# Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C++

arXiv: https://arxiv.org/abs/2307.07686 · HTML: https://mlsanity.com/paper/2307.07686/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 15 Jul 2023.

## Builds on (most cited first, 6 of 6)

- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [StarCoder: may the source be with you!](https://mlsanity.com/paper/2305.06161/) (2023)
- [CodeBLEU: a Method for Automatic Evaluation of Code Synthesis](https://mlsanity.com/paper/2009.10297/) (2020)
- [How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation](https://mlsanity.com/paper/2302.09210/) (2023)
- [Unsupervised Translation of Programming Languages](https://mlsanity.com/paper/2006.03511/) (2020)
- [CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks](https://mlsanity.com/paper/2105.12655/) (2021)

## Built on by (most cited first, 11 of 11)

- [CodeRosetta: Pushing the Boundaries of Unsupervised Code Translation for Parallel Programming](https://mlsanity.com/paper/2410.20527/) (2024)
- [Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generation](https://mlsanity.com/paper/2407.19619/) (2024)
- [LASSI: An LLM-based Automated Self-Correcting Pipeline for Translating Parallel Scientific Codes](https://mlsanity.com/paper/2407.01638/) (2024)
- [LLM & HPC:Benchmarking DeepSeek's Performance in High-Performance Computing Tasks](https://mlsanity.com/paper/2504.03665/) (2025)
- [LLM-Assisted Translation of Legacy FORTRAN Codes to C++: A Cross-Platform Study](https://mlsanity.com/paper/2504.15424/) (2025)
- [Enhancing Cross-Language Code Translation via Task-Specific Embedding Alignment in Retrieval-Augmented Generation](https://mlsanity.com/paper/2412.05159/) (2024)
- [Can Large Language Models Predict Parallel Code Performance?](https://mlsanity.com/paper/2505.03988/) (2025)
- [Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translation](https://mlsanity.com/paper/2512.03086/) (2025)
- [Beyond BLEU: A Semantic Evaluation Method for Code Translation](https://mlsanity.com/paper/2605.05282/) (2026)
- [OMPILOT: Harnessing Transformer Models for Auto Parallelization to Shared Memory Computing Paradigms](https://mlsanity.com/paper/2511.03866/) (2025)
- [AstraAI: LLMs, Retrieval, and AST-Guided Assistance for HPC Codebases](https://mlsanity.com/paper/2603.27423/) (2026)

## Abstract (arXiv)

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 ×~5.1 in their CodeBLEU scores, while models with some coding familiarity saw an impressive ×~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 hrefhttps://github.com/bin123apple/Fortran-CPP-HPC-code-translation-datasetOpenMP-Fortran-CPP-Translation.