PrETi: Predicting Execution Time in Early Stage with LLVM and Machine Learning
This provides a robust solution for developers needing early-stage timing analysis, though it is incremental as it builds on existing simulation and ML techniques.
The paper tackles the problem of predicting software execution time early in development by introducing PrETi, a framework that uses LLVM-based simulation and machine learning to achieve an average Absolute Percentage Error of 11.98%, outperforming existing methods.
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.