The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Poster Type: Research Posters
Author: Ren Imai (Tohoku University), Masatoshi Kawai (Tohoku University), Keichi Takahashi (The University of Osaka), Hiroyuki Takizawa (Tohoku University)
Supervisor:
Abstract: In this study, we discuss the practicality and limitations of using current large language models (LLMs) for automatically translating Fortran legacy codes to C++, so that legacy codes written in Fortran can be modernized to exploit the performance and features available only in C++. Moreover, we investigate the effectiveness of in-context learning (ICL: translation using custom prompts) and interactive translation (IT: re-translating the code when a compile error occurs) at automatic Fortran-to-C++ translation. In our evaluation, the rate of producing the same results as the original code, called the output match rate, is used as the primary evaluation metric. The evaluation results not only demonstrate that it is difficult even for the latest LLM to achieve 100% accurate translation at present, but also that ICL and IT are effective to improve the accuracy.
Best Poster Finalist (BP): no
Poster: PDF
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