7adbcdfcb6
## Summary
Completed Phase 54-60 optimization work:
**Phase 54-56: Memory-Lean mode (LEAN+OFF prewarm suppression)**
- Implemented ss_mem_lean_env_box.h with ENV gates
- Balanced mode (LEAN+OFF) promoted as production default
- Result: +1.2% throughput, better stability, zero syscall overhead
- Added to bench_profile.h: MIXED_TINYV3_C7_BALANCED preset
**Phase 57: 60-min soak finalization**
- Balanced mode: 60-min soak, RSS drift 0%, CV 5.38%
- Speed-first mode: 60-min soak, RSS drift 0%, CV 1.58%
- Syscall budget: 1.25e-7/op (800× under target)
- Status: PRODUCTION-READY
**Phase 59: 50% recovery baseline rebase**
- hakmem FAST (Balanced): 59.184M ops/s, CV 1.31%
- mimalloc: 120.466M ops/s, CV 3.50%
- Ratio: 49.13% (M1 ACHIEVED within statistical noise)
- Superior stability: 2.68× better CV than mimalloc
**Phase 60: Alloc pass-down SSOT (NO-GO)**
- Implemented alloc_passdown_ssot_env_box.h
- Modified malloc_tiny_fast.h for SSOT pattern
- Result: -0.46% (NO-GO)
- Key lesson: SSOT not applicable where early-exit already optimized
## Key Metrics
- Performance: 49.13% of mimalloc (M1 effectively achieved)
- Stability: CV 1.31% (superior to mimalloc 3.50%)
- Syscall budget: 1.25e-7/op (excellent)
- RSS: 33MB stable, 0% drift over 60 minutes
## Files Added/Modified
New boxes:
- core/box/ss_mem_lean_env_box.h
- core/box/ss_release_policy_box.{h,c}
- core/box/alloc_passdown_ssot_env_box.h
Scripts:
- scripts/soak_mixed_single_process.sh
- scripts/analyze_epoch_tail_csv.py
- scripts/soak_mixed_rss.sh
- scripts/calculate_percentiles.py
- scripts/analyze_soak.py
Documentation: Phase 40-60 analysis documents
## Design Decisions
1. Profile separation (core/bench_profile.h):
- MIXED_TINYV3_C7_SAFE: Speed-first (no LEAN)
- MIXED_TINYV3_C7_BALANCED: Balanced mode (LEAN+OFF)
2. Box Theory compliance:
- All ENV gates reversible (HAKMEM_SS_MEM_LEAN, HAKMEM_ALLOC_PASSDOWN_SSOT)
- Single conversion points maintained
- No physical deletions (compile-out only)
3. Lessons learned:
- SSOT effective only where redundancy exists (Phase 60 showed limits)
- Branch prediction extremely effective (~0 cycles for well-predicted branches)
- Early-exit pattern valuable even when seemingly redundant
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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Phase 52: Tail Latency Proxy（epoch throughput quantiles）
目的: 「alloc/free の tail」を コード変更なしで近似し、SSOT 化する。
方針: 単一プロセス soak の 短い epoch を大量に取り、epoch throughput の分布（p50/p90/p99/p999）で tail を proxy する。
理由:
- 1-op latency histogram を入れると測定オーバーヘッドが大きい
- 既に epoch 出力（
[EPOCH] ... Throughput ... rss_kb=...）があるので、低コストで tail proxy が取れる
Step 0: Build
make bench_random_mixed_hakmem_minimal
make bench_random_mixed_system
make bench_random_mixed_mi
Step 1: Single-process epoch soak（5分、epoch=1秒）
hakmem FAST:
BENCH_BIN=./bench_random_mixed_hakmem_minimal \
DURATION_SEC=300 EPOCH_SEC=1 WS=400 \
scripts/soak_mixed_single_process.sh > tail_epoch_hakmem_fast_5m.csv
mimalloc / system:
BENCH_BIN=./bench_random_mixed_mi \
DURATION_SEC=300 EPOCH_SEC=1 WS=400 \
scripts/soak_mixed_single_process.sh > tail_epoch_mimalloc_5m.csv
BENCH_BIN=./bench_random_mixed_system \
DURATION_SEC=300 EPOCH_SEC=1 WS=400 \
scripts/soak_mixed_single_process.sh > tail_epoch_system_5m.csv
注意:
EPOCH_SEC=1で ~300 サンプル（p99 近傍が見える）- さらに tail を見たい場合は
DURATION_SEC=1800（30分）で ~1800 サンプル
Step 2: 集計（p50/p90/p99/p999）
CSV から分位を計算する。
重要:
- throughput の tail は “低い側”（p1/p0.1）で見る（p99 は “速い側”）
- latency percentiles は per-epoch latency を作ってから計算する（
p99(latency) ≠ 1/p99(throughput)）
推奨（正しい計算）:
python3 scripts/analyze_epoch_tail_csv.py tail_epoch_hakmem_fast_5m.csv
推奨:
- p50/p90/p99 は 5分で十分
- p999 は 30分以上推奨（サンプル数が足りない）
（分析スクリプトがある場合はそれを使用。なければ簡易 awk/python を追加する Phase 52-1 で行う）
Step 3: SSOT 更新
更新先:
docs/analysis/PERFORMANCE_TARGETS_SCORECARD.md
追記:
- tail proxy（p99 epoch throughput / p99 latency proxy）
- どの条件（FAST/WS/epoch_sec/duration）で測ったか