SSG-AT: An Auto-tuning Method of Sparse Matrix-vector Multiplicataion for Semi-structured Grids -- An Adaptation to OpenFOAM

We are developing ppOpen-AT, which is an infrastructureof auto-tuning (AT) for ppOpen-HPC. ppOpen-HPC is numerical middleware for post Petascale era. In this study, we propose a new auto-tuning (AT) facility for semi-structured grids in OpenFOAM. We focus on sparse matrix-vector multiplication and the matrix storage formats. Using the features of input data and mesh connectivity, we propose a hybrid storage format that is suitable for semistructured grids. We evaluate the proposed AT facility on the T2K supercomputer and an Intel Xeon cluster. For a typical computational fluid dynamics scenario, we obtain speedup factors of 1.3 on the T2K and 1.84 on the Xeon cluster. These results indicate that the proposed AT method has the potential to select the optimal data format according to features of the input sparse matrix.