Behavioral Embeddings of Programs: A Quasi-Dynamic Approach for Optimization Prediction

• URL: http://arxiv.org/abs/2510.13158v1
• Date: Wed, 15 Oct 2025 05:18:41 GMT
• Title: Behavioral Embeddings of Programs: A Quasi-Dynamic Approach for Optimization Prediction
• Authors: Haolin Pan, Jinyuan Dong, Hongbin Zhang, Hongyu Lin, Mingjie Xing, Yanjun Wu,
• Abstract summary: This paper proposes a novel quasi-dynamic framework for program representation.<n>The core insight is to model a program's optimization sensitivity.<n>To effectively encode this high-dimensional, continuous spectrum, we pioneer a compositional learning approach.
• Score: 35.89884852302035
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning effective numerical representations, or embeddings, of programs is a fundamental prerequisite for applying machine learning to automate and enhance compiler optimization. Prevailing paradigms, however, present a dilemma. Static representations, derived from source code or intermediate representation (IR), are efficient and deterministic but offer limited insight into how a program will behave or evolve under complex code transformations. Conversely, dynamic representations, which rely on runtime profiling, provide profound insights into performance bottlenecks but are often impractical for large-scale tasks due to prohibitive overhead and inherent non-determinism. This paper transcends this trade-off by proposing a novel quasi-dynamic framework for program representation. The core insight is to model a program's optimization sensitivity. We introduce the Program Behavior Spectrum, a new representation generated by probing a program's IR with a diverse set of optimization sequences and quantifying the resulting changes in its static features. To effectively encode this high-dimensional, continuous spectrum, we pioneer a compositional learning approach. Product Quantization is employed to discretize the continuous reaction vectors into structured, compositional sub-words. Subsequently, a multi-task Transformer model, termed PQ-BERT, is pre-trained to learn the deep contextual grammar of these behavioral codes. Comprehensive experiments on two representative compiler optimization tasks -- Best Pass Prediction and -Oz Benefit Prediction -- demonstrate that our method outperforms state-of-the-art static baselines. Our code is publicly available at https://github.com/Panhaolin2001/PREP/.

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