Title: Predicting Dynamic Program Behavior with Compiler-Guided Machine Learning

Date: Monday, April 27th, 2026

Time: 1:00 - 3:00 PM EST

Location: Klaus 2108

Virtual Link: Zoom (https://gatech.zoom.us/j/92124146699?pwd=HZbNuDUQntsJGfjLNaflLph0oQVl3E.1)

Bodhisatwa Chatterjee
Ph.D. Student

School of Computer Science

College of Computing

Georgia Institute of Technology

Committee:

Dr. Santosh Pande (Advisor) – School of Computer Science, Georgia Institute of Technology

Dr. Qirun Zhang – School of Computer Science, Georgia Institute of Technology

Dr. Vijay Ganesh – School of Computer Science, Georgia Institute of Technology

Dr. Willow Ahrens – School of Computer Science, Georgia Institute of Technology

Abstract

Accurate knowledge of program execution behavior is critical for both compiler optimizations and system-level resource management. However, modern workloads exhibit complex, input-dependent behavior with irregular control flow and dynamic phase changes, making static reasoning inadequate. While dynamic optimization techniques offer an alternative, their high analysis overhead often negates potential performance gains. In this work, we propose lightweight predictive techniques that leverage runtime values and machine learning models to estimate loop characteristics in a just-in-time manner at loop boundaries. Although such predictions do not provide strict guarantees, we demonstrate empirically that they enable a range of “soft” optimizations that yield significant performance improvements.

In our first work, we showcase Compiler-Guided Cache Apportioning System (Com-CAS) for effectively apportioning the shared Last-Level Cache (LLC), through the use of runtime and compile-time cooperative framework. In our second work, we extend this approach to estimate trip count for irregular loops, and present Beacons Framework, an end-to-end system that estimates dynamic loop characteristics and leverages them for proactive workload scheduling.

In our third work, we introduce Phaedrus, a framework for profile-driven software optimization via predictive modeling of dynamic application behavior. Phaedrus combines application profile generalization and application profile synthesis, integrating generative deep learning models and LLMs with static compiler analysis to predict function calls and control-flow behavior. This enables profile-guided optimization without a priori profiling, and demonstrates performance gains over traditional offline profiling approaches.

Finally, we propose to extend this research along two complementary directions. First, we investigate selective instrumentation and fine-grained prediction for software optimization, where loops are categorized based on their dynamic trip count characteristics, and predictive models are selectively developed only for computationally intensive regions to enable superior profile-guided optimizations while minimizing overhead. Secondly, we plan to leverage domain knowledge and large language models in understanding program intent and map it to more efficient algorithmic variants tailored to specific inputs.