Cache-Aware Matrix Polynomials

Efficient solvers for partial differential equations are among the most important areas of algorithmic research in high-performance computing. In this paper we present a new optimization for solving linear autonomous partial differential equations. Our approach is based on polynomial approximations for exponential time integration, which involves the computation of matrix polynomial terms ([Image: see text]) in every time step. This operation is very memory intensive and requires targeted optimizations. In our approach, we exploit the cache-hierarchy of modern computer architectures using a temporal cache blocking approach over the matrix polynomial terms. We develop two single-core implementations realizing cache blocking over several sparse matrix-vector multiplications of the polynomial approximation and compare it to a reference method that performs the computation in the traditional iterative way. We evaluate our approach on three different hardware platforms and for a wide range of different matrices and demonstrate that our approach achieves time savings of up to 50% for a large number of matrices. This is especially the case on platforms with large caches, significantly increasing the performance to solve linear autonomous differential equations.