← Back to all news
arXiv AI By Genghan Zhang, Yixin Dong, Chengze Fan, Zhichen Zeng, Yueming Yuan, Shaowei Zhu, Kunle Olukotun

PTXBench: Benchmark and Adapt LLMs for GPU Kernel Optimization with Architecture-specific PTX

Read the original on arXiv AI →

PTXBench is a benchmark designed to evaluate and adapt large language models (LLMs) for GPU kernel optimization using architecture‑specific PTX code. It assesses functional correctness, runtime execution of target instructions, and speedup over state‑of‑the‑art libraries on GEMM and attention workloads on H100 and B200 GPUs. The study finds uneven success rates, especially on complex attention backward tasks, and shows that executing target instructions does not guarantee competitive performance, with no model consistently outperforming frontier libraries. The authors also fine‑tune Qwen3.6‑27B, noting that repair‑conditioned training improves some tasks but generalization remains inconsistent, highlighting the importance of data coverage, balance, and teacher quality.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

Hugging Face Trending Papers
Aug 18

PTXBench: Benchmark and Adapt LLMs for GPU Kernel Optimization with Architecture-specific PTX

PTXBench is a benchmark designed to evaluate and adapt large language models (LLMs) for GPU kernel optimization using architecture-specific PTX code. It assesses functional correctness, runtime execution of target instructions, and speedup over leading libraries on GEMM and attention workloads on H100 and B200 GPUs. The study finds uneven performance across models, especially on complex attention backward tasks, and shows that fine‑tuning Qwen3.6‑27B improves some tasks but generalization remains inconsistent.

llmsfine-tuningbenchmarks
More like this →
arXiv Machine Learning
Aug 27

Ladder Up, Memory Down: Low-Cost Fine-Tuning With Side Nets

The paper introduces Ladder Side Tuning (LST), a parameter‑efficient fine‑tuning method that adds a lightweight side network to large language models. LST matches QLoRA’s compute scaling while halving peak memory usage, enabling 7B‑parameter models to be fine‑tuned on a single 12 GB GPU with 2k‑token contexts without gradient checkpointing. The authors also present xLadder, a depth‑extended variant that increases effective depth through cross‑connections, allowing deeper reasoning without extra memory overhead.

By Estelle Zheng, Nathan Cerisara, S\'ebastien Warichet, Emmanuel Helbert, Christophe Cerisara
llmsfine-tuningbenchmarks
More like this →
arXiv AI
Sep 16

Ave: Guiding Agentic GPU Optimization Using Data-Flow Invariants

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
llmsragagentsreinforcement-learningefficiencybenchmarkssafety
More like this →