Phaedrus: Predicting Dynamic Application Behavior with Lightweight Generative Models and LLMs

Application profiling is essential for software optimization tasks such as code layout and memory placement, where optimization decisions depend on program behavior. However, modern applications exhibit significant input-dependent variability, limiting the effectiveness of conventional profiling approaches that rely on a single representative execution. We present Phaedrus, a compiler-assisted deep learning framework that predicts dynamic program behavior across diverse execution instances, with a focus on dynamic function call prediction. These predicted call sequences are used to guide input-specific compiler optimizations, enabling code specialization without requiring program execution. Phaedrus introduces two complementary techniques. Application Behavior Synthesis (Dynamis) is a profile-less approach in which large language models infer dynamic behavior directly from source code and static compiler analysis, bypassing traditional profiling. Application Profile Generalization (Morpheus) employs generative models trained on compressed and augmented Whole Program Path (WPP) function profiles to predict application behavior for unseen inputs. Experimental results show that Phaedrus accurately identifies frequently executed and runtime-dominated hotspot functions, covering up to 85-99% of total execution time. Using these predictions, Phaedrus enables superior profile-guided optimizations, achieving an average performance improvement of 6% (upto 25%) and a binary size reduction of 5.19% (upto 19%), without executing the target program. Additionally, Phaedrus reduces WPP function profile sizes by up to $10^{7} \times $.