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

Bin Lei, Caiwen Ding, Le Chen, Pei Hung Lin, Chunhua Liao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

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 x 5.1 in their CodeBLEU scores, while models with some coding familiarity saw an impressive x 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 https://github.com/bin123apple/Fortran-CPP-HPC-code-translation-dataset.

Original languageEnglish (US)
Title of host publication2023 IEEE High Performance Extreme Computing Conference, HPEC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350308600
DOIs
StatePublished - 2023
Externally publishedYes
Event2023 IEEE High Performance Extreme Computing Conference, HPEC 2023 - Virtual, Online, United States
Duration: Sep 25 2023Sep 29 2023

Publication series

Name2023 IEEE High Performance Extreme Computing Conference, HPEC 2023

Conference

Conference2023 IEEE High Performance Extreme Computing Conference, HPEC 2023
Country/TerritoryUnited States
CityVirtual, Online
Period9/25/239/29/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • C++
  • Code Translation
  • Fortran
  • Large Language Model
  • OpenMP

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