June 10, 2024, 4:46 a.m. | Jubi Taneja, Avery Laird, Cong Yan, Madan Musuvathi, Shuvendu K. Lahiri

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.04693v1 Announce Type: cross
Abstract: Vectorization is a powerful optimization technique that significantly boosts the performance of high performance computing applications operating on large data arrays. Despite decades of research on auto-vectorization, compilers frequently miss opportunities to vectorize code. On the other hand, writing vectorized code manually using compiler intrinsics is still a complex, error-prone task that demands deep knowledge of specific architecture and compilers.
In this paper, we evaluate the potential of large-language models (LLMs) to generate vectorized (Single …

abstract applications arrays arxiv auto code compiler compilers computing cs.ai cs.lg cs.pf cs.se data error high performance computing llm loop opportunities optimization performance research type vectorization vectorize writing

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