4 papers
From Tokens to Regions: CUDA-Sensitive Instruction Tuning for GPU Kernel Generation
Wentao Chen, Jiace Zhu, Xing Zhe Chai +4
High-performance CUDA kernels are essential for scalable AI systems, while Large Language Models (LLMs) still struggle to generate correct kernels due to strict and implicit execut…
CUDABench: Benchmarking LLMs for Text-to-CUDA Generation
Jiace Zhu, Wentao Chen, Qi Fan +6
Recent studies have demonstrated the potential of Large Language Models (LLMs) in generating GPU Kernels. Current benchmarks focus on the translation of high-level languages into C…
Path-Consistency with Prefix Enhancement for Efficient Inference in LLMs
Jiace Zhu, Yuanzhe Huang, Yingtao Shen +2
To enhance the reasoning capabilities of large language models (LLMs), self-consistency has become a popular approach, combining multiple samplings with majority voting. However, c…
CUDA-LLM: LLMs Can Write Efficient CUDA Kernels
Wentao Chen, Jiace Zhu, Qi Fan +2
Large Language Models (LLMs) have demonstrated strong capabilities in general-purpose code generation. However, generating the code which is deeply hardware-specific, architecture-…