CUDA-Harness: Harnessing Agentic CUDA Kernel Generation and Optimization from Natural Language
Developing high-performance CUDA kernels demands specialized knowledge in algorithm implementation, correctness validation, and hardware-aware parallel optimization, creating a substantial expertise barrier and making generating CUDA kernels directly from natural language (Text2CUDA) essential. Meanwhile, the general-purpose code generation capability of Large Language Models (LLMs) prompts a series of works exploring LLM-based CUDA kernel generation. They mainly focus on transpilation from high-level frameworks such as PyTorch to CUDA (Torch2CUDA) rather than Text2CUDA, where models must understand the high-level input semantics and handle low-level kernel implementation and validation. ...
| Authors | Qi Fan, An Zou, Yehan Ma |
|---|---|
| Primary category | cs.CL |
| Published | 30 August 2026 |
| Last updated at source | 30 August 2026 |
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