Performance portability of a fluidized bed solver

Performance portability is a challenge for application developers as the source code needs to be executed and performant on various hybrid computing architectures. The linear iterative solvers implemented in most applications consume more than 70% of the runtime. This paper presents the results of a linear solver in Trilinos for fluidized bed applications. The linear solver implemented in our code is based on the Kokkos programming model in Trilinos, which uses a library approach to provide performance portability across diverse devices with different memory models. For large scale problems, the numerical experiments on Xeon Phi and Kepler GPU architectures show good performance over the results on Xeon (Haswell) computing architectures.