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VALORI KERNEL

Engineering Verification Report — v0.1.0

CONFIDENTIAL — ARCHITECTURE PREVIEW
Author: Varshith Gudur

TEST ENVIRONMENT SPECIFICATION

  • Hardware Model: Apple MacBook Air (2022)
  • Processor: Apple M2 (ARM64)
  • Memory: 8 GB Unified Memory
  • Storage: 256 GB SSD (APFS)
  • Operating System: macOS Sonoma 14.x
  • Kernel Version: Valori v0.1.0 (release build)
  • Compiler Flags: RUSTFLAGS="-C target-cpu=native"
  • Concurrency: Single-Threaded Execution (Baseline)
  • Math Precision: Q16.16 Fixed-Point (Deterministic)

This report documents validation of deterministic execution, recovery safety, metadata isolation, and algorithmic fidelity. All benchmarks were executed on consumer hardware to demonstrate conservative baseline guarantees.

1. Disaster Recovery & Persistence Validation

Purpose: Validate that the kernel can recover from process termination with no state loss and sub-second recovery time. This matters because trading platforms require deterministic restart behavior after crash, reboot, or operating system preemption.

Persistence Benchmark — Verified

  • Ingest Size: 50,000 vectors
  • Snapshot Save Time: ~48 ms
  • Cold Load Time: ~35 ms
  • Restored Graph Size: 50,000
  • Deterministic constraint enforced.

Persistence Benchmark Log Persistence benchmark execution log — save and cold recovery timing.

Why this matters: Recovery Time Objective (RTO) under 40 ms ensures algorithms resume execution without warm-up intervals. This eliminates cold-start latency risk commonly observed in Python and JVM-based vector systems.

2. Regulatory Compliance — Metadata Segregation

Purpose: Demonstrate kernel-level enforcement of cohort isolation to satisfy Chinese-wall constraints between trading domains. Isolation occurs inside the traversal path rather than at query layer, making leakage structurally impossible.

Metadata Filter Benchmark — Verified

  • Dataset Size: 10,000 tagged vectors
  • Result: 0 cross-cohort leakage
  • Hybrid Search: Vector + Metadata

Metadata Filter Log Metadata filter benchmark demonstrating strict cohort enforcement.

Why this matters: Compliance workflows require explainable separation between risk books. By resolving cohort membership through O(1) in-memory lookup during traversal, cross-domain leakage cannot occur.

3. Algorithmic Integrity — Recall Accuracy Validation

Purpose: Validate that fixed-point deterministic arithmetic preserves retrieval accuracy relative to floating-point ground truth. Accuracy is evaluated against the SIFT1M dataset using brute-force reference queries.

SIFT1M Recall Benchmark — Verified

  • Recall@1: 99%
  • Recall@10: 99%
  • Latency: ~0.5 ms/query

Recall Benchmark Log Recall accuracy validation against SIFT1M reference set.

Why this matters: The kernel guarantees deterministic output without sacrificing signal quality. This enables reproducible backtests and audit-traceable execution across research and production environments.

4. Determinism Cost Profile — Hot-Path Execution

Purpose: Measure hot-path execution cost and quantify overhead introduced by deterministic math constraints. Cold IO, structure decode, and deterministic compute are evaluated independently.

Hot Path Cost Deterministic hot-path cost breakdown and throughput computation.

Why this matters: Separation of IO and deterministic math demonstrates that latency is bounded by hardware memory bandwidth, not algorithmic overhead — a key requirement for predictable execution in trading systems.

6. Ingestion Throughput — Baseline Single-Core Reference

Ingestion Benchmark — Baseline

  • Events: 1,000,000
  • Execution Mode: Single-Threaded (Laptop)
  • Throughput: ~1,634 EPS (Note: 1.2M+ vectors/sec via bulk load)
  • Scaling Model: Linear via Sharding

Ingestion Benchmark Log End-to-end ingestion benchmark execution trace.

Why this matters: The benchmark represents a conservative baseline under consumer hardware. Architecture supports linear throughput expansion across sharded workers in production deployments.

End of Verification Record