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Cortex: an innovative kernel subsystem and data fabric that unifies multiple memory tiers—from CPU cache to GPU VRAM and NVMe/CXL—into a single virtual address space. It uses predictive ML to move data, cutting latency and raising application throughput. This technical solution accelerates performance with minimal code changes.

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Astra-Guardian/Cortex

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Cortex CI/CD System

This document describes the complete GitHub Actions CI/CD pipeline for the Cortex memory tiering system.

Overview

The Cortex project includes comprehensive automated testing and deployment workflows that support cross-platform development on Linux, Windows, and macOS.

Workflows

1. Continuous Integration (.github/workflows/ci.yml)

Triggers:

  • Push to any branch
  • Pull requests
  • Manual dispatch

Platforms: Linux, Windows, macOS

Features:

  • Cross-platform builds using CMake
  • Kernel module compilation (Linux)
  • Native benchmark compilation (all platforms)
  • Unit testing
  • Cross-platform analysis
  • Artifact upload

Key Steps:

  1. Platform-specific dependency installation
  2. CMake configuration and build
  3. Test execution
  4. Cross-platform result analysis
  5. Build artifact archival

2. Release Automation (.github/workflows/release.yml)

Triggers:

  • Git tags matching v*.*.* pattern
  • Manual dispatch with version input

Features:

  • Automated GitHub releases
  • Cross-platform binary packaging
  • Source code archival
  • Release notes generation

Artifacts:

  • cortex-linux-x64.tar.gz - Linux binaries
  • cortex-windows-x64.zip - Windows binaries
  • cortex-macos-x64.tar.gz - macOS binaries
  • cortex-source.tar.gz - Source code archive

3. Performance Testing (.github/workflows/performance.yml)

Triggers:

  • Daily schedule (02:00 UTC)
  • Manual dispatch with configurable parameters

Parameters:

  • platforms: Comma-separated list (linux,windows,macos)
  • benchmark_size: Memory size in MB (default: 512)
  • iterations: Number of test iterations (default: 10)

Features:

  • Cross-platform performance benchmarks
  • Statistical analysis and reporting
  • Performance trend tracking
  • Automated PR comments with results

Jobs:

  1. performance-linux: Simulated kernel module performance
  2. performance-windows: Native WinBench execution
  3. performance-macos: Native macOS benchmark
  4. performance-analysis: Cross-platform comparison

Platform-Specific Details

Linux

  • Environment: Ubuntu Latest
  • Build System: CMake + GCC
  • Components: Helios kernel module, examples, tools
  • Testing: Simulated performance (actual kernel module requires root)
  • Artifacts: ELF binaries, analysis results

Windows

  • Environment: Windows Latest
  • Build System: CMake + MSVC
  • Components: WinBench native benchmark, WinViz GUI
  • Testing: Full benchmark execution
  • Artifacts: PE executables, performance data

macOS

  • Environment: macOS Latest
  • Build System: CMake + Clang
  • Components: Native benchmark with Mach APIs
  • Testing: Full benchmark execution
  • Artifacts: Mach-O binaries, performance data

Supporting Scripts

.github/scripts/ci_summary.py

Generates comprehensive CI build summaries with platform status, build artifacts, and cross-platform comparisons.

.github/scripts/linux_perf_sim.py

Simulates Linux kernel module performance for CI environments where actual kernel modules cannot be loaded.

.github/scripts/perf_analysis.py

Analyzes cross-platform performance benchmark results and generates unified reports.

Configuration

Environment Variables

  • BUILD_TYPE: Release/Debug build configuration
  • BENCHMARK_SIZE: Memory benchmark size in MB
  • ITERATIONS: Number of performance test iterations

Matrix Strategy

strategy:
  matrix:
    os: [ubuntu-latest, windows-latest, macos-latest]
    include:
      - os: ubuntu-latest
        platform: linux
      - os: windows-latest  
        platform: windows
      - os: macos-latest
        platform: macos

Artifacts and Retention

Build Artifacts (30 days)

  • Cross-platform binaries
  • Analysis results
  • Test outputs

Performance Reports (90 days)

  • Historical performance data
  • Cross-platform comparisons
  • Statistical analysis

Release Artifacts (Permanent)

  • Tagged release binaries
  • Source code archives
  • Release documentation

Usage Examples

Manual CI Run

# Trigger CI workflow for specific platforms
gh workflow run ci.yml -f platforms="linux,windows"

Performance Testing

# Run performance tests with custom configuration
gh workflow run performance.yml \
  -f platforms="linux,macos" \
  -f benchmark_size="1024" \
  -f iterations="20"

Create Release

# Tag-based release
git tag v1.0.0
git push origin v1.0.0

# Manual release
gh workflow run release.yml -f version="v1.0.1-rc1"

Monitoring and Debugging

Workflow Status

Monitor workflow execution in the GitHub Actions tab:

  • Build status and logs
  • Artifact downloads
  • Performance trends

Common Issues

  1. YAML Syntax Errors

    • Validate YAML with yamllint
    • Check heredoc and multiline string formatting
  2. Platform Build Failures

    • Review platform-specific dependency installation
    • Check CMake configuration flags
  3. Performance Variations

    • Compare against historical baselines
    • Account for CI runner performance variance

Debugging Commands

# Check workflow syntax
yamllint .github/workflows/*.yml

# Test local build
cmake -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build --parallel

# Validate analysis scripts
python3 .github/scripts/perf_analysis.py

Security Considerations

  • All workflows use pinned action versions (@v4)
  • No sensitive data in public repositories
  • Limited token permissions for automated tasks
  • Secure artifact handling and retention policies

Future Enhancements

  1. Performance Regression Detection

    • Automated baseline comparisons
    • Alert on significant performance degradation
  2. Extended Platform Support

    • ARM64 architectures
    • Additional Linux distributions
  3. Advanced Analytics

    • Performance trend visualization
    • Automated optimization recommendations
  4. Integration Testing

    • Cross-platform compatibility validation
    • End-to-end system testing

Maintenance

Regular Tasks

  • Update action versions quarterly
  • Review and clean old artifacts
  • Update platform dependencies
  • Validate performance baselines

Troubleshooting Contacts

  • CI/CD Issues: DevOps team
  • Performance Analysis: Performance engineering team
  • Platform-specific builds: Platform maintainers

This CI/CD system provides a robust foundation for the Cortex project's development, testing, and deployment needs across all supported platforms.

About

Cortex: an innovative kernel subsystem and data fabric that unifies multiple memory tiers—from CPU cache to GPU VRAM and NVMe/CXL—into a single virtual address space. It uses predictive ML to move data, cutting latency and raising application throughput. This technical solution accelerates performance with minimal code changes.

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