Utilizing Robustness of Krylov Subspace Methods in Reducing the Effort of Sparse Matrix Vector Multiplication (bibtex)
by ,
Abstract:
Abstract Iterative solvers based on Krylov subspace method proved to be robust in the presence of inexact matrix vector products. In this paper, the inexactness is induced by reducing the number of nonzero elements of the matrix while maintaining the convergence of the iterative solver. We benefit from this property by reducing the computational effort and the communication volume when implementing sparse matrix vector multiplication (SMVM) on Network-on-Chip (NoC).
Reference:
A. Mansour, J. Götze, Utilizing Robustness of Krylov Subspace Methods in Reducing the Effort of Sparse Matrix Vector Multiplication, In Procedia Computer Science, vol. 18, pp. 2406-2409, 2013.
Bibtex Entry:
@Article{Mansour2013a,
  Title                    = {Utilizing Robustness of Krylov Subspace Methods in Reducing the Effort of Sparse Matrix Vector Multiplication},
  Author                   = {A. Mansour and J. G\"otze},
  Journal                  = {Procedia Computer Science},
  Year                     = {2013},
  Pages                    = {2406-2409},
  Volume                   = {18},

  Abstract                 = {Abstract Iterative solvers based on Krylov subspace method proved to be robust in the presence of inexact matrix vector products. In this paper, the inexactness is induced by reducing the number of nonzero elements of the matrix while maintaining the convergence of the iterative solver. We benefit from this property by reducing the computational effort and the communication volume when implementing sparse matrix vector multiplication (SMVM) on Network-on-Chip (NoC).},
  Doi                      = {10.1016/j.procs.2013.05.412}
}
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