AB
A.R.T.Y. Bartelink
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Krylov subspace methods are methods to find solutions to high-dimensional linear systems efficiently. One of those methods is the Induced Dimension Reduction method, a method that has been implemented in a parallel Fortran package. To ensure the efficiency of this package, it is important that low-level computations go fast, like creating the LU decomposition.
In this paper, a parallel algorithm for the LU decomposition is developed and improved. Later, the algorithm is extended to work efficiently for matrices with a special structure, band matrices. From this, it follows that the algorithms created do show an increase in efficiency and a decrease in computational time. Furthermore, initial testing after integration in the IDR package also shows an improvement in computational time. ...
In this paper, a parallel algorithm for the LU decomposition is developed and improved. Later, the algorithm is extended to work efficiently for matrices with a special structure, band matrices. From this, it follows that the algorithms created do show an increase in efficiency and a decrease in computational time. Furthermore, initial testing after integration in the IDR package also shows an improvement in computational time. ...
Krylov subspace methods are methods to find solutions to high-dimensional linear systems efficiently. One of those methods is the Induced Dimension Reduction method, a method that has been implemented in a parallel Fortran package. To ensure the efficiency of this package, it is important that low-level computations go fast, like creating the LU decomposition.
In this paper, a parallel algorithm for the LU decomposition is developed and improved. Later, the algorithm is extended to work efficiently for matrices with a special structure, band matrices. From this, it follows that the algorithms created do show an increase in efficiency and a decrease in computational time. Furthermore, initial testing after integration in the IDR package also shows an improvement in computational time.
In this paper, a parallel algorithm for the LU decomposition is developed and improved. Later, the algorithm is extended to work efficiently for matrices with a special structure, band matrices. From this, it follows that the algorithms created do show an increase in efficiency and a decrease in computational time. Furthermore, initial testing after integration in the IDR package also shows an improvement in computational time.