We introduce preti, a novel framework for predicting software execution time during the early stages of development. preti leverages an LLVM-based simulation environment to extract timing-related runtime information, such as the count of executed LLVM IR instructions. This information, combined with historical execution time data, is utilized to train machine learning models for accurate time prediction. To further enhance prediction accuracy, our approach incorporates simulations of cache accesses and branch prediction. The evaluations on public benchmarks demonstrate that preti achieves an average Absolute Percentage Error (APE) of 11.98\%, surpassing state-of-the-art methods. These results underscore the effectiveness and efficiency of preti as a robust solution for early-stage timing analysis.
View on arXiv@article{xu2025_2503.13679,
title={ PrETi: Predicting Execution Time in Early Stage with LLVM and Machine Learning },
author={ Risheng Xu and Philipp Sieweck and Hermann von Hasseln and Dirk Nowotka },
journal={arXiv preprint arXiv:2503.13679},
year={ 2025 }
}