Title
Accelerating Band Linear Algebra Operations on GPUs with Application in Model Reduction
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.
Year
DOI
Venue
2014
10.1007/978-3-319-09153-2_29
ICCSA (6)
Keywords
Field
DocType
high performance,linear algebra,band linear systems,control theory,graphics processors
Graphics,Linear algebra,Lyapunov equation,Coefficient matrix,Linear system,Computer science,Parallel computing,Computational science,Solver,Sparse matrix,LU decomposition
Conference
Volume
ISSN
Citations 
8584
0302-9743
4
PageRank 
References 
Authors
0.59
4
6
Name
Order
Citations
PageRank
Peter Benner1825114.06
Ernesto Dufrechou22511.02
Pablo Ezzatti312428.24
Pablo Igounet482.10
Enrique S. Quintana-Ortí51317150.59
Alfredo Remón69515.95