Show HN: Sovereign-Metal – Zero-Dependency Python/Metal GPGPU Advection

Sovereign-Metal, a zero-dependency Python-to-Metal GPGPU advection engine and local transformer pipeline, has been released on GitHub. The project enables direct MSL kernel programming from Python with zero-copy unified memory, branchless toroidal boundary wrapping, and standalone FP16 BERT-style embeddings on Apple GPUs. It targets high-throughput advection simulations and local transformer inference for the GPGPU developer community.

A zero-dependency, high-throughput Python-to-Metal GPGPU advection engine and local transformer pipeline. Program custom MSL Metal Shading Language kernels directly from Python with zero-copy unified memory alignment and zero PCIe bus latency. Zero-Copy Unified Memory: Directly maps NumPy arrays into shared CPU/GPU buffers MTLResourceStorageModeShared , completely bypassing PCIe transfer bottlenecks. Branchless Toroidal Boundary Wrapping: Implements hardware-level bitwise wrapping x - 1u & dim - 1u inside MSL shaders, avoiding thread divergence and division stalls on integrated Apple/Intel GPUs. Zero-Latency Reduction Tree: Runs a dedicated Shannon entropy, localization intensity, and energy proxy reduction kernel entirely in threadgroup scratchpad memory. Standalone GPGPU Transformer: Runs FP16 BERT-style embeddings all-MiniLM-L6-v2 locally on Metal with strict GPGPU acceleration. The advection advects a continuous scalar field Where: - $\nu$ represents the dissipation/damping coefficient. - $\alpha$ is the advection coupling strength. - $\kappa$ is the non-linear soliton crystallization rate. Clone the repository and install the dependencies: cd sovereign-metal pip install -e . Verify the local GPGPU transformer inference: python examples/local embeddings.py Execute the soliton advection simulation and watch the real-time crystallization metrics stream from the GPU: python examples/benchmark advection.py We stand on the shoulders of giants. A sincere thank you to: The SentenceTransformers Team: For developing the incredibly efficient all-MiniLM-L6-v2 model weights architecture. Hugging Face / Rust Tokenizers Team: For building the blazingly fast tokenization library that powers our input pipeline. Apple Metal Team: For providing the hardware-native GPGPU framework that makes continuous cognitive advection possible. MIT License. Crafted for the GPGPU developer community.