PrETi: Predicting Execution Time in Early Stage with LLVM and Machine Learning

We propose preti, a novel framework for predicting software execution time in the early stages of development. \texttt{preti} combines static analysis and simulation to extract timing-relevant features from LLVM intermediate representation (IR), which are then used to train machine learning models. Central to our approach is a control-flow graph (CFG) alignment algorithm that establishes a structural correspondence between IR basic blocks and their associated assembly instructions. To enhance the fidelity of timing predictions, \texttt{preti} simulates microarchitectural behaviors—including cache accesses, branch prediction, and register spills—thereby generating execution traces that approximate real-world behavior. This design enables accurate execution time prediction without requiring native execution on embedded hardware, making \texttt{preti} particularly suited for early-stage performance analysis.