arXiv:2607. 17979v1 Announce Type: cross Abstract: Large language models (LLMs) can assist GPU kernel generation, but their practical effectiveness depends on whether generated code can be reliably constrained, validated, profiled, and selected.
By Yue Shui, Chenyu Ma, Hangfei Xu, Shengzhao Wen, Yanpeng Wang
arXiv:2605.28213v2 Announce Type: replace
Abstract: LLM-based agents are increasingly used to generate GPU kernels, but they often struggle to determine when an optimization is sound because its requ...
By Shuoming Zhang, Qiuchu Yu, Ruiyuan Xu, Chenjing Zhang, Junjie Peng, Zhicheng Xie, Yangyu Zhang, Xiyu Shi, Ying Liu, Guangli Li, Xiaobing Feng, Huimin Cui, Xingjun Zhang, Jiacheng Zhao
arXiv:2606. 26758v1 Announce Type: new Abstract: High-performance GPU kernels are critical for reducing the exponentially growing computational costs of large language models (LLMs), but their development heavily relies on manual tuning by domain experts.
By Yaochen Han, Ke Fan, Hongxu Jiang, Wanqi Xu, Weiyu Xie, Runhua Zhang, Chenhui Zhu, Yixiang Zhang
arXiv:2604.18616v2 Announce Type: replace-cross
Abstract: LLM coding agents can generate correct GPU kernels, but their performance still trails expert libraries. Reaching peak throughput requires co...
By Haohui Mai, Xiaoyan Guo, Xiangyun Ding, Daifeng Li, Qiuchu Yu, Chenzhun Guo, Cong Wang, Jiacheng Zhao, Christos Kozyrakis, Binhang Yuan
arXiv:2607. 14541v1 Announce Type: new Abstract: Existing GPU kernel generation benchmarks draw problems from synthetic or curated sources that diverge from deployed workloads.
By Lingyun Yang, Yuxiao Wang, Shenghao Liang, Linfeng Yang, Daocheng Ying, Chunbo You, Rui Zhang, Luping Wang, Yinghao Yu, Guodong Yang, Liping Zhang
arXiv:2604. 01489v2 Announce Type: replace Abstract: High-performance GPU kernels are critical to modern machine learning systems, yet developing them remains a manual, expert-driven process.
By Tara Saba, Zhiyang Chen, Jikai Jason Li, Anne Ouyang, Xujie Si, Fan Long