AI Compiler Optimization via Reinforcement Learning
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Solution Overview
Problem
Existing compiler optimization techniques are inflexible and inefficient, relying on rule-based methods that do not effectively adapt to optimize computer program compilation for improved execution efficiency and code quality.
Innovation Solution
An AI-assisted compiler that employs a reinforcement learning framework to learn patterns in computer program compilation, generating embedding vectors from intermediate code and execution traces to determine optimization actions, which are then applied to improve the compilation process, allowing for dynamic and platform-agnostic optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If rule-based optimization techniques are used in compilers, then the compilation process is simple and predictable, but the optimization efficiency and code quality are insufficient
Solution Approach 1:
The patent implements feedback mechanisms where the compiler analyzes execution traces and performance metrics from previously compiled code, uses this feedback to learn optimization patterns through machine learning, and applies learned optimizations to subsequent compilation tasks. This creates a closed-loop system that continuously improves compilation efficiency while managing complexity through automated learning.
Solution Approach 2:
The patent replaces traditional rule-based mechanical optimization systems with machine learning-based optimization. Instead of relying on hand-crafted rules and heuristics, the system uses trained models that automatically learn optimization strategies from execution data, thereby improving compilation efficiency while the model handles the complexity internally.
2Reliability
If traditional compiler optimization methods are used, then the compilation process is fast, but the code execution performance and quality are limited
Solution Approach 1:
The patent performs preliminary analysis of execution traces and performance metrics during the compilation process, using this pre-computed information to guide optimization decisions. By preparing optimization strategies in advance based on learned patterns, the system improves code execution performance without excessive compilation time penalty.
Solution Approach 2:
The patent dynamically changes compilation parameters and optimization strategies based on learned patterns from execution traces. The system adjusts optimization levels, instruction selection, and code transformation parameters adaptively, improving execution performance while managing compilation time through intelligent parameter selection rather than exhaustive exploration.
Data Source
AI summary
This application describes methods, systems, and apparatus, including computer programs encoded on computer storage media, of an AI-assisted compiler. An example method includes obtaining intermediate code and executable code generated by compiling a computer program with a compiler; determining a reward based on one or more traces obtained by executing the executable code in a runtime system; generating an embedding vector based on the intermediate code and the one or more traces to represent code execution states; determining, using a reinforcement learning agent, one or more optimization actions based on the embedding vector and the reward; and updating the compiler by applying the one or more optimization actions.