Interest in applying Artificial Intelligence (AI) techniques to compiler optimizations is
increasing rapidly, but compiler research has a high entry barrier. Unlike in other domains …

Code is increasingly becoming a core data modality of modern machine learning research
impacting not only the way we write code with conversational agents like OpenAI's ChatGPT …

There is a large space of NUMA and hardware prefetcher configurations that can
significantly impact the performance of an application. Previous studies have demonstrated …

Code size is critical for resource-constrained devices, where memory and storage are
limited. Compilers, therefore, should offer optimizations aimed at code reduction. One such …

Decompilation is a well-studied area with numerous high-quality tools available. These are
frequently used for security tasks and to port legacy code. However, they regularly generate …

We introduce a novel paradigm in compiler optimization powered by Large Language
Models with compiler feedback to optimize the code size of LLVM assembly. The model …

Finding the optimal pass sequence of compilation can lead to a significant reduction in
program size. Prior works on compilation pass ordering have two major drawbacks. They …

In the last five years, predictive compilation has advanced with long strides. Contributions in
the field include new program embeddings, new learning architectures, and datasets with …

Deep learning has had a significant impact on many fields. Recently, code-to-code neural
models have been used in code translation, code refinement and decompilation. However …

Effective code optimization in compilers plays a central role in computer and software
engineering. While compilers can be made to automatically search the optimization space …