Abstract
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 6× 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 3× acceleration on the solution of a large-scale benchmark arising in model reduction.
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Benner, P., Dufrechou, E., Ezzatti, P., Igounet, P., Quintana-Ortí, E.S., Remón, A. (2014). Accelerating Band Linear Algebra Operations on GPUs with Application in Model Reduction. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8584. Springer, Cham. https://doi.org/10.1007/978-3-319-09153-2_29
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DOI: https://doi.org/10.1007/978-3-319-09153-2_29
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