# VecTrans: Enhancing Compiler Auto-Vectorization through LLM-Assisted Code Transformations

arXiv: https://arxiv.org/abs/2503.19449 · HTML: https://mlsanity.com/paper/2503.19449/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 25 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024)
- [GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models](https://mlsanity.com/paper/2410.05229/) (2024)
- [RepairAgent: An Autonomous, LLM-Based Agent for Program Repair](https://mlsanity.com/paper/2403.17134/) (2024)
- [Large Language Models for Compiler Optimization](https://mlsanity.com/paper/2309.07062/) (2023)
- [Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback](https://mlsanity.com/paper/2403.16792/) (2024)
- [NeuroVectorizer: End-to-End Vectorization with Deep Reinforcement Learning](https://mlsanity.com/paper/1909.13639/) (2019)
- [SLaDe: A Portable Small Language Model Decompiler for Optimized Assembly](https://mlsanity.com/paper/2305.12520/) (2023)
- [LLM-Vectorizer: LLM-based Verified Loop Vectorizer](https://mlsanity.com/paper/2406.04693/) (2024)
- [The ARM Scalable Vector Extension](https://mlsanity.com/paper/1803.06185/) (2018)
- [Don't Transform the Code, Code the Transforms: Towards Precise Code Rewriting using LLMs](https://mlsanity.com/paper/2410.08806/) (2024)

## Built on by (most cited first, 4 of 4)

- [Astra: A Multi-Agent System for GPU Kernel Performance Optimization](https://mlsanity.com/paper/2509.07506/) (2025)
- [A Two-Stage GPU Kernel Tuner Combining Semantic Refactoring and Search-Based Optimization](https://mlsanity.com/paper/2601.12698/) (2026)
- [AutoVecCoder: Teaching LLMs to Generate Explicitly Vectorized Code](https://mlsanity.com/paper/2605.17978/) (2026)
- [Can Large Language Models Recover Semantic Optimization Opportunities That Compilers Miss?](https://mlsanity.com/paper/2608.03983/) (2026)

## Abstract (arXiv)

Auto-vectorization is a fundamental optimization for modern compilers to exploit SIMD parallelism. However, state-of-the-art approaches still struggle to handle intricate code patterns, often requiring manual hints or domain-specific expertise. Large language models (LLMs), with their ability to capture intricate patterns, provide a promising solution, yet their effective application in compiler optimizations remains an open challenge due to issues such as hallucinations and a lack of domain-specific reasoning. In this paper, we present VecTrans, a novel framework that leverages LLMs to enhance compiler-based code vectorization. VecTrans first employs compiler analysis to identify potentially vectorizable code regions. It then utilizes an LLM to refactor these regions into patterns that are more amenable to the compilers auto-vectorization. To ensure semantic correctness, VecTrans further integrates a hybrid validation mechanism at the intermediate representation (IR) level. With the above efforts, VecTrans combines the adaptability of LLMs with the precision of compiler vectorization, thereby effectively opening up the vectorization opportunities. experimental results show that among all TSVC functions unvectorizable by GCC, ICC, Clang, and BiSheng Compiler, VecTrans achieves an geomean speedup of 1.77x and successfully vectorizes 24 of 51 test cases. This marks a significant advancement over state-of-the-art approaches while maintaining a cost efficiency of 0.012 per function optimization for LLM API usage.