@inproceedings{li-etal-2026-autoveccoder,
    title = "{A}uto{V}ec{C}oder: Teaching {LLM}s to Generate Explicitly Vectorized Code",
    author = "Li, ShangZhan  and
      Yin, Xinyu  and
      Jin, Xuanyu  and
      He, Ye  and
      Zhou, Yuxin  and
      Li, Yuxuan  and
      Han, Xu  and
      Che, Wanxiang  and
      Shi, Qi  and
      Liu, Ting  and
      Sun, Maosong",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1598/",
    pages = "31942--31959",
    ISBN = "979-8-89176-395-1",
    abstract = "Vectorization via Single Instruction, Multiple Data (SIMD) architectures is a cornerstone of high-performance computing. To fully exploit hardware potential, developers often resort to explicit vectorization using intrinsics, as compiler-based auto-vectorization frequently yields suboptimal results due to conservative static analysis. While Large Language Models (LLMs) have demonstrated remarkable proficiency in general code generation, they struggle with explicit vectorization due to the scarcity of high-quality corpora and the strict semantic constraints of low-level hardware instructions. In this paper, we propose AutoVecCoder, a novel framework designed to empower LLMs with the capability of automated explicit vectorization. AutoVecCoder integrates two core components: VecPrompt, an automated data synthesis pipeline to inject domain-specific intrinsic knowledge; and VecRL, a reinforcement learning framework that aligns code generation with execution efficiency. AutoVecCoder-8B trained by this framework achieves state-of-the-art performance on the SSE and AVX subsets of SimdBench and, in some cases, generates implementations surpassing standard optimizations, effectively overcoming the inherent bottlenecks of traditional automated vectorization."
}