Accelerating Band Linear Algebra Operations on GPUs with Application in Model Reduction
Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg
>Max Planck Institute for Dynamics of Complex Technical Systems, 2014
@article{benner2014accelerating,
title={Accelerating Band Linear Algebra Operations on GPUs with Application in Model Reduction},
author={Benner, Peter and Dufrechou, Ernesto and Ezzatti, Pablo and Igounet, Pablo and Quintana-Orti, Enrique S. and Remon, Alfredo},
year={2014}
}
In this paper we present new hybrid CPU-GPU routines to accelerate the solution of linear systems, with band coefficient matrix, by off-loading the major part of the computations to the GPU and leveraging highly tuned implementations of the BLAS for the graphics processor. Our experiments with an nVidia S2070 GPU report speed-ups up to 6x for the hybrid band solver based on the LU factorization over analogous CPU-only routines in Intel’s MKL. As a practical demonstration of these benefits, we plug the new CPU-GPU codes into a sparse matrix Lyapunov equation solver, showing a 3x acceleration on the solution of a large-scale benchmark arising in model reduction.
June 23, 2014 by hgpu
