  Hardware-aware design and optimization is crucial in exploiting emerging
architectures for PDE-based computational fluid dynamics applications. In this
work, we study optimizations aimed at acceleration of OpenFOAM-based
applications on emerging hybrid heterogeneous platforms. OpenFOAM uses MPI to
provide parallel multi-processor functionality, which scales well on
homogeneous systems but does not fully utilize the potential per-node
performance on hybrid heterogeneous platforms. In our study, we use two
OpenFOAM applications, icoFoam and laplacianFoam, both based on Krylov
iterative methods. We propose a number of optimizations of the dominant kernel
of the Krylov solver, aimed at acceleration of the overall execution of the
applications on modern GPU-accelerated heterogeneous platforms. Experimental
results show that the proposed hybrid implementation significantly outperforms
the state-of-the-art implementation.
