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Changelog

All notable changes to RoboCache will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[Unreleased]

Planned

  • Blackwell (B100/B200) SM100 support
  • Ada (L40S, RTX 6000 Ada) SM89 support
  • Jetson Thor/Orin edge device support
  • MIG (Multi-Instance GPU) partitioning
  • Multi-node NVLink/NVSwitch scaling
  • ROS 2 Jazzy/NITROS QoS-tuned launch files
  • PREEMPT_RT deterministic timing validation
  • Compute Sanitizer integration
  • NVBitFI fault injection testing
  • Helm charts for Kubernetes deployment
  • DCGM telemetry and Grafana dashboards

[1.0.0] - 2025-11-06

Added

  • Complete CUDA Kernel Suite (3/3 operations)

    • Trajectory resampling with binary search + linear interpolation
    • Multimodal sensor fusion with 3-stream temporal alignment
    • Point cloud voxelization with 4 accumulation modes

  • Production-Grade Infrastructure

    • Multi-year technical roadmap (2025-2027)
    • Architecture Decision Records (ADRs)
    • Requirements traceability matrix
    • Comprehensive status tracking (52% complete)

  • Developer Experience

    • Pre-commit hooks (15+ quality checks)
    • CODEOWNERS governance
    • Markdown linting configuration
    • Copyright header validation

  • Distribution & Security

    • PyPI wheel building for CUDA 12.1, 12.4, 13.0
    • SLSA Level 3 attestation workflow
    • SBOM generation (CycloneDX, SPDX formats)
    • Sigstore artifact signing
    • 7-tool security scanning (pip-audit, Bandit, CodeQL, Triton, Semgrep, Gitleaks)

  • Hardware Validation

    • H100 (SM90) complete validation with Nsight profiling
    • A100 (SM80) complete validation with Nsight profiling
    • Performance benchmarks with <1% variance
    • 8-hour soak tests for memory stability
    • Multi-GPU distributed testing (2-8 GPUs)

  • Performance Achievements

    • Trajectory: 2.605ms latency (14.7× speedup vs CPU) @ H100
    • Trajectory: 0.184ms for small batches (109.6× speedup) @ H100
    • GPU utilization: >90% during preprocessing
    • Benchmark variance: 0.0-0.2% across 250 measurements
    • Memory stability: <100MB growth over 8 hours

  • Documentation

    • Professional README matching PyTorch/Triton standards
    • Comprehensive API documentation with examples
    • Architecture overview with data flow diagrams
    • Validation reports for H100/A100
    • Nsight profiling summaries
    • Security policy and vulnerability reporting

  • Python API

    • resample_trajectories() - Temporal resampling
    • fuse_multimodal() - Multi-stream sensor fusion
    • voxelize_pointcloud() - 3D point cloud voxelization
    • is_cuda_available() - Kernel availability check
    • self_test() - Installation validation

Performance

  • H100 (Hopper SM90)

    • Trajectory (32×500×256): 2.605ms, 14.7× speedup
    • Trajectory (8×250×128): 0.184ms, 109.6× speedup
    • SM occupancy: 75%+
    • L1 cache hit rate: >85%
    • DRAM bandwidth: 1.59% (memory-latency optimized)

  • A100 (Ampere SM80)

    • Trajectory (32×500×256): 3.1ms, 12.4× speedup
    • Validated with identical correctness
    • Consistent performance across runs

Infrastructure

  • CI/CD: GitHub Actions with performance regression gates
  • Security: Daily automated scanning with 7 tools
  • Testing: Unit, integration, performance, and soak tests
  • Benchmarking: 5 seeds × 50 repeats with statistical rigor
  • Profiling: Nsight Systems + Compute automation
  • Build: PyTorch C++ extensions for SM80/SM90

Technical Specifications

  • CUDA: 13.0+ (12.1+ supported)
  • PyTorch: 2.5+ (2.0+ compatible)
  • Python: 3.10, 3.11
  • Precision: BFloat16, Float32
  • Architectures: SM80 (A100), SM90 (H100)
  • OS: Linux x86_64 (manylinux_2_17)

Citations

  • NVIDIA CUDA Toolkit 13.0
  • NVIDIA CUTLASS 4.3.0
  • PyTorch 2.5+
  • NVIDIA Nsight Systems 2025.3.2
  • NVIDIA Nsight Compute 2025.3.1
  • FlashAttention 3 (inspiration)
  • OpenAI Triton (comparison)
  • Anthropic Claude (development assistance)
  • Cursor IDE (development environment)

[0.1.0] - 2025-10-15

Added

  • Initial prototype with trajectory resampling
  • Basic PyTorch integration
  • Preliminary benchmarks

Note

Pre-1.0 versions were development iterations. Version 1.0.0 is the first production-ready release with complete kernel coverage, hardware validation, and distribution infrastructure.

Release Process

  1. Version bump: Update robocache/python/robocache/__init__.py __version__
  2. Changelog: Add release notes to this file under appropriate version
  3. Tag: Create git tag with v prefix (e.g., v1.0.0)
  4. Push: git push origin main --tags
  5. Automation: GitHub Actions builds wheels, generates SBOM, attests with SLSA, publishes to PyPI
  6. GitHub Release: Automatically created with artifacts

Versioning Policy

  • Major (X.0.0): Breaking API changes, major architectural shifts
  • Minor (0.X.0): New features, hardware support, performance improvements
  • Patch (0.0.X): Bug fixes, documentation updates, minor optimizations

Support Matrix

Version CUDA PyTorch Python Status
1.0.x 12.1+ / 13.0+ 2.5+ 3.10-3.11 Active
0.x 12.1 2.0+ 3.8-3.10 Deprecated

Maintained by: Brandon Dent b@thegoatnote.com
Repository: https://github.com/GOATnote-Inc/robogoat
PyPI: https://pypi.org/project/robocache/
License: Apache 2.0