CUDA-Harness addresses the expertise barrier in writing high-performance CUDA kernels by enabling direct generation from natural language. Unlike existing tools that transpile from high-level frameworks like PyTorch, this system handles low-level implementation and validation directly from semantic input. It leverages agentic workflows to overcome the limitations of standard reward-based models, which often struggle with correctness and hardware-aware optimization.
- Moves beyond Torch2CUDA transpilation to true Text2CUDA kernel generation.
- Uses agentic LLM workflows to handle complex low-level implementation tasks.
- Reduces the specialized expertise barrier for writing optimized CUDA code.
- Addresses reward hacking vulnerabilities common in standard LLM code generation.
- Integrates hardware-aware parallel optimization into the natural language generation loop.