Compiler-Grounded Hierarchical Diagnosis for LLM Triton Kernel Optimization
These signals reveal that a kernel is slow, but not why the backend compiler fails to realize a profitable optimization, especially on emerging accelerators such as NPUs. We therefore formulate kernel optimization as a progressive cross-layer diagnosis problem that links runtime symptoms to IR structure and compiler behavior before rewriting source. We implement the system on Triton for Ascend NPUs and evaluate it on 37 successfully converted entries from a standardized NPUKernelBench-derived Ascend 950 benchmark.
- ▪These signals reveal that a kernel is slow, but not why the backend compiler fails to realize a profitable optimization, especially on emerging accelerators such as NPUs.
- ▪We therefore formulate kernel optimization as a progressive cross-layer diagnosis problem that links runtime symptoms to IR structure and compiler behavior before rewriting source.
- ▪We implement the system on Triton for Ascend NPUs and evaluate it on 37 successfully converted entries from a standardized NPUKernelBench-derived Ascend 950 benchmark.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2607.23089 |
| Publication time | Mon, 03 Aug 2026 22:53:27 +0000 |
| Retrieval time | 2026-08-03T23:05:41.025Z |
| Last seen | 2026-08-03T23:05:41.025Z |
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Computer Science > Artificial Intelligence arXiv:2607.23089 (cs) [Submitted on 25 Jul 2026] Title:Compiler-Grounded Hierarchical Diagnosis for LLM-Based Triton Kernel Optimization Authors:Dongjie Chen, Ping Zhao, Bohua Zhan, Yulong Wang, Shushu Chen, Liangjun Feng, Hao Zhou, Min Shen, Linmu Wang, Weijia Sheng, Xiangyu Wei, Weijie Ding, Jianhui Huang, Yaoqing Gao View a PDF of the paper titled Compiler-Grounded Hierarchical Diagnosis for LLM-Based Triton Kernel Optimization, by Dongjie Chen and 13 other authors View PDF HTML (experimental) Abstract:Recent advances in large language models (LLMs) have enabled automated kernel generation and optimization, but most existing approaches rely on surface signals such as compilation feedback and profiling metrics.
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