[2608.13122] Validation-Centric AI-Assisted GPU Porting of a 250,000+ Line Legacy Weather Simulation Code

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Computer Science > Distributed, Parallel, and Cluster Computing

arXiv:2608.13122 (cs)

[Submitted on 13 Aug 2026]

Title:Validation-Centric AI-Assisted GPU Porting of a 250,000+ Line Legacy Weather Simulation Code

Authors:Tetsuya Hoshino, Masaya Kato, Kazuhisa Tsuboki, Daichi Mukunoki, Takahiro Katagiri, Toshihiro Hanawa<br>View a PDF of the paper titled Validation-Centric AI-Assisted GPU Porting of a 250,000+ Line Legacy Weather Simulation Code, by Tetsuya Hoshino and 5 other authors

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Abstract:Recent advances in large language models have made CLI-based AI agents a practical tool for accelerating GPU porting of large legacy scientific applications. Such applications, however, are not merely old code bases; they are scientific assets whose credibility has been accumulated through long-term development, comparison with observations, and use in domain studies. GPU porting must therefore preserve this scientific validity while adapting the implementation to GPU-centric HPC systems. This paper presents a validation-centric AI-assisted GPU porting workflow through a case study of CReSS, a legacy Fortran weather simulation code with more than 250,000 lines. The workflow uses an AI agent to extract OpenMP regions, generate dump-based kernel benchmarks from physically meaningful simulation states, apply OpenACC transformations, and validate results through element-wise comparison with dumped reference data and application-level validation. Using a real typhoon simulation, the workflow produced numerically validated GPU implementations for 162 target kernels and achieved a 5.1x application-level speedup within practical wall-clock development cost. In particular, it detected numerical discrepancies in five kernels caused by floating-point and intrinsic-function differences, including threshold-sensitive branch divergence and cancellation effects, enabling feedback to the application developers. The case study suggests that, for large legacy scientific applications requiring dump-based validation, practical AI-assisted GPU porting must manage session-spanning context, runtime-state reconstruction, and costly recovery from small static-analysis omissions. These findings demonstrate that AI-assisted GPU porting requires not only code generation, but validation-centric workflow design.

Comments:<br>11 pages, 1 figure, 3 tables. Submitted to AgenticAI4HPC 2026

Subjects:

Distributed, Parallel, and Cluster Computing (cs.DC)

Cite as:<br>arXiv:2608.13122 [cs.DC]

(or<br>arXiv:2608.13122v1 [cs.DC] for this version)

https://doi.org/10.48550/arXiv.2608.13122

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Tetsuya Hoshino [view email]<br>[v1]<br>Thu, 13 Aug 2026 11:52:51 UTC (161 KB)

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View a PDF of the paper titled Validation-Centric AI-Assisted GPU Porting of a 250,000+ Line Legacy Weather Simulation Code, by Tetsuya Hoshino and 5 other authors<br>View PDF<br>HTML (experimental)<br>TeX Source

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