🚀 LLVM + Reinforcement Learning for Loop Vectorization

🌟 Overview

Modern compilers like LLVM rely on fixed heuristics to optimize code, including loop vectorization. While these heuristics work in many cases, they often miss optimal opportunities for diverse program structures.

This project demonstrates how Reinforcement Learning (RL) can dynamically guide loop vectorization, enabling adaptive, data-driven compiler optimizations.
The system integrates a custom LLVM pass with a Python-based RL environment, where the agent learns to make decisions based on loop features and runtime feedback.

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✨ Features

• 📝 LLVM Pass for Loop Analysis: Extracts key loop features like depth, instruction count, and memory operations.
• 🤖 RL Environment: Gym-style Python environment modeling loop optimization as a reinforcement learning problem.
• 🧠 PPO-based Agent: Learns policies to vectorize loops efficiently using Stable Baselines3.
• 🔄 End-to-End Pipeline: From source code → LLVM IR → RL decisions → optimized executable → performance feedback.

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🛠 Pipeline

+------------------+
|  C / C++ Source  |
+------------------+
          │
          ▼
+------------------+
|   LLVM IR (.ll)  |
|  clang -emit-llvm|
+------------------+
          │
          ▼
+---------------------------+
|   LoopRLOpt LLVM Pass     |
|  (feature extraction &    |
|   action application)     |
+---------------------------+
          │
          ▼
+---------------------------+
| Python RL Environment     |
|  (Gym, loads features)    |
+---------------------------+
          │
          ▼
+---------------------------+
|      RL Agent             |
|  (PPO decides vectorize) |
+---------------------------+
          │
          ▼
+---------------------------+
| Optimized IR (.ll)        |
| Compile → Executable      |
+---------------------------+
          │
          ▼
+---------------------------+
| Run & Measure Performance |
|  (feedback → reward)     |
+---------------------------+
          │
          ▼
+---------------------------+
|  RL Agent updates policy  |
+---------------------------+

• The RL agent closes the loop, learning continuously from execution feedback.

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⚡ Installation

1. Clone the repository
   git clone <repository_url>
cd llvm-rl-vectorize-project

2. Build LLVM Pass
   mkdir build && cd build
cmake -DLLVM_DIR=/path/to/llvm/lib/cmake/llvm ..
make

3. Install Python dependencies
   pip install -r requirements.txt

✅ requirements.txt includes:
gym==0.26.3
stable-baselines3==2.2.0
torch==2.1.0
torchvision==0.16.1
numpy==1.26.2
pandas==2.1.0
matplotlib==3.8.0
seaborn==0.12.3
pyyaml==6.1
tqdm==4.68.0

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🚀 Usage

1. Generate LLVM IR from your benchmark
   clang -O0 -emit-llvm -S ../benchmark.c -o benchmark.ll

2. Run the custom LLVM pass
   opt -load-pass-plugin=./LoopRLOpt.so -passes=loop-rl-opt benchmark.ll -S -o out.ll

3. Train RL Agent
   python train.py

4. Compile optimized IR and run
   clang out.ll -o a.out
./a.out

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📊 Results

• The RL agent learns adaptive loop vectorization policies.
• Benchmarks show improved runtime compared to LLVM default heuristics by avoiding unnecessary vectorization.
• Demonstrates the feasibility of integrating machine learning into compiler optimization pipelines.

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🔮 Future Work

• Extend RL guidance to other compiler passes (e.g., inlining, unrolling).
• Incorporate advanced RL techniques (A2C, AlphaZero-style search).
• Test on large-scale, real-world workloads to improve generalization.

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📫 Contact

• Author: Aditya Kumar
• GitHub: https://github.com/CyberGenius01
• Email: aditya.kumar@example.com