  In advancing parallel programming, particularly with OpenMP, the shift
towards NLP-based methods marks a significant innovation beyond traditional S2S
tools like Autopar and Cetus. These NLP approaches train on extensive datasets
of examples to efficiently generate optimized parallel code, streamlining the
development process. This method's strength lies in its ability to swiftly
produce parallelized code that runs efficiently. However, this reliance on NLP
models, without direct code analysis, can introduce inaccuracies, as these
models might not fully grasp the nuanced semantics of the code they
parallelize. We build OMP-Engineer, which balances the efficiency and
scalability of NLP models with the accuracy and reliability of traditional
methods, aiming to enhance the performance of automating parallelization while
navigating its inherent challenges.
