A Generic Interface for Godunov-Type Finite Volume Methods on Adaptive Triangular Meshes

Abstract

We present and evaluate a programming interface for high performance Godunov-type finite volume applications with the framework sam(oa)2. This interface requires application developers only to provide problem-specific implementations of a set of operators, while sam(oa)2 transparently manages HPC features such as memory-efficient adaptive mesh refinement, parallelism in distributed and shared memory and vectorization of Riemann solvers. We focus especially on the performance of vectorization, which can be either managed by the framework (with compiler auto-vectorization of the operator calls) or directly by the developers in the operator implementation (possibly using more advanced techniques). We demonstrate the interface’s performance using two example applications based on variations of the shallow water equations. Our performance results show successful vectorization using both approaches, with similar performance. They also show that the applications developed with the new interface achieve performance comparable to analogous applications developed without the new layer of abstraction.