Improving the performance of matrix inversion with a Tesla GPU

Abstract

We study two different techniques for the computation of a matrix inverse, the traditional approach based on Gaussian factorization and the Gauss-Jordan elimination alternative more suitable for parallel architectures. The target architecture is a current general-purpose multi-core processor (CPU) connected to a graphics processor (GPU). Parallelism is obtained from the use of libraries MKL (for the CPU) and CUBLAS (for the GPU), as well as, performing simultaneously operations in both architectures. Numerical experiments performed on a system equipped with two Intel QuadCore processors and a Tesla C1060 GPU, illustrate the efficiency attained by the Gauss-Jordan elimination implementation.