# NeuroVectorizer: End-to-End Vectorization with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1909.13639 · HTML: https://mlsanity.com/paper/1909.13639/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2019.

## Built on by (most cited first, 30 of 37)

- [Large Language Models for Compiler Optimization](https://mlsanity.com/paper/2309.07062/) (2023)
- [CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research](https://mlsanity.com/paper/2109.08267/) (2021)
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- [LLM-Vectorizer: LLM-based Verified Loop Vectorizer](https://mlsanity.com/paper/2406.04693/) (2024)
- [Compiler Optimization for Quantum Computing Using Reinforcement Learning](https://mlsanity.com/paper/2212.04508/) (2022)
- [Learning Compiler Pass Orders using Coreset and Normalized Value Prediction](https://mlsanity.com/paper/2301.05104/) (2023)
- [Improving Parallel Program Performance with LLM Optimizers via Agent-System Interfaces](https://mlsanity.com/paper/2410.15625/) (2024)
- [MLGOPerf: An ML Guided Inliner to Optimize Performance](https://mlsanity.com/paper/2207.08389/) (2022)
- [Compiler generated feedback for Large Language Models](https://mlsanity.com/paper/2403.14714/) (2024)
- [Performance Optimization using Multimodal Modeling and Heterogeneous GNN](https://mlsanity.com/paper/2304.12568/) (2023)
- [CompilerDream: Learning a Compiler World Model for General Code Optimization](https://mlsanity.com/paper/2404.16077/) (2024)
- [A Reinforcement Learning Environment for Polyhedral Optimizations](https://mlsanity.com/paper/2104.13732/) (2021)
- [Customized Monte Carlo Tree Search for LLVM/Polly's Composable Loop Optimization Transformations](https://mlsanity.com/paper/2105.04555/) (2021)
- [A Highly Configurable Hardware/Software Stack for DNN Inference Acceleration](https://mlsanity.com/paper/2111.15024/) (2021)
- [Learning to Combine Instructions in LLVM Compiler](https://mlsanity.com/paper/2202.12379/) (2022)
- [RL4ReAl: Reinforcement Learning for Register Allocation](https://mlsanity.com/paper/2204.02013/) (2022)
- [VecTrans: Enhancing Compiler Auto-Vectorization through LLM-Assisted Code Transformations](https://mlsanity.com/paper/2503.19449/) (2025)
- [Generating GPU Compiler Heuristics using Reinforcement Learning](https://mlsanity.com/paper/2111.12055/) (2021)
- [Profile Guided Optimization without Profiles: A Machine Learning Approach](https://mlsanity.com/paper/2112.14679/) (2021)
- [POSET-RL: Phase ordering for Optimizing Size and Execution Time using Reinforcement Learning](https://mlsanity.com/paper/2208.04238/) (2022)
- [Power Constrained Autotuning using Graph Neural Networks](https://mlsanity.com/paper/2302.11467/) (2023)
- [MIREncoder: Multi-modal IR-based Pretrained Embeddings for Performance Optimizations](https://mlsanity.com/paper/2407.02238/) (2024)
- [VibeCodeHPC: An Agent-Based Iterative Prompting Auto-Tuner for HPC Code Generation Using LLMs](https://mlsanity.com/paper/2510.00031/) (2025)
- [End-to-end Mapping in Heterogeneous Systems Using Graph Representation Learning](https://mlsanity.com/paper/2204.11981/) (2022)
- [Target-independent XLA optimization using Reinforcement Learning](https://mlsanity.com/paper/2308.14364/) (2023)
- [LoopTune: Optimizing Tensor Computations with Reinforcement Learning](https://mlsanity.com/paper/2309.01825/) (2023)
- [YFlows: Systematic Dataflow Exploration and Code Generation for Efficient Neural Network Inference using SIMD Architectures on CPUs](https://mlsanity.com/paper/2310.00574/) (2023)
- [CHEHAB RL: Learning to Optimize Fully Homomorphic Encryption Computations](https://mlsanity.com/paper/2601.19367/) (2026)
- [Protean Compiler: An Agile Framework to Drive Fine-grain Phase Ordering](https://mlsanity.com/paper/2602.06142/) (2026)
- [Sonic: A Sampling-based Online Controller for Streaming Applications](https://mlsanity.com/paper/2108.10701/) (2021)

## Abstract (arXiv)

One of the key challenges arising when compilers vectorize loops for today's SIMD-compatible architectures is to decide if vectorization or interleaving is beneficial. Then, the compiler has to determine how many instructions to pack together and how many loop iterations to interleave. Compilers are designed today to use fixed-cost models that are based on heuristics to make vectorization decisions on loops. However, these models are unable to capture the data dependency, the computation graph, or the organization of instructions. Alternatively, software engineers often hand-write the vectorization factors of every loop. This, however, places a huge burden on them, since it requires prior experience and significantly increases the development time. In this work, we explore a novel approach for handling loop vectorization and propose an end-to-end solution using deep reinforcement learning (RL). We conjecture that deep RL can capture different instructions, dependencies, and data structures to enable learning a sophisticated model that can better predict the actual performance cost and determine the optimal vectorization factors. We develop an end-to-end framework, from code to vectorization, that integrates deep RL in the LLVM compiler. Our proposed framework takes benchmark codes as input and extracts the loop codes. These loop codes are then fed to a loop embedding generator that learns an embedding for these loops. Finally, the learned embeddings are used as input to a Deep RL agent, which determines the vectorization factors for all the loops. We further extend our framework to support multiple supervised learning methods. We evaluate our approaches against the currently used LLVM vectorizer and loop polyhedral optimization techniques. Our experiments show 1.29X-4.73X performance speedup compared to baseline and only 3% worse than the brute-force search on a wide range of benchmarks.