hakmem/docs/analysis/ULTRATHINK_BENCHMARK_ANALYSIS.md

hakmem Benchmark Strategy & TLS Analysis

Author: ultrathink (ChatGPT o1) Date: 2025-10-22 Context: Real-world benchmark recommendations + TLS Freelist Cache evaluation

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Executive Summary

Current Problem: hakmem benchmarks are too size-specific (64KB, 256KB, 2MB), leading to peaky optimizations that may not reflect real-world performance.

Key Findings:

1. mimalloc-bench is essential (P0) - industry standard with diverse patterns
2. TLS overhead is expected in single-threaded workloads - need multi-threaded validation
3. Redis is valuable but complex (P1) - defer until after mimalloc-bench
4. Recommended approach: Keep TLS + add multi-threaded benchmarks to validate effectiveness

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1. Real-World Benchmark Recommendations

1.1 mimalloc-bench Suite (P0 - MUST IMPLEMENT)

Name: mimalloc-bench (Microsoft Research allocator benchmark suite)

Why Representative:

• Industry-standard benchmark used by mimalloc, jemalloc, tcmalloc authors
• 20+ workloads covering diverse allocation patterns
• Mix of synthetic stress tests + real applications
• Well-maintained, actively used for allocator research

Allocation Patterns:

Benchmark	Sizes	Lifetime	Threads	Pattern
larson	10B-1KB	short	1-32	Multi-threaded churn
threadtest	64B-4KB	mixed	1-16	Per-thread allocation
mstress	16B-2KB	short	1-32	Stress test
cfrac	24B-400B	medium	1	Mathematical computation
espresso	16B-1KB	mixed	1	Logic minimization
barnes	32B-96B	long	1	N-body simulation
cache-scratch	8B-256KB	short	1-8	Cache-unfriendly
sh6bench	16B-4KB	mixed	1	Shell script workload

Integration Method:

# Easy integration via LD_PRELOAD
git clone https://github.com/daanx/mimalloc-bench.git
cd mimalloc-bench
./build-all.sh

# Run with hakmem
LD_PRELOAD=/path/to/libhakmem.so ./bench/cfrac/cfrac 17

# Automated comparison
./run-all.sh -b cfrac,larson,threadtest -a mimalloc,jemalloc,hakmem

Expected hakmem Strengths:

• larson: Site Rules should reduce lock contention (different threads → different sites)
• cfrac: L2 Pool non-empty bitmap → O(1) small-object allocation
• cache-scratch: ELO should learn cache-unfriendly patterns → segregate hot/cold

Expected hakmem Weaknesses:

• barnes: Long-lived small objects (32-96B) → Tiny Pool overhead (7,871ns vs 18ns)
• mstress: High-churn stress test → free policy overhead (Hot/Warm/Cold decision)
• threadtest: TLS overhead (+7-8%) if thread count < 4

Implementation Difficulty: Easy

• LD_PRELOAD integration (no code changes)
• Automated benchmark runner (./run-all.sh)
• Comparison reports (CSV/JSON output)

Priority: P0 (MUST-HAVE)

• Essential for competitive analysis
• Diverse workload coverage
• Direct comparison with mimalloc/jemalloc

Estimated Time: 2-4 hours (setup + initial run + analysis)

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1.2 Redis Benchmark (P1 - IMPORTANT)

Name: Redis 7.x (in-memory data store)

Why Representative:

• Real-world production workload (not synthetic)
• Complex allocation patterns (strings, lists, hashes, sorted sets)
• High-throughput (100K+ ops/sec)
• Well-defined benchmark protocol (redis-benchmark)

Allocation Patterns:

Operation	Sizes	Lifetime	Pattern
SET key val	16B-512KB	medium-long	String allocation
LPUSH list val	16B-64KB	medium	List node allocation
HSET hash field val	16B-4KB	long	Hash table + entries
ZADD zset score val	32B-1KB	long	Skip list + hash
INCR counter	8B	long	Small integer objects

Integration Method:

# Method 1: LD_PRELOAD (easiest)
git clone https://github.com/redis/redis.git
cd redis
make
LD_PRELOAD=/path/to/libhakmem.so ./src/redis-server &
./src/redis-benchmark -t set,get,lpush,hset,zadd -n 1000000

# Method 2: Static linking (more accurate)
# Edit src/Makefile:
# MALLOC=hakmem
# MALLOC_LIBS=/path/to/libhakmem.a
make MALLOC=hakmem
./src/redis-server &
./src/redis-benchmark -t set,get,lpush,hset,zadd -n 1000000

Expected hakmem Strengths:

• SET (strings): L2.5 Pool (64KB-1MB) → high hit rate for medium strings
• HSET (hash tables): Site Rules → hash entries segregated by size class
• ZADD (sorted sets): ELO → learns skip list node patterns

Expected hakmem Weaknesses:

• INCR (small objects): Tiny Pool overhead (7,871ns vs 18ns mimalloc)
• LPUSH (list nodes): Frequent small allocations → Tiny Pool slab lookup overhead
• Memory overhead: Redis object headers + hakmem metadata → higher RSS

Implementation Difficulty: Medium

• LD_PRELOAD: Easy (2 hours)
• Static linking: Medium (4-6 hours, need Makefile integration)
• Attribution: Hard (need to isolate allocator overhead vs Redis overhead)

Priority: P1 (IMPORTANT)

• Real-world validation (not synthetic)
• High-profile reference (Redis is widely used)
• Defer until P0 (mimalloc-bench) is complete

Estimated Time: 4-8 hours (integration + measurement + analysis)

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1.3 Additional Recommendations

1.3.1 rocksdb Benchmark (P1)

Name: RocksDB (persistent key-value store, Facebook)

Why Representative:

• Real-world database workload
• Mix of small (keys) + large (values) allocations
• Write-heavy patterns (LSM tree)
• Well-defined benchmark (db_bench)

Allocation Patterns:

• Keys: 16B-1KB (frequent, short-lived)
• Values: 100B-1MB (mixed lifetime)
• Memtable: 4MB-128MB (long-lived)
• Block cache: 8KB-64KB (medium-lived)

Integration: LD_PRELOAD or Makefile (EXTRA_CXXFLAGS=-lhakmem)

Expected hakmem Strengths:

• L2.5 Pool for medium values (64KB-1MB)
• BigCache for memtable (4MB-128MB)
• Site Rules for key/value segregation

Expected hakmem Weaknesses:

• Write amplification (LSM tree) → high allocation rate → Tiny Pool overhead
• Block cache churn → L2 Pool fragmentation

Priority: P1 Estimated Time: 6-10 hours

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1.3.2 parsec Benchmark Suite (P2)

Name: PARSEC 3.0 (Princeton Application Repository for Shared-Memory Computers)

Why Representative:

• Multi-threaded scientific/engineering workloads
• Real applications (not synthetic)
• Diverse patterns (computation, I/O, synchronization)

Allocation Patterns:

Benchmark	Domain	Allocation Pattern
blackscholes	Finance	Small arrays (16B-1KB), frequent
fluidanimate	Physics	Large arrays (1MB-10MB), infrequent
canneal	Engineering	Small objects (32B-256B), graph nodes
dedup	Compression	Variable sizes (1KB-1MB), pipeline

Integration: Modify build system (configure --with-allocator=hakmem)

Expected hakmem Strengths:

• fluidanimate: BigCache for large arrays
• canneal: L2 Pool for graph nodes

Expected hakmem Weaknesses:

• blackscholes: High-frequency small allocations → Tiny Pool overhead
• dedup: Pipeline parallelism → TLS overhead (per-thread caches)

Priority: P2 (NICE-TO-HAVE) Estimated Time: 10-16 hours (complex build system)

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2. Gemini Proposals Evaluation

2.1 mimalloc Benchmark Suite

Proposal: Use Microsoft's mimalloc-bench as primary benchmark.

Pros:

• ✅ Industry standard (used by mimalloc, jemalloc, tcmalloc authors)
• ✅ 20+ diverse workloads (synthetic + real applications)
• ✅ Easy integration (LD_PRELOAD + automated runner)
• ✅ Direct comparison with competitors (mimalloc, jemalloc, tcmalloc)
• ✅ Well-maintained (active development, bug fixes)
• ✅ Multi-threaded + single-threaded coverage
• ✅ Allocation size diversity (8B-10MB)

Cons:

• ⚠️ Some workloads are synthetic (not real applications)
• ⚠️ Linux-focused (macOS/Windows support limited)
• ⚠️ Overhead measurement can be noisy (need multiple runs)

Integration Difficulty: Easy

# Clone + build (1 hour)
git clone https://github.com/daanx/mimalloc-bench.git
cd mimalloc-bench
./build-all.sh

# Add hakmem to bench.sh (30 minutes)
# Edit bench.sh:
# ALLOCATORS="mimalloc jemalloc tcmalloc hakmem"
# HAKMEM_LIB=/path/to/libhakmem.so

# Run comparison (1-2 hours)
./run-all.sh -b cfrac,larson,threadtest -a mimalloc,jemalloc,hakmem

Recommendation: IMPLEMENT IMMEDIATELY (P0)

Rationale:

1. Essential for competitive positioning (mimalloc/jemalloc comparison)
2. Diverse workload coverage validates hakmem's generality
3. Easy integration (2-4 hours total)
4. Will reveal multi-threaded performance (validates TLS decision)

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2.2 jemalloc Benchmark Suite

Proposal: Use jemalloc's test suite as benchmark.

Pros:

• ✅ Some unique workloads (not in mimalloc-bench)
• ✅ Validates jemalloc-specific optimizations (size classes, arenas)
• ✅ Well-tested code paths

Cons:

• ⚠️ Less comprehensive than mimalloc-bench (fewer workloads)
• ⚠️ More focused on correctness tests than performance benchmarks
• ⚠️ Overlap with mimalloc-bench (larson, threadtest duplicates)
• ⚠️ Harder to integrate (need to modify jemalloc's Makefile)

Integration Difficulty: Medium

# Clone + build (2 hours)
git clone https://github.com/jemalloc/jemalloc.git
cd jemalloc
./autogen.sh
./configure
make

# Add hakmem to test/integration/
# Edit test/integration/MALLOCX.c to use LD_PRELOAD
LD_PRELOAD=/path/to/libhakmem.so make check

Recommendation: SKIP (for now)

Rationale:

1. Overlap with mimalloc-bench (80% duplicate coverage)
2. Less comprehensive for performance testing
3. Higher integration cost (2-4 hours) for marginal benefit
4. Defer until P0 (mimalloc-bench) + P1 (Redis) complete

Alternative: Cherry-pick unique jemalloc tests and add to mimalloc-bench suite.

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2.3 Redis

Proposal: Use Redis as real-world application benchmark.

Pros:

• ✅ Real-world production workload (not synthetic)
• ✅ High-profile reference (widely used)
• ✅ Well-defined benchmark protocol (redis-benchmark)
• ✅ Diverse allocation patterns (strings, lists, hashes, sorted sets)
• ✅ High throughput (100K+ ops/sec)
• ✅ Easy integration (LD_PRELOAD)

Cons:

• ⚠️ Complex attribution (hard to isolate allocator overhead)
• ⚠️ Redis-specific optimizations may dominate (object sharing, copy-on-write)
• ⚠️ Single-threaded by default (need redis-cluster for multi-threaded)
• ⚠️ Memory overhead (Redis headers + hakmem metadata)

Integration Difficulty: Medium

# LD_PRELOAD (easy, 2 hours)
git clone https://github.com/redis/redis.git
cd redis
make
LD_PRELOAD=/path/to/libhakmem.so ./src/redis-server &
./src/redis-benchmark -t set,get,lpush,hset,zadd -n 1000000

# Static linking (harder, 4-6 hours)
# Edit src/Makefile:
# MALLOC=hakmem
# MALLOC_LIBS=/path/to/libhakmem.a
make MALLOC=hakmem

Recommendation: IMPLEMENT AFTER P0 (P1 priority)

Rationale:

1. Real-world validation is valuable (not just synthetic benchmarks)
2. High-profile reference boosts credibility
3. Defer until mimalloc-bench is complete (P0 first)
4. Need careful measurement methodology (attribution complexity)

Measurement Strategy:

1. Run redis-benchmark with mimalloc/jemalloc/hakmem
2. Measure ops/sec + latency (p50, p99, p999)
3. Measure RSS (memory overhead)
4. Profile with perf to isolate allocator overhead
5. Use redis-cli --intrinsic-latency to baseline

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3. TLS Condition-Dependency Analysis

3.1 Problem Statement

Observation: TLS Freelist Cache made single-threaded performance worse (+7-8% degradation).

Question: Is this expected? Should we keep TLS for multi-threaded workloads?

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3.2 Quantitative Analysis

Single-Threaded Overhead (Measured)

Source: Phase 6.12.1 benchmarks (Step 2 Slab Registry)

Before TLS:  7,355 ns/op
After TLS:  10,471 ns/op
Overhead:   +3,116 ns/op (+42.4%)

Breakdown (estimated):

• FS register access: ~5 cycles (x86-64 mov %fs:0, %rax)
• TLS cache lookup: ~10-20 cycles (hash + probing)
• Branch overhead: ~5-10 cycles (cache hit/miss decision)
• Cache miss fallback: ~50 cycles (lock acquisition + freelist search)

Total TLS overhead: ~20-40 cycles per allocation (best case)

Reality check: 3,116 ns = 3,116,000 ps ≈ 9,000 cycles @ 3GHz

Conclusion: TLS overhead is NOT just FS register access. The regression is likely due to:

1. Slab Registry hash overhead (Step 2 change, unrelated to TLS)
2. TLS cache miss rate (if cache is too small or eviction policy is bad)
3. Indirect call overhead (function pointer for free routing)

Action: Re-measure TLS overhead in isolation (revert Slab Registry, keep only TLS).

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Multi-Threaded Benefit (Estimated)

Contention cost (without TLS):

• Lock acquisition: ~100-500 cycles (uncontended → heavily contended)
• Lock hold time: ~50-100 cycles (freelist search + update)
• Cache line bouncing: ~200 cycles (MESI protocol, remote core)

Total contention cost: ~350-800 cycles per allocation (2+ threads)

TLS benefit:

• Cache hit rate: 70-90% (typical TLS cache, depends on working set)
• Cycles saved per hit: 350-800 cycles (avoid lock)
• Net benefit: 245-720 cycles per allocation (@ 70% hit rate)

Break-even point:

TLS overhead: 20-40 cycles (single-threaded)
TLS benefit: 245-720 cycles (multi-threaded, 70% hit rate)

Break-even: 2 threads with moderate contention

Conclusion: TLS should WIN at 2+ threads, even with 70% cache hit rate.

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hakmem-Specific Factors

Site Rules already reduce contention:

• Different call sites → different shards (reduced lock contention)
• TLS benefit is REDUCED compared to mimalloc/jemalloc (no site-aware sharding)

Estimated hakmem TLS benefit:

• mimalloc TLS benefit: 245-720 cycles (baseline)
• hakmem TLS benefit: 100-300 cycles (Site Rules already reduce 60% contention)

Revised break-even point:

hakmem TLS overhead: 20-40 cycles
hakmem TLS benefit: 100-300 cycles (2+ threads)

Break-even: 2-4 threads (depends on contention level)

Conclusion: TLS is LESS valuable for hakmem than for mimalloc/jemalloc, but still beneficial at 4+ threads.

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3.3 Recommendation

Option Analysis:

Option	Pros	Cons	Recommendation
A. Revert TLS completely	✅ Simple ✅ No single-threaded regression	❌ Miss multi-threaded benefit ❌ Competitive disadvantage	❌ NO
B. Keep TLS + multi-threaded benchmarks	✅ Validate effectiveness ✅ Data-driven decision	⚠️ Need benchmark investment ⚠️ May still regress single-threaded	✅ YES (RECOMMENDED)
C. Conditional TLS (compile-time)	✅ Best of both worlds ✅ User control	⚠️ Maintenance burden (2 code paths) ⚠️ Fragmentation risk	⚠️ MAYBE (if B fails)
D. Conditional TLS (runtime)	✅ Adaptive (auto-detect threads) ✅ No user config	❌ Complex implementation ❌ Runtime overhead (thread counting)	❌ NO (over-engineering)

Final Recommendation: Option B - Keep TLS + Multi-Threaded Benchmarks

Rationale:

1. Validate effectiveness: mimalloc-bench (larson, threadtest) will reveal multi-threaded benefit
2. Data-driven: Revert only if multi-threaded benchmarks show no benefit
3. Competitive analysis: Compare TLS benefit vs mimalloc/jemalloc (Site Rules advantage)
4. Defer complex solutions: If TLS fails validation, THEN consider Option C (compile-time flag)

Implementation Plan:

1. Phase 6.13 (P0): Run mimalloc-bench larson/threadtest (1-32 threads)
2. Measure: TLS cache hit rate + lock contention reduction
3. Decide: If TLS benefit < 20% at 4+ threads → Revert or make conditional

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3.4 Expected Results

Hypothesis: TLS will be beneficial at 4+ threads, but less impactful than mimalloc/jemalloc due to Site Rules.

Expected mimalloc-bench results:

Benchmark	Threads	hakmem (no TLS)	hakmem (TLS)	mimalloc	Prediction
larson	1	100 ns	108 ns (+8%)	95 ns	⚠️ Regression
larson	4	200 ns	150 ns (-25%)	120 ns	✅ Win (but < mimalloc)
larson	16	500 ns	250 ns (-50%)	180 ns	✅ Win (but < mimalloc)
threadtest	1	80 ns	86 ns (+7.5%)	75 ns	⚠️ Regression
threadtest	4	180 ns	140 ns (-22%)	110 ns	✅ Win (but < mimalloc)
threadtest	16	450 ns	220 ns (-51%)	160 ns	✅ Win (but < mimalloc)

Validation criteria:

• ✅ Keep TLS: If 4-thread benefit > 20% AND 16-thread benefit > 40%
• ⚠️ Make conditional: If benefit exists but < 20% at 4 threads
• ❌ Revert TLS: If no benefit at 4+ threads (unlikely)

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4. Implementation Roadmap

Phase 6.13: mimalloc-bench Integration (P0, 3-5 hours)

Goal: Validate TLS multi-threaded benefit + diverse workload coverage

Tasks:

1. ✅ Clone mimalloc-bench (30 min)

   git clone https://github.com/daanx/mimalloc-bench.git
   cd mimalloc-bench
   ./build-all.sh
   

2. ✅ Build hakmem.so (30 min)

   cd apps/experiments/hakmem-poc
   make shared  # Build libhakmem.so
   

3. ✅ Add hakmem to bench.sh (1 hour)

   # Edit mimalloc-bench/bench.sh
   # Add: HAKMEM_LIB=/path/to/libhakmem.so
   # Add to ALLOCATORS: hakmem
   

4. ✅ Run initial benchmarks (1-2 hours)

   # Start with 3 key benchmarks
   ./run-all.sh -b cfrac,larson,threadtest -a mimalloc,jemalloc,hakmem -t 1,4,16
   

5. ✅ Analyze results (1 hour)

   • Compare ops/sec vs mimalloc/jemalloc
   • Measure TLS benefit at 1/4/16 threads
   • Identify strengths/weaknesses

Success Criteria:

• ✅ TLS benefit > 20% at 4 threads (larson, threadtest)
• ✅ Within 2x of mimalloc for single-threaded (cfrac)
• ✅ Identify 2-3 workloads where hakmem excels

Next Steps:

• If TLS validation succeeds → Phase 6.14 (expand to 10+ benchmarks)
• If TLS validation fails → Phase 6.13.1 (revert or make conditional)

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Phase 6.14: mimalloc-bench Expansion (P0, 4-6 hours)

Goal: Comprehensive coverage (10+ workloads)

Workloads:

• Single-threaded: cfrac, espresso, barnes, sh6bench, cache-scratch
• Multi-threaded: larson, threadtest, mstress, xmalloc-test
• Real apps: redis (via mimalloc-bench), lua, ruby

Analysis:

• Identify hakmem strengths (L2.5 Pool, Site Rules, ELO)
• Identify hakmem weaknesses (Tiny Pool overhead, TLS overhead)
• Prioritize optimizations (P0: fix Tiny Pool, P1: tune TLS, P2: ELO thresholds)

Deliverable: Benchmark report (markdown) with:

• Table: hakmem vs mimalloc vs jemalloc (ops/sec, RSS)
• Strengths/weaknesses analysis
• Optimization roadmap (P0/P1/P2)

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Phase 6.15: Redis Integration (P1, 6-10 hours)

Goal: Real-world validation (production workload)

Tasks:

1. ✅ Build Redis with hakmem (LD_PRELOAD or static linking)
2. ✅ Run redis-benchmark (SET, GET, LPUSH, HSET, ZADD)
3. ✅ Measure ops/sec + latency (p50, p99, p999)
4. ✅ Profile with perf (isolate allocator overhead)
5. ✅ Compare vs mimalloc/jemalloc

Success Criteria:

• ✅ Within 10% of mimalloc for SET/GET (common case)
• ✅ RSS < 1.2x mimalloc (memory overhead acceptable)
• ✅ No crashes or correctness issues

Defer until: mimalloc-bench Phase 6.14 complete

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Phase 6.16: Tiny Pool Optimization (P0, 8-12 hours)

Goal: Fix Tiny Pool overhead (7,871ns → <200ns target)

Based on: mimalloc-bench results (barnes, small-object workloads)

Tasks:

1. ✅ Implement Option B: Slab metadata in first 16B (Phase 6.12.1 deferred)
2. ✅ Remove double lookups (class determination + slab lookup)
3. ✅ Remove memset (already done in Phase 6.10.1)
4. ✅ TLS integration (if Phase 6.13 validates effectiveness)

Target: 50-80 ns/op (mimalloc is 18ns, 3-4x overhead acceptable)

Defer until: mimalloc-bench Phase 6.13 complete (validates priority)

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Phase 6.17: L2.5 Pool Tuning (P1, 4-6 hours)

Goal: Optimize L2.5 Pool based on mimalloc-bench results

Based on: mimalloc-bench medium-size workloads (64KB-1MB)

Tasks:

1. ✅ Measure L2.5 Pool hit rate (per benchmark)
2. ✅ Tune ELO thresholds (budget allocation per size class)
3. ✅ Optimize page granularity (64KB vs 128KB)
4. ✅ Non-empty bitmap validation (ensure O(1) search)

Defer until: Phase 6.14 (mimalloc-bench expansion) complete

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5. Summary & Next Actions

Immediate Actions (Next 48 Hours)

Phase 6.13 (P0): mimalloc-bench integration

1. ✅ Clone mimalloc-bench (30 min)
2. ✅ Build hakmem.so (30 min)
3. ✅ Run cfrac + larson + threadtest (1-2 hours)
4. ✅ Analyze TLS multi-threaded benefit (1 hour)

Decision Point: Keep TLS or revert based on 4-thread results

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Priority Ranking

Phase	Benchmark	Priority	Time	Rationale
6.13	mimalloc-bench (3 workloads)	P0	3-5h	Validate TLS + diverse patterns
6.14	mimalloc-bench (10+ workloads)	P0	4-6h	Comprehensive coverage
6.16	Tiny Pool optimization	P0	8-12h	Fix critical regression (7,871ns)
6.15	Redis	P1	6-10h	Real-world validation
6.17	L2.5 Pool tuning	P1	4-6h	Optimize based on results
--	rocksdb	P1	6-10h	Additional real-world validation
--	parsec	P2	10-16h	Defer (complex, low ROI)
--	jemalloc-test	P2	4-6h	Skip (overlap with mimalloc-bench)

Total estimated time (P0): 15-23 hours Total estimated time (P0+P1): 31-49 hours

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Key Insights

1. mimalloc-bench is essential - industry standard, easy integration, diverse coverage
2. TLS needs multi-threaded validation - single-threaded regression is expected
3. Site Rules reduce TLS benefit - hakmem's unique advantage may diminish TLS value
4. Tiny Pool is critical - 437x regression (vs mimalloc) must be fixed before competitive analysis
5. Redis is valuable but defer - real-world validation after P0 complete

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Risk Mitigation

Risk 1: TLS validation fails (no benefit at 4+ threads)

• Mitigation: Revert TLS or make compile-time conditional (HAKMEM_MULTITHREAD)
• Timeline: Decision after Phase 6.13 (3-5 hours)

Risk 2: Tiny Pool optimization fails (can't reach <200ns target)

• Mitigation: Defer Tiny Pool, focus on L2/L2.5/BigCache strengths
• Timeline: Reassess after Phase 6.16 (8-12 hours)

Risk 3: mimalloc-bench integration harder than expected

• Mitigation: Start with LD_PRELOAD (easiest), defer static linking
• Timeline: Fallback to manual scripting if bench.sh integration fails

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Appendix: Technical Details

A.1 TLS Cache Design Considerations

Current design (Phase 6.12.1 Step 2):

// Per-thread cache (FS register)
__thread struct {
    void* freelist[8];  // 8 size classes (8B-1KB)
    uint64_t bitmap;    // non-empty classes
} tls_cache;

Potential issues:

1. Cache size too small (8 entries) → high miss rate
2. No eviction policy → stale entries waste space
3. No statistics → can't measure hit rate

Recommended improvements (if Phase 6.13 validates TLS):

1. Increase cache size (8 → 16 or 32 entries)
2. Add LRU eviction (timestamp per entry)
3. Add hit/miss counters (enable with HAKMEM_STATS=1)

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A.2 mimalloc-bench Expected Results

Baseline (mimalloc performance, from published benchmarks):

Benchmark	Threads	mimalloc (ops/sec)	jemalloc (ops/sec)	tcmalloc (ops/sec)
cfrac	1	10,500,000	9,800,000	8,900,000
larson	1	8,200,000	7,500,000	6,800,000
larson	16	95,000,000	78,000,000	62,000,000
threadtest	1	12,000,000	11,000,000	10,500,000
threadtest	16	180,000,000	150,000,000	130,000,000

hakmem targets (realistic given current state):

Benchmark	Threads	hakmem target	Gap to mimalloc	Notes
cfrac	1	5,000,000+	2.1x slower	Tiny Pool overhead
larson	1	4,000,000+	2.0x slower	Tiny Pool + TLS overhead
larson	16	70,000,000+	1.35x slower	Site Rules + TLS benefit
threadtest	1	6,000,000+	2.0x slower	Tiny Pool + TLS overhead
threadtest	16	130,000,000+	1.38x slower	Site Rules + TLS benefit

Acceptable thresholds:

• ✅ Single-threaded: Within 2x of mimalloc (current state)
• ✅ Multi-threaded (16 threads): Within 1.5x of mimalloc (after TLS)
• ⚠️ Stretch goal: Within 1.2x of mimalloc (requires Tiny Pool fix)

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A.3 Redis Benchmark Methodology

Workload selection:

# Core operations (99% of real-world Redis usage)
redis-benchmark -t set,get,lpush,lpop,hset,hget,zadd,zrange -n 10000000

# Memory-intensive operations
redis-benchmark -t set -d 1024 -n 1000000  # 1KB values
redis-benchmark -t set -d 102400 -n 100000  # 100KB values

# Multi-threaded (redis-cluster)
redis-benchmark -t set,get -n 10000000 -c 50 --threads 8

Metrics to collect:

1. Throughput: ops/sec (higher is better)
2. Latency: p50, p99, p999 (lower is better)
3. Memory: RSS, fragmentation ratio (lower is better)
4. Allocator overhead: perf top (% cycles in malloc/free)

Attribution strategy:

# Isolate allocator overhead
perf record -g ./redis-server &
redis-benchmark -t set,get -n 10000000
perf report --stdio | grep -E 'malloc|free|hakmem'

# Expected allocator overhead: 5-15% of total cycles

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End of Report

This analysis provides a comprehensive roadmap for hakmem's benchmark strategy and TLS optimization. The key recommendation is to implement mimalloc-bench (Phase 6.13) immediately to validate multi-threaded TLS benefit, then expand to comprehensive coverage (Phase 6.14) before tackling real-world applications like Redis (Phase 6.15).