  Support for lower precision computation is becoming more common in
accelerator hardware due to lower power usage, reduced data movement and
increased computational performance. However, computational science and
engineering (CSE) problems require double precision accuracy in several
domains. This conflict between hardware trends and application needs has
resulted in a need for multiprecision strategies at the linear algebra
algorithms level if we want to exploit the hardware to its full potential while
meeting the accuracy requirements. In this paper, we focus on preconditioned
sparse iterative linear solvers, a key kernel in several CSE applications. We
present a study of multiprecision strategies for accelerating this kernel on
GPUs. We seek the best methods for incorporating multiple precisions into the
GMRES linear solver; these include iterative refinement and parallelizable
preconditioners. Our work presents strategies to determine when multiprecision
GMRES will be effective and to choose parameters for a multiprecision iterative
refinement solver to achieve better performance. We use an implementation that
is based on the Trilinos library and employs Kokkos Kernels for performance
portability of linear algebra kernels. Performance results demonstrate the
promise of multiprecision approaches and demonstrate even further improvements
are possible by optimizing low-level kernels.
