A new method, namely the Parallel Two-Level Hybrid (PTH) method, is developed to solve tridiagonal systems on parallel computers. PTH is designed based on Parallel Diagonal Dominant (PDD) algorithm. Like PDD, PTH is highly scalable. It provides accurate solutions when PDD may not be applicable and maintains a near PDD performance when the underlying machine ensemble size is large. By controlling its two-level partition, PTH can deliver optimal performance for different machine ensemble and problem sizes. Theoretical analyses and numerical experiments indicate that PTH is significantly better than existing methods for many scientific and engineering applications
this paper, the wrap-around partitioning methodology, originally proposed by Hegland [1], is conside...
[[abstract]]The solution of special linear, circulant-tridiagonal systems is considered. In this pap...
A new parallel approach for solving a pentadiagonal linear system is presented. The parallel partiti...
Abstract—A new method, namely, the Parallel Two-Level Hybrid (PTH) method, is developed to solve tri...
The Parallel Diagonal Dominant (PDD) algorithm is an efficient tridiagonal solver. In this paper, a ...
The Parallel Diagonal Dominant (PDD) algorithm is a highly efficient, ideally scalable tridiagonal s...
In this paper we describe an hybrid algorithm for an even number of processors based on an algorithm...
We formalize the concept of patm!kZfitorhztim as a set of scalar factorizations. By means of this co...
AbstractWe formalize the concept of parallel factorization as a set of scalar factorizations. By mea...
An optimized parallel algorithm is proposed to solve the problem occurred in the process of complica...
In this paper, we consider a new form of the arithmetic mean method for solving large block tridiago...
AbstractIn this paper, we consider a new form of the arithmetic mean method for solving large block ...
In this paper, we consider a new form of the arithmetic mean method for solving large block tridiago...
While parallel computers offer significant computational performance, it is generally nec-essary to ...
Various tridiagonal solvers have been proposed in recent years for different parallel platforms. In ...
this paper, the wrap-around partitioning methodology, originally proposed by Hegland [1], is conside...
[[abstract]]The solution of special linear, circulant-tridiagonal systems is considered. In this pap...
A new parallel approach for solving a pentadiagonal linear system is presented. The parallel partiti...
Abstract—A new method, namely, the Parallel Two-Level Hybrid (PTH) method, is developed to solve tri...
The Parallel Diagonal Dominant (PDD) algorithm is an efficient tridiagonal solver. In this paper, a ...
The Parallel Diagonal Dominant (PDD) algorithm is a highly efficient, ideally scalable tridiagonal s...
In this paper we describe an hybrid algorithm for an even number of processors based on an algorithm...
We formalize the concept of patm!kZfitorhztim as a set of scalar factorizations. By means of this co...
AbstractWe formalize the concept of parallel factorization as a set of scalar factorizations. By mea...
An optimized parallel algorithm is proposed to solve the problem occurred in the process of complica...
In this paper, we consider a new form of the arithmetic mean method for solving large block tridiago...
AbstractIn this paper, we consider a new form of the arithmetic mean method for solving large block ...
In this paper, we consider a new form of the arithmetic mean method for solving large block tridiago...
While parallel computers offer significant computational performance, it is generally nec-essary to ...
Various tridiagonal solvers have been proposed in recent years for different parallel platforms. In ...
this paper, the wrap-around partitioning methodology, originally proposed by Hegland [1], is conside...
[[abstract]]The solution of special linear, circulant-tridiagonal systems is considered. In this pap...
A new parallel approach for solving a pentadiagonal linear system is presented. The parallel partiti...