Accelerating Time Integration for Climate Modeling Using GPUs

Abstract

The push towards larger and larger computational platforms has made it possible for climate simulations to resolve climate dynamics across multiple spatial and temporal scales. This direction in climate simulation has created a strong need to develop scalable timestepping methods capable of accelerating throughput on high performance computing. This work details the recent advances in the implementation of implicit time stepping of the spectral element dynamical core within the United States Department of Energy (DOE) Accelerated Climate Model for Energy (ACME) on graphical processing units (GPU) based machines. We demonstrate how solvers in the Trilinos project are interfaced with ACME and GPU kernels to increase computational speed of the residual calculations in the implicit time stepping method for the atmosphere dynamics. We show the optimization gains and data structure reorganization that facilitates the performance improvements.