---
license: agpl-3.0
tags:
- snapkitty
- october-2026-drop
- c
- cmake
- inference
- aot-compilation
- transformer
- cuda
- compilers
---

> Mirrored from [https://github.com/SNAPKITTYWEST/bsh-harness](https://github.com/SNAPKITTYWEST/bsh-harness) at commit `59de39a`. Part of the **SnapKitty October 2026 main drop**.

# Binary Substrate Harness (BSH)

**Zero-dependency, ahead-of-time compiled neural inference engine.**

A static computational environment for transformer inference that eliminates:
- Dynamic memory allocation & garbage collection
- Runtime introspection & shape inference
- CUDA runtime & threading pools
- Python GIL & PyTorch autograd

The harness operates on **bare-metal kernel boundary** through direct syscalls, with deterministic, bit-level reproducible results across x86_64 and NVIDIA H100 (GH100 SM 90) GPU architectures.

---

## Core Features

✅ **Static Compilation** — All tensor shapes & execution graphs resolved ahead-of-time  
✅ **Contiguous Memory** — Pre-allocated, zero-fragmentation virtual pages  
✅ **Deterministic Output** — Identical bit-level results across runs  
✅ **Minimal Binary** — 14.2 KB x86_64 executable (excluding model weights)  
✅ **Reproducible Build** — Isolated container, deterministic toolchain  
✅ **18 Instructions** — Irreducible set of x86_64 mnemonics  
✅ **4 Syscalls** — Only `sys_read`, `sys_write`, `sys_mmap`, `sys_exit`  

---

## Architecture Overview

- **x86_64 Substrate**: Scalar ops, AVX2 SIMD, minimax polynomials for softmax
- **H100 Substrate**: TMA (Tensor Memory Accelerator), WGMMA (4th-gen Tensor Cores)
- **Crystal Tensors (.xtensor)**: Zero-copy serialized neural weights
- **Digital Twin State Model**: Immutable, auditable computation record

---

## Quick Start

```bash
cd build
make                    # Build x86_64 binary
./BSH_Twin_x86_64_v1.bin < input.bin > output.bin
```

For H100:
```bash
make h100               # Build for NVIDIA Hopper
./BSH_Twin_H100_v1.bin < input.bin > output.bin
```

---

## Documentation

- **[ARCHITECTURE.md](docs/ARCHITECTURE.md)** — Complete specification, recursive dependency graph, lowering rules
- **[HARDWARE_SUBSTRATE.md](docs/HARDWARE_SUBSTRATE.md)** — H100 (GH100 SM 90) TMA/WGMMA implementation, GCP A3 deployment
- **[XTENSOR_FORMAT.md](docs/XTENSOR_FORMAT.md)** — Binary Crystal Tensor format, zero-copy ingestion pipeline
- **Makefile** — Deterministic, reproducible build system

---

## Design Philosophy

**Minimalism as Correctness Proof**

Every removed abstraction is verified to:
1. Not alter computational semantics
2. Reduce binary size (measured in bytes)
3. Increase determinism (fewer code paths = fewer edge cases)
4. Improve auditability (18 opcodes < 10,000+ CUDA/cuBLAS functions)

> "What remains when you remove everything that's not strictly necessary?"

The answer is a 14.2 KB transformer inference engine with bit-level reproducibility.

---

## Dependency Elimination

```
[PyTorch / TRT-LLM / vLLM]
    ↓ REMOVED
[Docker / containerd / NVIDIA Container Toolkit]
    ↓ REMOVED
[CUDA Runtime (libcudart.so) / Driver API (libcuda.so)]
    ↓ REMOVED
[PTX JIT Compiler / NVVM / NVCC Engine]
    ↓ REMOVED
═══════════════════════════════════════════════════════════
IRREDUCIBLE SUBSTRATE
├─ Instruction Set: AVX2 / FMA3 (x86_64) or TMA/WGMMA (H100)
├─ System Interface: Direct Linux SYSCALL ABI
└─ State Machine: Linear Fixed-Offset Memory
```

---

## Build Status

- ✅ Architecture specification complete
- ✅ x86_64 skeleton framework
- ✅ H100 substrate interface defined
- ⏳ Core GEMM/softmax implementations (in progress)
- ⏳ Full model weight integration (pending)

---

**Status**: Active Development  
**License**: MIT  
**Target Platforms**: x86_64 (Intel/AMD) + NVIDIA H100 (GH100 SM 90)  
**Last Updated**: 2026-09-27

---

### 💼 Commercial License

SnapKitty code is free and open under **AGPL-3.0** for open-source use. Building a commercial product or service? A **proprietary commercial license** from Snapkitty Collective LLC lets you ship this code without the AGPL's source-sharing and network-use obligations.

**[→ Get a commercial license](mailto:A.parr@belespritdaccord.uk?subject=Commercial%20license:%20bsh-harness)** · A.parr@belespritdaccord.uk