@inproceedings{liu-etal-2026-amdkernelvault,
title = "{AMDK}ernel{V}ault: Large-Scale Datasets and Agentic Training for {AMD} {GPU} Kernel Optimization",
author = "Liu, Ji and
Majumder, Saptarshi and
Huang, Yiqing and
Ouyang, Wenwen and
Pandey, Umang and
Li, Zeping and
Chen, Chushi and
An, Zihao and
Yang, Puyuan and
Li, Zekai and
Rafati, Sina and
Liu, Ziqiong and
Brahma, Pratik Prabhanjan and
Li, Dong and
Liu, Zicheng and
Zhou, Sharon and
Barsoum, Emad",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1493/",
pages = "31775--31798",
ISBN = "979-8-89176-462-0",
abstract = "We introduce AMDKernelVault, an open corpus and training framework for optimizing HIP and Triton kernels on recent AMD CDNA GPUs. Existing LLM-based kernel agents are predominantly designed for CUDA and NVIDIA hardware and often rely on repeated calls to frontier models for generation, reflection, and optimization. To address this gap, we develop HIPKernelGen and TritonKernelGen, agent-driven pipelines that translate PyTorch references into GPU kernels, compile and validate candidates under ROCm, and profile their latency on AMD hardware. AMDKernelVault contains 62,153 execution-verified HIP kernel samples, 2,377 production-grounded ROCm Libraries QA entries, and 39,893 Triton kernels. To demonstrate the corpus{'}s utility, we specialize Qwen3-8B through supervised fine-tuning and execution-aware reinforcement learning. Under fixed evaluation budgets and identical agent settings, the resulting model achieves the highest correctness among the evaluated models on PyTorch-to-HIP (34.0{\%} Pass@1), TritonBench-G (33.2{\%} Corr@3), and ROCmBench (41.94{\%} Corr@3), while running locally on AMD hardware. These advantages are specific to the reported correctness metrics and do not extend uniformly to compilation accuracy or execution speed. The corpus and documentation are publicly available at https://huggingface.co/datasets/amd/AIG-Datasets."
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<abstract>We introduce AMDKernelVault, an open corpus and training framework for optimizing HIP and Triton kernels on recent AMD CDNA GPUs. Existing LLM-based kernel agents are predominantly designed for CUDA and NVIDIA hardware and often rely on repeated calls to frontier models for generation, reflection, and optimization. To address this gap, we develop HIPKernelGen and TritonKernelGen, agent-driven pipelines that translate PyTorch references into GPU kernels, compile and validate candidates under ROCm, and profile their latency on AMD hardware. AMDKernelVault contains 62,153 execution-verified HIP kernel samples, 2,377 production-grounded ROCm Libraries QA entries, and 39,893 Triton kernels. To demonstrate the corpus’s utility, we specialize Qwen3-8B through supervised fine-tuning and execution-aware reinforcement learning. Under fixed evaluation budgets and identical agent settings, the resulting model achieves the highest correctness among the evaluated models on PyTorch-to-HIP (34.0% Pass@1), TritonBench-G (33.2% Corr@3), and ROCmBench (41.94% Corr@3), while running locally on AMD hardware. These advantages are specific to the reported correctness metrics and do not extend uniformly to compilation accuracy or execution speed. The corpus and documentation are publicly available at https://huggingface.co/datasets/amd/AIG-Datasets.</abstract>
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%0 Conference Proceedings
%T AMDKernelVault: Large-Scale Datasets and Agentic Training for AMD GPU Kernel Optimization
%A Liu, Ji
%A Majumder, Saptarshi
%A Huang, Yiqing
%A Ouyang, Wenwen
%A Pandey, Umang
%A Li, Zeping
%A Chen, Chushi
%A An, Zihao
%A Yang, Puyuan
%A Li, Zekai
%A Rafati, Sina
%A Liu, Ziqiong
%A Brahma, Pratik Prabhanjan
%A Li, Dong
%A Liu, Zicheng
%A Zhou, Sharon
%A Barsoum, Emad
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F liu-etal-2026-amdkernelvault
%X We introduce AMDKernelVault, an open corpus and training framework for optimizing HIP and Triton kernels on recent AMD CDNA GPUs. Existing LLM-based kernel agents are predominantly designed for CUDA and NVIDIA hardware and often rely on repeated calls to frontier models for generation, reflection, and optimization. To address this gap, we develop HIPKernelGen and TritonKernelGen, agent-driven pipelines that translate PyTorch references into GPU kernels, compile and validate candidates under ROCm, and profile their latency on AMD hardware. AMDKernelVault contains 62,153 execution-verified HIP kernel samples, 2,377 production-grounded ROCm Libraries QA entries, and 39,893 Triton kernels. To demonstrate the corpus’s utility, we specialize Qwen3-8B through supervised fine-tuning and execution-aware reinforcement learning. Under fixed evaluation budgets and identical agent settings, the resulting model achieves the highest correctness among the evaluated models on PyTorch-to-HIP (34.0% Pass@1), TritonBench-G (33.2% Corr@3), and ROCmBench (41.94% Corr@3), while running locally on AMD hardware. These advantages are specific to the reported correctness metrics and do not extend uniformly to compilation accuracy or execution speed. The corpus and documentation are publicly available at https://huggingface.co/datasets/amd/AIG-Datasets.
%U https://aclanthology.org/2026.findings-emnlp.1493/
%P 31775-31798
Markdown (Informal)
[AMDKernelVault: Large-Scale Datasets and Agentic Training for AMD GPU Kernel Optimization](https://aclanthology.org/2026.findings-emnlp.1493/) (Liu et al., Findings 2026)
ACL
- Ji Liu, Saptarshi Majumder, Yiqing Huang, Wenwen Ouyang, Umang Pandey, Zeping Li, Chushi Chen, Zihao An, Puyuan Yang, Zekai Li, Sina Rafati, Ziqiong Liu, Pratik Prabhanjan Brahma, Dong Li, Zicheng Liu, Sharon Zhou, and Emad Barsoum. 2026. AMDKernelVault: Large-Scale Datasets and Agentic Training for AMD GPU Kernel Optimization. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 31775–31798, Budapest, Hungary. Association for Computational Linguistics.