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

2026-08-13

AI Summary

This work addresses the tension between performance gains and scientific validity when porting large legacy scientific codes to GPUs by proposing a verification-centric, AI-assisted migration workflow. The approach integrates a large language model–driven CLI agent, OpenACC-based automated code transformation, and physics-informed kernel benchmark generation, ensuring consistency through both element-wise numerical comparison and application-level meteorological simulations. For the first time, scientific validation is deeply embedded into an AI-assisted porting pipeline, enabling automatic detection of floating-point semantic discrepancies and branch sensitivity, while highlighting the critical roles of conversational context management and runtime state reconstruction. Applied to the 250K-line Fortran weather model CReSS, the method successfully produced verified GPU implementations for 162 core kernels, achieving a 5.1× speedup in real typhoon simulations and uncovering five instances of numerical divergence, substantially reducing migration costs.

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.

Problem

• GPU porting
• scientific validity
• legacy code
• numerical validation
• weather simulation

Innovation

• validation-centric
• AI-assisted GPU porting
• legacy scientific code
• dump-based validation
• numerical correctness