Creating a Dataset for High-Performance Computing Code Translation: A Bridge Between HPC 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 initially refined using a meticulous code similarity test. The effectiveness of our dataset is assessed using both quantitative (CodeBLEU) and qualitative (human evaluation) methods. We demonstrate how this dataset can significantly improve the translation capabilities of large-scale language models, with improvements of $\mathbf{\times 5.1}$ for models with no prior coding knowledge and $\mathbf{\times 9.9}$ for models with some coding familiarity. Our work highlights the potential of this dataset to advance the field of code translation for high-performance computing. The dataset is available at https://github.com/bin123apple/Fortran-CPP-HPC-code-translation-dataset