- Observability Fix: Enabled unified cache hit/miss stats in release builds when HAKMEM_UNIFIED_CACHE_STATS_COMPILED is set.
- WarmPool Sizing: Decoupled hardcoded '12' from prefill logic; now uses TINY_WARM_POOL_DEFAULT_PER_CLASS macro and respects ENV overrides.
- Increased TINY_WARM_POOL_MAX_PER_CLASS to 32 to support wider ENV sweeps.
- Added unified_cache_auto_stats destructor to dump metrics at exit (replacing debug print hack).
Key Changes:
- Reduced static capacity from 16 to 12 SuperSlabs per class
- Fixed prefill threshold from hardcoded 4 to match capacity (12)
- Updated environment variable clamping to [1,12]
- This allows warm pool to actually utilize its full capacity
Performance:
- Baseline (post-unified-cache-opt): 4.76M ops/s
- After Phase 1: 4.84M ops/s
- Improvement: +1.6% (expected +15-20%)
Note: Actual improvement lower than expected because the warm pool
bottleneck is only part of the overall allocation path. Unified cache
optimization (+14.9%) already addressed much of the registry scan overhead.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Problem: Warm pool had 0% hit rate (only 1 hit per 3976 misses) despite being
implemented, causing all cache misses to go through expensive superslab_refill
registry scans.
Root Cause Analysis:
- Warm pool was initialized once and pushed a single slab after each refill
- When that slab was exhausted, it was discarded (not pushed back)
- Next refill would push another single slab, which was immediately exhausted
- Pool would oscillate between 0 and 1 items, yielding 0% hit rate
Solution: Secondary Prefill on Cache Miss
When warm pool becomes empty, we now do multiple superslab_refills and prefill
the pool with 3 additional HOT superlslabs before attempting to carve. This
builds a working set of slabs that can sustain allocation pressure.
Implementation Details:
- Modified unified_cache_refill() cold path to detect empty pool
- Added prefill loop: when pool count == 0, load 3 extra superlslabs
- Store extra slabs in warm pool, keep 1 in TLS for immediate carving
- Track prefill events in g_warm_pool_stats[].prefilled counter
Results (1M Random Mixed 256B allocations):
- Before: C7 hits=1, misses=3976, hit_rate=0.0%
- After: C7 hits=3929, misses=3143, hit_rate=55.6%
- Throughput: 4.055M ops/s (maintained vs 4.07M baseline)
- Stability: Consistent 55.6% hit rate at 5M allocations (4.102M ops/s)
Performance Impact:
- No regression: throughput remained stable at ~4.1M ops/s
- Registry scan avoided in 55.6% of cache misses (significant savings)
- Warm pool now functioning as intended with strong locality
Configuration:
- TINY_WARM_POOL_MAX_PER_CLASS increased from 4 to 16 to support prefill
- Prefill budget hardcoded to 3 (tunable via env var if needed later)
- All statistics always compiled, ENV-gated printing via HAKMEM_WARM_POOL_STATS=1
Next Steps:
- Monitor for further optimization opportunities (prefill budget tuning)
- Consider adaptive prefill budget based on class-specific hit rates
- Validate at larger allocation counts (10M+ pending registry size fix)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
