Strengthening the EFIT Solver for Burning Plasmas

EFIT is one of the most extensively used equilibrium reconstruction code in the world. Although robust, EFIT plasma reconstructions will face new challenges in the burning plasma era. These include adapting to new operating regimes and relying on diagnostics that can survive in a harsher, radioactive environment. These stricter plasma control scenarios have motivated exploration of machine learning techniques to improve the quality of real-time equilibrium reconstructions. In order to train these new methods, a database of solutions is required which carefully tracks all constraints and fits performed by EFIT. To support these new developments, we are also improving the core Grad-Shafranov solver. Changes include clearly separating device-specific coding, improving code portability, developing a continuous development pipeline with automatic regression testing, and ensuring thread-safety in preparation for GPU-developments. Using extremely-portable OpenMP and OpenACC directives, we have been able to improve the performance of EFIT using GPU hardware. For the subroutines tested we have observed 40 times speedup across multiple GPU vendors. New options have also been added to assist with creation and analysis of database results. Access to these new techniques is made widely available with Gitlab hosted documentation [https://efit-ai.gitlab.io/efit/] and integration with the OMFIT framework [https://omfit.io] and existing workflows.