Files
hakmem/analyze_soak_single.py
Moe Charm (CI) 7adbcdfcb6 Phase 54-60: Memory-Lean mode, Balanced mode stabilization, M1 (50%) achievement
## 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>
2025-12-17 06:24:01 +09:00

97 lines
3.6 KiB
Python
Executable File

#!/usr/bin/env python3
"""Analyze single-process soak test CSV results for Phase 51."""
import sys
import csv
import statistics
def analyze_csv(filename):
"""Analyze a single CSV file and return metrics."""
throughputs = []
rss_values = []
with open(filename, 'r') as f:
reader = csv.DictReader(f)
for row in reader:
throughput = float(row['throughput_ops_s'])
rss = float(row['rss_mb'])
throughputs.append(throughput)
rss_values.append(rss)
if len(throughputs) == 0:
return None
# Calculate metrics
first_5 = throughputs[:5] if len(throughputs) >= 5 else throughputs
last_5 = throughputs[-5:] if len(throughputs) >= 5 else throughputs
first_throughput = statistics.mean(first_5)
last_throughput = statistics.mean(last_5)
throughput_drift_pct = ((last_throughput - first_throughput) / first_throughput) * 100
mean_throughput = statistics.mean(throughputs)
stddev_throughput = statistics.stdev(throughputs) if len(throughputs) > 1 else 0
cv_pct = (stddev_throughput / mean_throughput) * 100
first_rss = rss_values[0]
last_rss = rss_values[-1]
rss_drift_pct = ((last_rss - first_rss) / first_rss) * 100 if first_rss > 0 else 0
peak_rss = max(rss_values)
return {
'samples': len(throughputs),
'mean_throughput': mean_throughput,
'first_throughput': first_throughput,
'last_throughput': last_throughput,
'throughput_drift_pct': throughput_drift_pct,
'stddev_throughput': stddev_throughput,
'cv_pct': cv_pct,
'first_rss': first_rss,
'last_rss': last_rss,
'peak_rss': peak_rss,
'rss_drift_pct': rss_drift_pct,
}
def main():
files = {
'hakmem FAST': 'soak_single_hakmem_fast_5m.csv',
'mimalloc': 'soak_single_mimalloc_5m.csv',
'system malloc': 'soak_single_system_5m.csv',
}
results = {}
for name, filename in files.items():
try:
metrics = analyze_csv(filename)
if metrics:
results[name] = metrics
print(f"\n{'='*60}")
print(f"Allocator: {name}")
print(f"{'='*60}")
print(f"Samples: {metrics['samples']}")
print(f"Mean throughput: {metrics['mean_throughput']/1e6:.2f} M ops/s")
print(f"First 5 avg: {metrics['first_throughput']/1e6:.2f} M ops/s")
print(f"Last 5 avg: {metrics['last_throughput']/1e6:.2f} M ops/s")
print(f"Throughput drift: {metrics['throughput_drift_pct']:+.2f}%")
print(f"Throughput CV: {metrics['cv_pct']:.2f}%")
print(f"First RSS: {metrics['first_rss']:.2f} MB")
print(f"Last RSS: {metrics['last_rss']:.2f} MB")
print(f"Peak RSS: {metrics['peak_rss']:.2f} MB")
print(f"RSS drift: {metrics['rss_drift_pct']:+.2f}%")
except Exception as e:
print(f"Error processing {name}: {e}", file=sys.stderr)
print(f"\n{'='*60}")
print("Summary")
print(f"{'='*60}")
print(f"{'Allocator':<20} {'Throughput':>12} {'TP Drift':>10} {'CV':>8} {'Peak RSS':>10} {'RSS Drift':>10}")
print(f"{'':<20} {'(M ops/s)':>12} {'(%)':>10} {'(%)':>8} {'(MB)':>10} {'(%)':>10}")
print("-" * 80)
for name in ['hakmem FAST', 'mimalloc', 'system malloc']:
if name in results:
m = results[name]
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}")
if __name__ == '__main__':
main()