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>
Problem: hak_core_init.inc.h references KPI measurement variables
(g_latency_histogram, g_latency_samples, g_baseline_soft_pf, etc.)
but hakmem.c was including hak_kpi_util.inc.h AFTER hak_core_init.inc.h,
causing undefined reference errors.
Solution: Reorder includes so hak_kpi_util.inc.h (definition) comes
before hak_core_init.inc.h (usage).
Build result: ✅ Success (libhakmem.so 547KB, 0 errors)
Minor changes:
- Added extern __thread declarations for TLS SLL debug variables
- Added signal handler logging for debug_dump_last_push
- Improved hakmem_tiny.c structure for Phase 2 preparation
🤖 Generated with Claude Code + Task Agent
Co-Authored-By: Gemini <gemini@example.com>
Co-Authored-By: Claude <noreply@anthropic.com>
This commit introduces a comprehensive tracing mechanism for allocation failures within the Adaptive Cache Engine (ACE) component. This feature allows for precise identification of the root cause for Out-Of-Memory (OOM) issues related to ACE allocations.
Key changes include:
- **ACE Tracing Implementation**:
- Added environment variable to enable/disable detailed logging of allocation failures.
- Instrumented , , and to distinguish between "Threshold" (size class mismatch), "Exhaustion" (pool depletion), and "MapFail" (OS memory allocation failure).
- **Build System Fixes**:
- Corrected to ensure is properly linked into , resolving an error.
- **LD_PRELOAD Wrapper Adjustments**:
- Investigated and understood the wrapper's behavior under , particularly its interaction with and checks.
- Enabled debugging flags for environment to prevent unintended fallbacks to 's for non-tiny allocations, allowing comprehensive testing of the allocator.
- **Debugging & Verification**:
- Introduced temporary verbose logging to pinpoint execution flow issues within interception and routing. These temporary logs have been removed.
- Created to facilitate testing of the tracing features.
This feature will significantly aid in diagnosing and resolving allocation-related OOM issues in by providing clear insights into the failure pathways.
