RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks
In modern AI frameworks, GPU kernels are key to overall system performance. Combining usability, portability, and near-handwritten CUDA performance, Triton is widely adopted for implementing GPU kernels. Recent advances show the potential of large language models (LLMs) to automatically generate Triton kernels, reducing the manual effort required from expert kernel developers. Several benchmarks evaluate LLM-generated Triton kernels. However, they suffer from three key limitations: (1) they restrict tasks to PyTorch-to-Triton translation, failing to reflect the diversity and complexity of real-world Triton tasks; (2) they evaluate only individual-kernel performance rather than end-to-end ...
| Authors | Jinjun Huang, Zhongzhen Wen, Tongtong Xu et al. |
|---|---|
| Primary category | cs.SE |
| Published | 12 August 2026 |
| Last updated at source | 12 August 2026 |
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