Increasing the multiscale/multiphysics capability of CAM-SE using implicit time integration and GPU accelerators

Rick Archibald, Oak Ridge National Laboratory, Oak Ridge, TN, United States, Katherine J Evans, Oak Ridge National Laboratory, Oak Ridge, United States, Patrick Worley, PHWorley Consulting, Oak Ridge, TN, United States, Matthew R Norman, Oak Ridge National Lab, Oak Ridge, TN, United States, Aaron Lott, Universities Space Research Association, Moffet Field, United States, Andrew Salinger, Sandia National Laboratory, Albuquerque, NM, United States and Carol S Woodward, Lawrence Livermore National Laboratory, Livermore, CA, United States
Abstract:
The recent focus on regional refinement in the Community Atmosphere Model (CAM5) has created a strong need to develop time-stepping methods capable of accelerating throughput on high performance computing for climate dynamics across multiple spatial and temporal scales. This research is focused on developing implicit methods that can be executed at scale on GPU based machines. Efforts to port the scalable spectral element dynamical core to incorporate these developments is presented, including both 2D and 3D benchmark test case results. The current implicit solver and preconditioner implementations utilize a Fortran interface package within the Trilinos project, third party software that allows fully tested, optimized, and robust code with a suite of parameter options to be included a priori. Merging this coding strategy with GPU libraries will be discussed along with beneficial optimization gains and data structure requirements to evaluate Trilinos binded residual calculations on GPU processors.
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