{"690019":{"#nid":"690019","#data":{"type":"event","title":"PhD Proposal by Bodhisatwa Chatterjee","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E: Predicting Dynamic Program Behavior with Compiler-Guided Machine Learning\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EDate\u003C\/strong\u003E: Monday, April 27th, 2026\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETime\u003C\/strong\u003E: 1:00 - 3:00 PM EST\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ELocation\u003C\/strong\u003E: Klaus 2108\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EVirtual Link\u003C\/strong\u003E: Zoom (\u003Ca href=\u0022https:\/\/nam12.safelinks.protection.outlook.com\/?url=https%3A%2F%2Fgatech.zoom.us%2Fj%2F92124146699%3Fpwd%3DHZbNuDUQntsJGfjLNaflLph0oQVl3E.1\u0026amp;data=05%7C02%7Ctm186%40gtvault.onmicrosoft.com%7Cd265fca0ab844136646208dea16de20b%7C482198bbae7b4b258b7a6d7f32faa083%7C1%7C0%7C639125690515632063%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C\u0026amp;sdata=zx4POYmFkHu208kt1ZwHcF4GBBrWmj9JCPj%2FxFmFOfk%3D\u0026amp;reserved=0\u0022\u003Ehttps:\/\/gatech.zoom.us\/j\/92124146699?pwd=HZbNuDUQntsJGfjLNaflLph0oQVl3E.1\u003C\/a\u003E)\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBodhisatwa Chatterjee\u003C\/strong\u003E\u003Cbr\u003EPh.D. Student\u003C\/p\u003E\u003Cp\u003ESchool of Computer Science\u003C\/p\u003E\u003Cp\u003ECollege of Computing\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EDr. Santosh Pande (Advisor) \u2013 School of Computer Science, Georgia Institute of Technology\u003C\/p\u003E\u003Cp\u003EDr. Qirun Zhang \u2013 School of Computer Science, Georgia Institute of Technology\u003C\/p\u003E\u003Cp\u003EDr. Vijay Ganesh \u2013 School of Computer Science, Georgia Institute of Technology\u003C\/p\u003E\u003Cp\u003EDr. Willow Ahrens \u2013 School of Computer Science, Georgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EAbstract\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EAccurate 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 \u003Cem\u003Elightweight predictive techniques that leverage runtime values and machine learning models to estimate loop characteristics in a just-in-time manner at loop boundaries\u003C\/em\u003E. Although such predictions do not provide strict guarantees, we demonstrate empirically that they enable a range of \u201csoft\u201d optimizations that yield significant performance improvements.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EIn our first work, we showcase \u003Cstrong\u003ECompiler-Guided Cache Apportioning System (Com-CAS)\u003C\/strong\u003E\u0026nbsp;for effectively apportioning the shared Last-Level Cache (LLC), through the \u003Cem\u003Euse of runtime and compile-time cooperative framework\u003C\/em\u003E. In our second work, we extend this approach to estimate trip count for irregular loops, and present\u003Cstrong\u003E\u0026nbsp;Beacons Framework\u003C\/strong\u003E, an end-to-end system that estimates dynamic loop characteristics and leverages them for proactive workload scheduling.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EIn our third work, we introduce\u003Cstrong\u003E\u0026nbsp;Phaedrus\u003C\/strong\u003E, a framework for profile-driven software optimization via predictive modeling of dynamic application behavior. Phaedrus combines \u003Cem\u003Eapplication profile generalization\u003C\/em\u003E\u0026nbsp;and \u003Cem\u003Eapplication profile synthesis\u003C\/em\u003E, 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.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EFinally, we propose to extend this research along two complementary directions. First, we investigate \u003Cem\u003Eselective instrumentation and fine-grained prediction\u003C\/em\u003E\u0026nbsp;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 \u003Cem\u003Edomain knowledge and large language models in understanding program intent\u003C\/em\u003E\u0026nbsp;and map it to more efficient algorithmic variants tailored to specific inputs.\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EPredicting Dynamic Program Behavior with Compiler-Guided Machine Learning\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Predicting Dynamic Program Behavior with Compiler-Guided Machine Learning"}],"uid":"27707","created_gmt":"2026-04-24 19:16:18","changed_gmt":"2026-04-24 19:16:46","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-04-27T13:00:00-04:00","event_time_end":"2026-04-27T15:00:33-04:00","event_time_end_last":"2026-04-27T15:00:33-04:00","gmt_time_start":"2026-04-27 17:00:00","gmt_time_end":"2026-04-27 19:00:33","gmt_time_end_last":"2026-04-27 19:00:33","rrule":null,"timezone":"America\/New_York"},"location":"Klaus 2108","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"102851","name":"Phd proposal"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}