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CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

arXiv – CS AI|Jiace Zhu, Wentao Chen, Qi Fan, Zhixing Ren, Junying Wu, Xing Zhe Chai, Chotiwit Rungrueangwutthinon, Yehan Ma, An Zou|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers introduce CUDABench, a comprehensive benchmark for evaluating Large Language Models' ability to generate CUDA code from text descriptions. The benchmark reveals significant challenges including high compilation success rates but low functional correctness, lack of domain-specific knowledge, and poor GPU hardware utilization.

Key Takeaways

→CUDABench is the first comprehensive benchmark specifically designed to evaluate LLMs' text-to-CUDA generation capabilities.
→The benchmark covers diverse application domains including AI, scientific computing, and data analytics with breadth-depth-difficulty evaluation.
→Testing reveals a notable mismatch between high compilation success rates and low functional correctness in LLM-generated CUDA code.
→Current LLMs lack domain-specific algorithmic knowledge for effective GPU programming.
→LLMs demonstrate suboptimal utilization of GPU hardware resources in generated code.

#llm #cuda #gpu-programming #benchmark #code-generation #artificial-intelligence #performance-evaluation #research #programming

Read Original →via arXiv – CS AI

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