Experience with adapting to a software framework for a use-case in computational science

Abstract

The effective use of HPC infrastructure critically depends on the human resources involved in the maintenance and operation of these systems alongside the domain scientists and scientific programmers who develop scientific applications to leverage these systems. The workforce typically consists of undergraduates/postgraduates in different fields with broad areas of training in scientific computing and some programming skills with aptitude in HPC. However, there is a gap in the university-level curriculum and the skill set required to adapt to the requirements for developing scientific applications. Some efforts are there to fill this gap through workforce training programs to prepare the graduates for HPC jobs in industry/national labs. In this work, we share our experience training the workforce to adapt to AMReX (https://amrex-codes.github.io/amrex/docs_html/), a software framework developed under the Exascale computing project for scientific application development. It requires recapitulation of partial differential equations (PDEs), an indispensable mathematical model for describing physical systems across different scientific domains. We discuss our engagement with the intern, the trainees, and the development team in orienting them to scientific computing on the HPC platform, PDE solvers in particular. We highlight some of the features of the AMReX framework that helped the development team to contribute AMReX-based phase field solvers in the MicroSim phase field solver suite as a case study in adapting to the framework. These solvers can target different architectures without modifications due to the abstraction layer that provides immunity to developers for programming on different architectures. This experience can help to evolve a training model to build the HPC workforce.