Yebin Chon will present her FPO "LILAC: Enhancing Parallel Code Naturalness through Hybrid Compiler-LLM Translation" on Monday, January 26 , 2026 at 1:30pm in CS 402. Readers: Margaret Martonosi, Zachary Kincaid, David August (adviser) Examiners: Sneha Goenka, Aarti Gupta, David August Title: "LILAC: Enhancing Parallel Code Naturalness through Hybrid Compiler-LLM Translation" All are welcome to attend. Abstract: Modern parallel hardware is becoming increasingly diverse, and there is a following increase in the heterogeneity of parallel programming models (PPMs), often tied to a particular parallel architecture. This brings a need for translation between different programming models in order to properly port and maintain code for various hardware systems. Source-to-source translators may automate this step and provide production compiler-level correctness guarantees. However, previous attempts to build translators that can go between different PPMs often result in unnatural and hard-to-read code, hindering programmer comprehension and long-term maintenance. More recent Large Language Model (LLM) based methods produce highly natural, artful code, but lack the correctness guarantees of traditional compiler-based methods. One work provides a formal proof of correctness for the LLM-generated translation, but is severely limited in the code that it can process. This creates a critical trade-off: developers must choose between the correctness and applicability of compilers and the artfulness of LLMs, both of which are essential. This thesis provides a new way to bring the two methods together, creating cross-PPM translations that are simultaneously correct and natural. To achieve this, it introduces LILAC, a hybrid framework that combines the strengths of traceable and widely applicable compiler transformations and the stylistic capabilities of LLMs. LILAC builds on top of an existing compiler-based translator to provide functionally correct parallel translations, and at the same time detect areas of potential unnaturalness. It then uses an LLM through an API to obtain suggestions for naturalizing the areas identified by the compiler. The compiler then checks these suggestions for correctness and applies the transformations to the code. In this way, LILAC performs several hybrid compiler-LLM passes to naturalize the code - refactoring control flow structures, improving variable names, and inserting comments - without compromising correctness. In a human study with 26 expert C/C++ programmers evaluating CUDA-to-OpenMP translations, LILAC-generated code was judged to be significantly more natural and maintainable than the output of the baseline compiler. The benchmarks used are unable to be translated by the formally verified LLM method. By building on a correct compiler translation, LILAC ensures that enhancements in naturalness do not come at the cost of correctness or applicability.