首页|Inexact Sparse Matrix Vector Multiplication in Krylov Subspace Methods: An Application-Oriented Reduction Method
Inexact Sparse Matrix Vector Multiplication in Krylov Subspace Methods: An Application-Oriented Reduction Method
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NETL
Springer Nature
Iterative solvers based on Krylov subspace method proved to be robust in the presence of well monitored inexact matrix vector products。 In this paper， we show that the iterative solver performs well while gradually reducing the number of nonzero elements of the matrix throughout the iterations。 We benefit from this robustness in reducing the computational effort and the communication volume when implementing sparse matrix vector multiplication (SMVM) on a Network-on-Chip (NoC)。
NETL
Springer Nature
