  High resolution simulations of polar ice-sheets play a crucial role in the
ongoing effort to develop more accurate and reliable Earth-system models for
probabilistic sea-level projections. These simulations often require a massive
amount of memory and computation from large supercomputing clusters to provide
sufficient accuracy and resolution. The latest exascale machines poised to come
online contain a diverse set of computing architectures. In an effort to avoid
architecture specific programming and maintain productivity across platforms,
the ice-sheet modeling code known as MALI uses high level abstractions to
integrate Trilinos libraries and the Kokkos programming model for performance
portable code across a variety of different architectures. In this paper, we
analyze the performance portable features of MALI via a performance analysis on
current CPU-based and GPU-based supercomputers. The analysis highlights
performance portable improvements made in finite element assembly and multigrid
preconditioning within MALI with speedups between 1.26-1.82x across CPU and GPU
architectures but also identifies the need to further improve performance in
software coupling and preconditioning on GPUs. We also perform a weak
scalability study and show that simulations on GPU-based machines perform
1.24-1.92x faster when utilizing the GPUs. The best performance is found in
finite element assembly which achieved a speedup of up to 8.65x and a weak
scaling efficiency of 82.9% with GPUs. We additionally describe an automated
performance testing framework developed for this code base using a changepoint
detection method. The framework is used to make actionable decisions about
performance within MALI. We provide several concrete examples of scenarios in
which the framework has identified performance regressions, improvements, and
algorithm differences over the course of two years of development.
