Using graphics processors to accelerate the computation of the matrix inverse

We study the use of massively parallel architectures for computing a matrix inverse. Two different algorithms are reviewed, the traditional approach based on Gaussian elimination and the Gauss–Jordan elimination alternative, and several high performance implementations are presented and evaluated. The target architecture is a current general-purpose multicore processor (CPU) connected to a graphics processor (GPU). Numerical experiments show the efficiency attained by the proposed implementations and how the computation of large-scale inverses, which only a few years ago would have required a distributed-memory cluster, take only a few minutes on a hybrid architecture formed by a multicore CPU and a GPU.