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CompilerDream: Learning a Compiler World Model for General Code Optimization
- CompilerDream is a model-based reinforcement learning approach designed for general code optimization in compilers.
- Optimization in compilers is crucial for computer and software engineering success.
- The effectiveness of optimizations relies on the selection and ordering of optimization passes applied to the code.
- Current methods for finding the optimal sequence of optimization passes are either slow or struggle to generalize to unseen code.
- CompilerDream introduces a compiler world model that simulates optimization passes and an agent trained on this model for generating effective optimization strategies.
- By training on a large-scale program dataset, CompilerDream can serve as a general code optimizer for various application scenarios and source-code languages.
- CompilerDream showcases strong optimization capabilities for autotuning and outperforms LLVM's built-in optimizations, leading the CompilerGym leaderboard.
- The model's ability to generalize across diverse datasets without prior training surpasses state-of-the-art methods in both value prediction and end-to-end code optimization.
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