## 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>
97 lines
3.6 KiB
Python
97 lines
3.6 KiB
Python
#!/usr/bin/env python3
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"""Analyze soak test CSV results for Phase 50."""
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import sys
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import csv
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import statistics
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def analyze_csv(filename):
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"""Analyze a single CSV file and return metrics."""
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throughputs = []
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rss_values = []
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with open(filename, 'r') as f:
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reader = csv.DictReader(f)
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for row in reader:
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throughput = float(row['throughput_ops_s'])
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rss = float(row['peak_rss_mb'])
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throughputs.append(throughput)
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rss_values.append(rss)
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if len(throughputs) == 0:
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return None
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# Calculate metrics
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first_5 = throughputs[:5] if len(throughputs) >= 5 else throughputs
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last_5 = throughputs[-5:] if len(throughputs) >= 5 else throughputs
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first_throughput = statistics.mean(first_5)
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last_throughput = statistics.mean(last_5)
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throughput_drift_pct = ((last_throughput - first_throughput) / first_throughput) * 100
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mean_throughput = statistics.mean(throughputs)
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stddev_throughput = statistics.stdev(throughputs) if len(throughputs) > 1 else 0
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cv_pct = (stddev_throughput / mean_throughput) * 100
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first_rss = rss_values[0]
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last_rss = rss_values[-1]
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rss_drift_pct = ((last_rss - first_rss) / first_rss) * 100
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peak_rss = max(rss_values)
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return {
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'samples': len(throughputs),
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'mean_throughput': mean_throughput,
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'first_throughput': first_throughput,
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'last_throughput': last_throughput,
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'throughput_drift_pct': throughput_drift_pct,
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'stddev_throughput': stddev_throughput,
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'cv_pct': cv_pct,
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'first_rss': first_rss,
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'last_rss': last_rss,
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'peak_rss': peak_rss,
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'rss_drift_pct': rss_drift_pct,
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}
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def main():
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files = {
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'hakmem FAST': 'soak_fast_5min.csv',
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'mimalloc': 'soak_mimalloc_5min.csv',
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'system malloc': 'soak_system_5min.csv',
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}
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results = {}
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for name, filename in files.items():
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try:
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metrics = analyze_csv(filename)
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if metrics:
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results[name] = metrics
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print(f"\n{'='*60}")
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print(f"Allocator: {name}")
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print(f"{'='*60}")
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print(f"Samples: {metrics['samples']}")
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print(f"Mean throughput: {metrics['mean_throughput']/1e6:.2f} M ops/s")
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print(f"First 5 avg: {metrics['first_throughput']/1e6:.2f} M ops/s")
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print(f"Last 5 avg: {metrics['last_throughput']/1e6:.2f} M ops/s")
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print(f"Throughput drift: {metrics['throughput_drift_pct']:+.2f}%")
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print(f"Throughput CV: {metrics['cv_pct']:.2f}%")
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print(f"First RSS: {metrics['first_rss']:.2f} MB")
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print(f"Last RSS: {metrics['last_rss']:.2f} MB")
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print(f"Peak RSS: {metrics['peak_rss']:.2f} MB")
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print(f"RSS drift: {metrics['rss_drift_pct']:+.2f}%")
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except Exception as e:
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print(f"Error processing {name}: {e}", file=sys.stderr)
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print(f"\n{'='*60}")
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print("Summary")
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print(f"{'='*60}")
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print(f"{'Allocator':<20} {'Throughput':>12} {'TP Drift':>10} {'CV':>8} {'Peak RSS':>10} {'RSS Drift':>10}")
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print(f"{'':<20} {'(M ops/s)':>12} {'(%)':>10} {'(%)':>8} {'(MB)':>10} {'(%)':>10}")
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print("-" * 80)
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for name in ['hakmem FAST', 'mimalloc', 'system malloc']:
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if name in results:
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m = results[name]
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print(f"{name:<20} {m['mean_throughput']/1e6:>12.2f} {m['throughput_drift_pct']:>10.2f} {m['cv_pct']:>8.2f} {m['peak_rss']:>10.2f} {m['rss_drift_pct']:>10.2f}")
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if __name__ == '__main__':
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main()
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