Using State-of-the-Art Sparse Matrix Optimizations for Accelerating the Performance of Multiphysics Simulations

Introduction

Multiphysics simulations are at the core of modern Computer Aided Engineering (CAE) allowing the analysis of multiple, simultaneously acting physical phenomena. These simulations often rely on Finite Element Methods (FEM) and the solution of large linear systems which, in turn, end up in multiple calls of the costly Sparse Matrix-Vector Multiplication (SpM×V) kernel. The major—and mostly inherent—performance problem of the this kernel is its very low flop:byte ratio, meaning that the algorithm must retrieve a significant amount of data from the memory hierarchy in order to perform a useful operation.

The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007–2013) under grant agreement n° RI-261557.