LLM-Vectorizer: LLM-based Verified Loop Vectorizer
<|reference_start|>LLM-Vectorizer: LLM-based Verified Loop Vectorizer: 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 Instruction Multiple Data) code from scalar programs that process individual array elements. We propose a novel finite-state machine multi-agents based approach that harnesses LLMs and test-based feedback to generate vectorized code. Our findings indicate that LLMs are capable of producing high performance vectorized code with run-time speedup ranging from 1.1x to 9.4x as compared to the state-of-the-art compilers such as Intel Compiler, GCC, and Clang.   To verify the correctness of vectorized code, we use Alive2, a leading bounded translation validation tool for LLVM IR. We describe a few domain-specific techniques to improve the scalability of Alive2 on our benchmark dataset. Overall, our approach is able to verify 38.2% of vectorizations as correct on the TSVC benchmark dataset.<|reference_end|>
@article{taneja2024llm-vectorizer:,
    title={LLM-Vectorizer: LLM-based Verified Loop Vectorizer},
    author={Jubi Taneja, Avery Laird, Cong Yan, Madan Musuvathi, Shuvendu K.
  Lahiri},
    journal={arXiv preprint arXiv:2406.04693},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04693},
    primaryClass={cs.SE cs.AI cs.LG cs.PF}
}