Automated In Situ Computational Steering Using Ascent’s Capable Yes-No Machine

ABSTRACT The life of a multi-physics code user is complicated. Simulation crashes, efficient resource utilization, and simulation parameter choices are time consuming workflow issues that increase a user’s iteration time. Simulations often don’t provide general tools to support automatically adapting workflows to the diverse set of problems that multi-physics codes are capable of simulating. In situ visualization and analysis infrastructures are designed to be general. They are repositories of shared capability that support multiple simulation codes. Normally, the connection between simulation and in situ analysis is unidirectional (e.g., render an image or querying the mesh). In this work, we close the loop between an in situ infrastructure and a simulation, and we explore opportunities to leverage in situ triggers for automatic computational steering at runtime. We demonstrate using Ascent’s in situ trigger interface as a capable yes-no machine for controlling simulation choices.