The Experts below are selected from a list of 126 Experts worldwide ranked by ideXlab platform
Zahari Zlatev - One of the best experts on this subject based on the ideXlab platform.
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PARA - A Parallel Sparse QR-Factorization Algorithm
Lecture Notes in Computer Science, 1996Co-Authors: Tzvetan Ostromsky, Per Christian Hansen, Zahari ZlatevAbstract:A sparse QR-factorization algorithm for coarse-grain parallel computations is described. Initially the coefficient matrix, which is assumed to be general sparse, is reordered properly in an attempt to bring as many zero elements in the Lower Left Corner as possible. Then the matrix is partitioned into large blocks of rows and Givens rotations are applied in each block. These are independent tasks and can be done in parallel. Row and column permutations are carried out within the blocks to exploit the sparsity of the matrix.
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Improving the numerical stability and the performance of a parallel sparse solver
Computers & Mathematics with Applications, 1995Co-Authors: A.c.n. Van Duin, Tzvetan Ostromsky, Per Christian Hansen, H. Wijshoff, Zahari ZlatevAbstract:Abstract Coarse grain parallel codes for solving sparse systems of linear algebraic equations can be developed in several different ways. The following procedure is suitable for some parallel computers. A preliminary reordering of the matrix is first applied to move as many zero elements as possible to the Lower Left Corner. After that the matrix is partitioned into large blocks and the blocks in the Lower Left Corner contain only zero elements. An attempt to obtain a good load-balance is carried out by allowing the diagonal blocks to be rectangular. While the algorithm based on the above ideas has good parallel properties, some stability problems may arise during the factorization because the pivotal search is restricted to the diagonal blocks. A simple a priori procedure has been used in a previous version in an attempt to stabilize the algorithm. In this paper it is shown that three enhanced stability devices can successfully be incorporated in the algorithm so that it is further stabilized and, moreover, the parallel properties of the original algorithm are preserved. The first device is based on a dynamic check of the stability. In the second device a slightly modified reordering is used in an attempt to get more nonzero elements in the diagonal blocks (the number of candidates for pivots tends to increase in this situation and, therefore, there is a better chance to select more stable pivots). The third device applies a P 5 -like ordering as a secondary criterion in the basic reordering procedure. This tends to improve the reordering and the performance of the solver. Moreover, the device is stable, while the original P 5 ordering is often unstable. Numerical results obtained by using the three new devices are presented. The well-known sparse matrices from the Harwell-Boeing set are used in the experiments.
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PARA - Two Enhancements in a Partitioned Sparse Code
Parallel Scientific Computing, 1994Co-Authors: Per Christian Hansen, Tzvetan Ostromsky, Zahari ZlatevAbstract:Coarse grain parallel codes for solving systems of linear algebraic equations whose coefficient matrices are sparse can be developed in several different ways. The following procedure is suitable for some parallel computers. A preliminary reordering device is first applied to move as many zero elements as possible to the Lower Left Corner of the matrix. After that the matrix is partitioned into large blocks. The blocks in the Lower Left Corner contains only zero elements. An attempt to obtain a good load-balance is carried out by allowing the diagonal blocks to be rectangular.
Tzvetan Ostromsky - One of the best experts on this subject based on the ideXlab platform.
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PARA - A Parallel Sparse QR-Factorization Algorithm
Lecture Notes in Computer Science, 1996Co-Authors: Tzvetan Ostromsky, Per Christian Hansen, Zahari ZlatevAbstract:A sparse QR-factorization algorithm for coarse-grain parallel computations is described. Initially the coefficient matrix, which is assumed to be general sparse, is reordered properly in an attempt to bring as many zero elements in the Lower Left Corner as possible. Then the matrix is partitioned into large blocks of rows and Givens rotations are applied in each block. These are independent tasks and can be done in parallel. Row and column permutations are carried out within the blocks to exploit the sparsity of the matrix.
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Improving the numerical stability and the performance of a parallel sparse solver
Computers & Mathematics with Applications, 1995Co-Authors: A.c.n. Van Duin, Tzvetan Ostromsky, Per Christian Hansen, H. Wijshoff, Zahari ZlatevAbstract:Abstract Coarse grain parallel codes for solving sparse systems of linear algebraic equations can be developed in several different ways. The following procedure is suitable for some parallel computers. A preliminary reordering of the matrix is first applied to move as many zero elements as possible to the Lower Left Corner. After that the matrix is partitioned into large blocks and the blocks in the Lower Left Corner contain only zero elements. An attempt to obtain a good load-balance is carried out by allowing the diagonal blocks to be rectangular. While the algorithm based on the above ideas has good parallel properties, some stability problems may arise during the factorization because the pivotal search is restricted to the diagonal blocks. A simple a priori procedure has been used in a previous version in an attempt to stabilize the algorithm. In this paper it is shown that three enhanced stability devices can successfully be incorporated in the algorithm so that it is further stabilized and, moreover, the parallel properties of the original algorithm are preserved. The first device is based on a dynamic check of the stability. In the second device a slightly modified reordering is used in an attempt to get more nonzero elements in the diagonal blocks (the number of candidates for pivots tends to increase in this situation and, therefore, there is a better chance to select more stable pivots). The third device applies a P 5 -like ordering as a secondary criterion in the basic reordering procedure. This tends to improve the reordering and the performance of the solver. Moreover, the device is stable, while the original P 5 ordering is often unstable. Numerical results obtained by using the three new devices are presented. The well-known sparse matrices from the Harwell-Boeing set are used in the experiments.
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PARA - Two Enhancements in a Partitioned Sparse Code
Parallel Scientific Computing, 1994Co-Authors: Per Christian Hansen, Tzvetan Ostromsky, Zahari ZlatevAbstract:Coarse grain parallel codes for solving systems of linear algebraic equations whose coefficient matrices are sparse can be developed in several different ways. The following procedure is suitable for some parallel computers. A preliminary reordering device is first applied to move as many zero elements as possible to the Lower Left Corner of the matrix. After that the matrix is partitioned into large blocks. The blocks in the Lower Left Corner contains only zero elements. An attempt to obtain a good load-balance is carried out by allowing the diagonal blocks to be rectangular.
Per Christian Hansen - One of the best experts on this subject based on the ideXlab platform.
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PARA - A Parallel Sparse QR-Factorization Algorithm
Lecture Notes in Computer Science, 1996Co-Authors: Tzvetan Ostromsky, Per Christian Hansen, Zahari ZlatevAbstract:A sparse QR-factorization algorithm for coarse-grain parallel computations is described. Initially the coefficient matrix, which is assumed to be general sparse, is reordered properly in an attempt to bring as many zero elements in the Lower Left Corner as possible. Then the matrix is partitioned into large blocks of rows and Givens rotations are applied in each block. These are independent tasks and can be done in parallel. Row and column permutations are carried out within the blocks to exploit the sparsity of the matrix.
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Improving the numerical stability and the performance of a parallel sparse solver
Computers & Mathematics with Applications, 1995Co-Authors: A.c.n. Van Duin, Tzvetan Ostromsky, Per Christian Hansen, H. Wijshoff, Zahari ZlatevAbstract:Abstract Coarse grain parallel codes for solving sparse systems of linear algebraic equations can be developed in several different ways. The following procedure is suitable for some parallel computers. A preliminary reordering of the matrix is first applied to move as many zero elements as possible to the Lower Left Corner. After that the matrix is partitioned into large blocks and the blocks in the Lower Left Corner contain only zero elements. An attempt to obtain a good load-balance is carried out by allowing the diagonal blocks to be rectangular. While the algorithm based on the above ideas has good parallel properties, some stability problems may arise during the factorization because the pivotal search is restricted to the diagonal blocks. A simple a priori procedure has been used in a previous version in an attempt to stabilize the algorithm. In this paper it is shown that three enhanced stability devices can successfully be incorporated in the algorithm so that it is further stabilized and, moreover, the parallel properties of the original algorithm are preserved. The first device is based on a dynamic check of the stability. In the second device a slightly modified reordering is used in an attempt to get more nonzero elements in the diagonal blocks (the number of candidates for pivots tends to increase in this situation and, therefore, there is a better chance to select more stable pivots). The third device applies a P 5 -like ordering as a secondary criterion in the basic reordering procedure. This tends to improve the reordering and the performance of the solver. Moreover, the device is stable, while the original P 5 ordering is often unstable. Numerical results obtained by using the three new devices are presented. The well-known sparse matrices from the Harwell-Boeing set are used in the experiments.
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PARA - Two Enhancements in a Partitioned Sparse Code
Parallel Scientific Computing, 1994Co-Authors: Per Christian Hansen, Tzvetan Ostromsky, Zahari ZlatevAbstract:Coarse grain parallel codes for solving systems of linear algebraic equations whose coefficient matrices are sparse can be developed in several different ways. The following procedure is suitable for some parallel computers. A preliminary reordering device is first applied to move as many zero elements as possible to the Lower Left Corner of the matrix. After that the matrix is partitioned into large blocks. The blocks in the Lower Left Corner contains only zero elements. An attempt to obtain a good load-balance is carried out by allowing the diagonal blocks to be rectangular.
Kurt Binder - One of the best experts on this subject based on the ideXlab platform.
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Properties of the Ising magnet confined in a Corner geometry
Applied Surface Science, 2007Co-Authors: Ezequiel V. Albano, Andres De Virgiliis, Marcus Müller, Kurt BinderAbstract:Abstract The properties of Ising square lattices with nearest neighbor ferromagnetic exchange confined in a Corner geometry, are studied by means of Monte Carlo simulations. Free boundary conditions at which boundary magnetic fields ± h are applied, i.e., at the two boundary rows ending at the Lower Left Corner a field + h acts, while at the two boundary rows ending at the upper right Corner a field − h acts. For temperatures T less than the critical temperature T c of the bulk, this boundary condition leads to the formation of two domains with opposite orientation of the magnetization direction, separated by an interface which for T larger than the filling transition temperature T f ( h ) runs from the upper Left Corner to the Lower right Corner, while for T T f ( h ) this interface is localized either close to the Lower Left Corner or close to the upper right Corner. It is shown that for T = T f ( h ) the magnetization profile m ( z ) in the z-direction normal to the interface simply is linear and the interfacial width scales as w ∝ L , while for T > T f ( h ) it scales as w ∝ L . The distribution P ( l ) of the interface position l (measured along the z-direction from the Corners) decays exponentially for T T f ( h ) from either Corner, is essentially flat for T = T f ( h ) , and is a Gaussian centered at the middle of the diagonal for T > T f ( h ) . Unlike the findings for critical wetting in the thin film geometry of the Ising model, the Monte Carlo results for Corner wetting are in very good agreement with the theoretical predictions.
Ezequiel V. Albano - One of the best experts on this subject based on the ideXlab platform.
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Properties of the Ising magnet confined in a Corner geometry
Applied Surface Science, 2007Co-Authors: Ezequiel V. Albano, Andres De Virgiliis, Marcus Müller, Kurt BinderAbstract:Abstract The properties of Ising square lattices with nearest neighbor ferromagnetic exchange confined in a Corner geometry, are studied by means of Monte Carlo simulations. Free boundary conditions at which boundary magnetic fields ± h are applied, i.e., at the two boundary rows ending at the Lower Left Corner a field + h acts, while at the two boundary rows ending at the upper right Corner a field − h acts. For temperatures T less than the critical temperature T c of the bulk, this boundary condition leads to the formation of two domains with opposite orientation of the magnetization direction, separated by an interface which for T larger than the filling transition temperature T f ( h ) runs from the upper Left Corner to the Lower right Corner, while for T T f ( h ) this interface is localized either close to the Lower Left Corner or close to the upper right Corner. It is shown that for T = T f ( h ) the magnetization profile m ( z ) in the z-direction normal to the interface simply is linear and the interfacial width scales as w ∝ L , while for T > T f ( h ) it scales as w ∝ L . The distribution P ( l ) of the interface position l (measured along the z-direction from the Corners) decays exponentially for T T f ( h ) from either Corner, is essentially flat for T = T f ( h ) , and is a Gaussian centered at the middle of the diagonal for T > T f ( h ) . Unlike the findings for critical wetting in the thin film geometry of the Ising model, the Monte Carlo results for Corner wetting are in very good agreement with the theoretical predictions.
