Kernel Benchmarks ​

Benchmark Overview ​

This suite validates a single systems claim:

• Public-path recomputation should be bounded by dependency complexity (k), not dataset size (n).
• Secret-path overhead must be measurable, bounded, and continuously improved without changing DSL semantics.

Performance Phases (Closed Status) ​

The kernel now has three completed performance phases:

PhaseWhat ClosedLatest Closure SignalPhase 1Batch write viability + lightweight journal hygieneStable 14ms-20ms batch writes per 100-item chunk, practical ceiling around 137,300 items before V8 heap residency became the limiter.Phase 2Bounded residency + columnar secret vector corpusColumnar secret chunks validated, Float32Array payload round-trip confirmed, and the 100k encrypted leaf-write microbenchmark reached 1886 vps with roughly 122MB post-GC heap.Phase 3Exact baseline + IVF sidecar searchOn a realistic chunk-coherent corpus, 100k vectors now search at 3.32s p95 with recall@10 = 1.000, 18.4 chunks/query, and 23.2x speedup over exact scan.## Phase Closure Summary ​

Phase 1 | Batch Write Path and Journal Discipline ​

Benchmarks:

• tests/Fase.1GB.js

• tests/Benchmarks/Fase.1.Final.BenchmarkReport.js What Phase 1 proved:

• The batch write path remained stable deep into the run instead of degrading quadratically.

• commitIndexedBatch() no longer retained full batch payloads in _memories; audit entries stayed lightweight.

• The practical ceiling was live V8 heap residency, not write-path collapse or journal duplication. Key closure numbers:

• Typical batch latency: 14ms-20ms per 100-item chunk

• Practical ceiling: ~137,300 items before heap OOM

• Peak heap region observed: ~1660MB

Phase 2 | Bounded Residency and Secret Vector Storage ​

Benchmarks:

• tests/Benchmarks/benchmark.vector-corpus.ts

• tests/Benchmarks/benchmark-100.ts What Phase 2 closed:

• Secret vector corpora now write as chunked columnar envelopes instead of tree-shaped JS payloads.

• Chunk reads come back as Float32Array payloads, not Array.from() materializations.

• The write path moved from “heap residency risk” to a stable, disk-backed bounded-residency model. Key closure signals:

• benchmark.vector-corpus.ts: columnarWrites > 0, Float32Array payload confirmed, chunk-read heap delta ~2.7MB

• benchmark-100.ts (leaf-write microbenchmark): 100k encrypted vectors in 0.9min, 1886 vps, ~122MB post-GC heap

Phase 3 | Exact Search + IVF Sidecar ​

Benchmarks:

• tests/Fase.3.0.search-exact.test.ts

• tests/Benchmarks/benchmark.search-exact-scale.ts

• tests/Benchmarks/benchmark.ivf-vs-exact.ts

• tests/Benchmarks/benchmark.ivf-tuning.ts What Phase 3 closed:

• searchExact() established a correctness baseline before approximate search.

• IVF became a persistent sidecar outside the kernel log, with explicit buildVectorIndex() and searchVector() APIs.

• Candidate-cap tuning, chunk-aware build, and realistic-vs-hostile corpus separation made ANN behavior measurable instead of anecdotal. Official 100k closure profiles:

CorpusExact p95IVF p95Recall@10Chunks / QuerySpeedupchunk_coherent (realistic)77129.34ms``3318.42ms``1.000``18.40``23.2x``legacy_fragmented (hostile)166603.63ms``19353.27ms``1.000``97.60``8.6xInterpretation:

• The realistic corpus is now the README-grade claim.
• The hostile corpus remains part of the suite to document worst-case chunk fragmentation honestly.
• The remaining Phase 3 caveat is build cost for large IVF indexes; search behavior itself is now closed for the realistic profile.

Benchmark Matrix ​

BenchmarkFileWhat It Proves#5 Sustained Mutationtests/Benchmarks/benchmark.5.sustained-mutation.test.tsThroughput stability over long mutation streams; p95 drift over time windows.#6 Fan-Out Sensitivitytests/Benchmarks/benchmark.6.fanout-sensitivity.test.tsLatency behavior as fan-out grows, with constant derivation complexity k.#7 Cold vs Warmtests/Benchmarks/benchmark.7.cold-warm-profiles.test.tsSeparation of cold setup cost vs warm and steady-state runtime.#8 Explain Overheadtests/Benchmarks/benchmark.8.explain-overhead.test.tsObservability overhead of explain(path) vs baseline mutation/read loops.#9 Secret-Scope Impacttests/Benchmarks/benchmark.9.secret-scope-impact.test.tsPublic vs secret latency envelope under equivalent workloads.#10 Push vs Pulltests/Benchmarks/benchmark.10.push-vs-pull.test.tsIsolation of write-only (push) and first-read-after-write (pull) in eager vs lazy modes.#11 Secret Push vs Pulltests/Benchmarks/benchmark.11.secret-push-vs-pull.test.tsSecret/public split of push vs pull; confirms secret-path cost structure after chunking/cache refactors.Regression Gatetests/Benchmarks/benchmark.regression-gate.test.tsCI pass/fail checks for p95 latency, k complexity bound, and stealth masking correctness.Vector Corpustests/Benchmarks/benchmark.vector-corpus.tsValidates columnar secret vector chunks, typed payload round-trip, and chunk-read heap discipline.Exact Search Scaletests/Benchmarks/benchmark.search-exact-scale.tsShows how exact vector search scales by chunk count and where decrypted-chunk cache thrash begins.IVF vs Exacttests/Benchmarks/benchmark.ivf-vs-exact.tsCompares exact scan and IVF sidecar on the same corpus.IVF Tuningtests/Benchmarks/benchmark.ivf-tuning.tsSweeps nlist, nprobe, and candidate caps; now also supports realistic and hostile corpus generators.## Latest Verified Local Runs ​

Machine: Suis-MacBook-Air Run context: isolated local runs on April 18-19, 2026

Notes:

• The tables below were taken from isolated runs on the current branch.
• #9 and #11 are the most sensitive to ambient machine load, so they should be read as performance envelopes, not hard SLAs.
• The historical pre-5.4/5.5 secret baseline still lives in tests/Benchmarks/BASELINE-5-FINAL.md.

#5 Throughput Under Sustained Mutation ​

MetricValue (ms)p500.0074p950.0109p990.0165max0.7165Windowed p95 drift: -40.97% (end window vs start window).

Interpretation:

• No upward drift under sustained updates.
• Throughput remains stable as history grows.

#6 Fan-Out Sensitivity Curves ​

Fanoutkp50 (ms)p95 (ms)p99 (ms)1020.01280.02040.093910020.00860.01480.029050020.00810.01090.0156100020.00650.00820.0104250020.00640.00790.0132500020.00620.00930.0111Interpretation:

• k stays constant at 2.
• Latency stays in micro-to-low-millisecond range across fanout values.

#7 Cold vs Warm Runtime Profiles ​

NodesCold (ms)Warm (ms)Steady Avg (ms)Steady Min (ms)Steady Max (ms)1000.17150.08920.01440.01080.090710000.01160.01230.00660.00580.012050000.01350.01400.00690.00570.0155Interpretation:

• Cold penalty is isolated.
• Warm/steady paths are consistently fast.

#8 Explain Overhead Budget ​

Modep50 (ms)p95 (ms)p99 (ms)baseline0.00870.01540.0370with_explain0.01410.01980.0298p95 overhead: 28.11%.

Interpretation:

• explain(path) adds bounded overhead while preserving traceability.
• Absolute overhead remains sub-millisecond, making it production-safe for real-time auditing.

#9 Secret-Scope Performance Impact ​

Latest isolated run:

Scopep50 (ms)p95 (ms)p99 (ms)public0.01020.01750.1177secret0.32770.36840.6566Interpretation:

• Secret path remains slower than public by design cost: crypto, stealth boundary logic, and lazy refresh/write-back.
• In absolute terms, secret p95 stays well below 1ms for this scenario.
• Compared with the March 2026 historical baseline in BASELINE-5-FINAL.md (4.6503ms secret p95), the current local value remains roughly 92% lower in absolute p95.
• Slowdown percentages still look dramatic because the public path is already extremely close to the timer floor.

#10 Push vs Pull (Eager vs Lazy) ​

Selected row from the latest isolated run (fanout = 5000):

ModeFanoutkMutation p95 (ms)Read p95 (ms)eager500020.00360.0033lazy500020.00360.0036Interpretation:

• Both modes remain low-latency in steady-state.
• push and first-read-after-write stay measurable as separate costs, which is the benchmark's main purpose.
• Single-run outliers can appear at lower fanouts, so this benchmark is best read as a shape test rather than a one-number SLA.

#11 Secret Push vs Pull (Shared v3 Key Cache + Lazy Recompute) ​

PlaneNodesMutation p95 (ms)Read p95 (ms)public1000.00870.0131secret1000.04060.4040public3000.00450.0058secret3000.02810.1945public6000.00430.0048secret6000.02520.3084Slowdown ratios (secret/public p95):

NodesMutation slowdown xRead slowdown x1004.67x30.84x3006.24x33.53x6005.86x64.25xInterpretation:

• Secret mutation cost stays in low-sub-millisecond territory.
• Secret read cost is still the noisiest part of the runtime because this benchmark couples crypto with lazy derivation refresh and write-back.
• In absolute terms, the latest isolated run still kept secret read p95 below 1ms at 100 and 600 nodes, and around 0.35ms at 300 nodes.

Regression and Vector Search Status ​

Latest gates now cover both the original kernel loop and the vector-search stack:

• latency_p95: ✅ kernel mutation/read loop remains in the low-millisecond envelope
• complexity_k: ✅ k=2 remains stable across fanout and graph size tests
• stealth_masking: ✅ secret origins remain masked in explain()
• searchExact: ✅ correctness baseline established before ANN
• IVF realistic: ✅ 100k, recall@10 = 1.000, IVF p95 = 3318.42ms
• IVF hostile: ✅ 100k, recall@10 = 1.000, IVF p95 = 19353.27ms

What Is Proven Now ​

• Public-path performance is stable and effectively bounded by small k.
• Lazy/eager recompute modes are operational and benchmarked.
• Explainability overhead is measurable and bounded.
• Secret-path cost is no longer monolithic; shared v3 key reuse removed the worst cold branch-derivation penalty.
• Secret reads are still the most expensive path, but they now sit in the sub-millisecond envelope for the main #9 scenario on isolated runs.
• Privacy and semantic invariants remain intact while performance work lands.
• The secret vector corpus is now genuinely columnar and typed, with Float32Array payloads on the hot path.
• Exact vector search is correct and measurable.
• IVF sidecars are now part of the supported runtime story, with separate realistic and hostile closure profiles.

Next Optimization Frontier ​

• On the kernel side, the next real frontier is still separating lazy derivation refresh from full memory append/hash-chain write-back on internal recomputes.
• On the vector side, the next frontier is IVF build cost and finer posting selectivity, not correctness.
• That is a structural systems task, not just another round of micro-optimizations.