Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C++
In this study, we present a novel dataset for training machine learning
models translating between OpenMP Fortran and C++ code. To ensure reliability
and applicability, the dataset is created from a range of representative
open-source OpenMP benchmarks. It is also refined using a meticulous code
similarity test. The effectiveness of our dataset is assessed using both
quantitative (CodeBLEU) and qualitative (human evaluation) methods. We showcase
how this dataset significantly elevates the translation competencies of large
language models (LLMs). Specifically, models without prior coding knowledge
experienced a boost of $\mathbf{\times~5.1}$ in their CodeBLEU scores, while
models with some coding familiarity saw an impressive
$\mathbf{\times~9.9}$-fold increase. The best fine-tuned model using our
dataset outperforms GPT-4. It is also reaching human-level accuracy. This work
underscores the immense potential of our dataset in propelling advancements in
the domain of code translation for high-performance computing. The dataset is
accessible at
\href{https://github.com/bin123apple/Fortran-CPP-HPC-code-translation-dataset}{OpenMP-Fortran-CPP-Translation}.