Evaluating Claude Code’s Coding and Test Automation for GPU Acceleration ofa Legacy Fortran Application: A GeoFEM Case Study

With the rise of AI research, supercomputers equipped with GPUs by default have become commonplace, and the use of GPUs in simulation has become even more important. At the same time, porting legacy codes developed for CPUs to GPUs remains a major challenge. A promising way to address this issue is to leverage rapidly advancing code-generation AI; however, because this approach is very new, it has not been sufficiently evaluated. In this study, we attempt GPU-oriented code generation using Claude Code—one such code-generation AI—for GeoFEM, a Fortran-based finite element application parallelized with MPI+OpenMP. We systematically investigate methods to achieve both the generation of high-performance GPU code and the streamlining of the code-generation process itself, including automated test execution to assess correctness and performance of the generated GPU code.