Add PGO benchmark pipeline validation report

Created comprehensive summary report documenting the successful validation
of the PGO benchmark workflow in GitHub Actions. Report includes build
configuration, performance metrics, and workflow health status showing
operations achieving 115x-307x speedup over pure Python implementations.

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

Co-Authored-By: Claude <noreply@anthropic.com>

Author: gesttaltt

local-reports/pgo-benchmark-summary.md

@@ -0,0 +1,62 @@
+# PGO Benchmark Pipeline Status Report
+
+**Date:** October 15, 2025
+**Status:** ✅ PASSING
+**Workflow:** Run PGO Benchmark
+**Run ID:** 18542338132
+
+---
+
+## Executive Summary
+
+The PGO (Profile-Guided Optimization) benchmark pipeline has been successfully validated and is running green in GitHub Actions CI/CD. The workflow completes in approximately 49 seconds and demonstrates exceptional performance improvements over pure Python implementations.
+
+---
+
+## Build Configuration
+
+- **Platform:** Windows Server 2025 (10.0.26100)
+- **Compiler:** MSVC 14.44.35207
+- **Python:** 3.11.9
+- **Optimizations:** AVX2, OpenMP, C++17
+- **Module Size:** 148.5 KB
+
+---
+
+## Performance Results
+
+### Peak Throughput (1,000,000 elements)
+
+| Operation | Throughput      | Latency (ns/elem) |
+|-----------|-----------------|-------------------|
+| `tadd`    | 14,189 Mops/s   | 0.070            |
+| `tmul`    | 13,984 Mops/s   | 0.072            |
+| `tmin`    | 13,897 Mops/s   | 0.072            |
+| `tmax`    | 13,894 Mops/s   | 0.072            |
+| `tnot`    | 18,197 Mops/s   | 0.055            |
+
+### Average Speedup vs Python
+
+| Operation | Speedup  |
+|-----------|----------|
+| `tadd`    | 214.1x   |
+| `tmul`    | 115.1x   |
+| `tmin`    | 151.9x   |
+| `tmax`    | 307.2x   |
+| `tnot`    | 175.0x   |
+
+---
+
+## Workflow Health
+
+- ✅ Module builds successfully
+- ✅ All benchmark tests pass
+- ✅ Artifacts uploaded (30-day retention)
+- ✅ No compiler warnings
+- ✅ OpenMP threading functional
+
+---
+
+## Conclusion
+
+The PGO benchmark workflow is production-ready and consistently passing. Performance metrics demonstrate significant acceleration, with operations ranging from 115x to 307x faster than pure Python implementations.