Commit Graph

3 Commits

Author SHA1 Message Date
5ea6c1237b Tiny: add per-class refill count tuning infrastructure (ChatGPT)
External AI (ChatGPT Pro) implemented hierarchical refill count tuning:
- Move getenv() from hot path to init (performance hygiene)
- Add per-class granularity: global → hot/mid → per-class precedence
- Environment variables:
  * HAKMEM_TINY_REFILL_COUNT (global default)
  * HAKMEM_TINY_REFILL_COUNT_HOT (classes 0-3)
  * HAKMEM_TINY_REFILL_COUNT_MID (classes 4-7)
  * HAKMEM_TINY_REFILL_COUNT_C{0..7} (per-class override)

Performance impact: Neutral (no tuning applied yet, default=16)
- Larson 4-thread: 4.19M ops/s (unchanged)
- No measurable overhead from init-time parsing

Code quality improvement:
- Better separation: hot path reads plain ints (no syscalls)
- Future-proof: enables A/B testing per size class
- Documentation: ENV_VARS.md updated

Note: Per Ultrathink's advice, further tuning deferred until bottleneck
visualization (superslab_refill branch analysis) is complete.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: ChatGPT <external-ai@openai.com>
2025-11-05 17:45:11 +09:00
af938fe378 Add RDTSC profiling - Identify refill bottleneck
Profiling Results:
- Fast path: 143 cycles (10.4% of time)  Good
- Refill: 19,624 cycles (89.6% of time) 🚨 Bottleneck!

Refill is 137x slower than fast path and dominates total cost.
Only happens 6.3% of the time but takes 90% of execution time.

Next: Optimize sll_refill_small_from_ss() backend.
2025-11-05 06:35:03 +00:00
52386401b3 Debug Counters Implementation - Clean History
Major Features:
- Debug counter infrastructure for Refill Stage tracking
- Free Pipeline counters (ss_local, ss_remote, tls_sll)
- Diagnostic counters for early return analysis
- Unified larson.sh benchmark runner with profiles
- Phase 6-3 regression analysis documentation

Bug Fixes:
- Fix SuperSlab disabled by default (HAKMEM_TINY_USE_SUPERSLAB)
- Fix profile variable naming consistency
- Add .gitignore patterns for large files

Performance:
- Phase 6-3: 4.79 M ops/s (has OOM risk)
- With SuperSlab: 3.13 M ops/s (+19% improvement)

This is a clean repository without large log files.

🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-05 12:31:14 +09:00