Matrix computations (3rd ed.)
State-space truncation methods for parallel model reduction of large-scale systems
Parallel Computing - Special issue: Parallel and distributed scientific and engineering computing
Approximation of Large-Scale Dynamical Systems (Advances in Design and Control) (Advances in Design and Control)
Solving linear-quadratic optimal control problems on parallel computers
Optimization Methods & Software
Exploiting the capabilities of modern GPUs for dense matrix computations
Concurrency and Computation: Practice & Experience
Reduction to condensed forms for symmetric eigenvalue problems on multi-core architectures
PPAM'09 Proceedings of the 8th international conference on Parallel processing and applied mathematics: Part I
Using hybrid CPU-GPU platforms to accelerate the computation of the matrix sign function
Euro-Par'09 Proceedings of the 2009 international conference on Parallel processing
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Model order reduction of dynamical linear time-invariant system appears in many scientific and engineering applications. Numerically reliable SVD-based methods for this task require $\mathcal{O}(n^3)$ floating-point arithmetic operations, with n being in the range 103−105 for many practical applications. In this paper we investigate the use of graphics processors (GPUs) to accelerate model reduction of large-scale linear systems via Balanced Stochastic Truncation, by off-loading the computationally intensive tasks to this device. Experiments on a hybrid platform consisting of state-of-the-art general-purpose multi-core processors and a GPU illustrate the potential of this approach.