
  * HAKMEM_TINY_REFILL_COUNT (global default)
  * HAKMEM_TINY_REFILL_COUNT_HOT (classes 0-3)
  * HAKMEM_TINY_REFILL_COUNT_MID (classes 4-7)
  * HAKMEM_TINY_REFILL_COUNT_C{0..7} (per-class override)

Performance impact: Neutral (no tuning applied yet, default=16)
- Larson 4-thread: 4.19M ops/s (unchanged)
- No measurable overhead from init-time parsing

Code quality improvement:
- Better separation: hot path reads plain ints (no syscalls)
- Future-proof: enables A/B testing per size class
- Documentation: ENV_VARS.md updated

Note: Per Ultrathink's advice, further tuning deferred until bottleneck
visualization (superslab_refill branch analysis) is complete.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: ChatGPT <external-ai@openai.com>
 2025-11-05 17:45:11 +09:00
Claude
af938fe378 Add RDTSC profiling - Identify refill bottleneck 
Profiling Results:
- Fast path: 143 cycles (10.4% of time) ✅ Good
- Refill: 19,624 cycles (89.6% of time) 🚨 Bottleneck!

Refill is 137x slower than fast path and dominates total cost.
Only happens 6.3% of the time but takes 90% of execution time.

Next: Optimize sll_refill_small_from_ss() backend.
 2025-11-05 06:35:03 +00:00
Moe Charm (CI)
52386401b3 Debug Counters Implementation - Clean History 
Major Features:
- Debug counter infrastructure for Refill Stage tracking
- Free Pipeline counters (ss_local, ss_remote, tls_sll)
- Diagnostic counters for early return analysis
- Unified larson.sh benchmark runner with profiles
- Phase 6-3 regression analysis documentation

Bug Fixes:
- Fix SuperSlab disabled by default (HAKMEM_TINY_USE_SUPERSLAB)
- Fix profile variable naming consistency
- Add .gitignore patterns for large files

Performance:
- Phase 6-3: 4.79 M ops/s (has OOM risk)
- With SuperSlab: 3.13 M ops/s (+19% improvement)

This is a clean repository without large log files.

🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
 2025-11-05 12:31:14 +09:00