13 pages, 6 figures.A block tridiagonal matrix is factored with minimal fill-in using a cyclic reduction algorithm that is easily parallelized. Storage of the factored blocks allows the application of the inverse to multiple right-hand sides which may not be known at factorization time. Scalability with the number of block rows is achieved with cyclic reduction, while scalability with the block size is achieved using multithreaded routines (OpenMP, GotoBLAS) for block matrix manipulation. This dual scalability is a noteworthy feature of this new solver, as well as its ability to efficiently handle arbitrary (non-powers-of-2) block row and processor numbers. Comparison with a state-of-the art parallel sparse solver is presented. It is expect...
We formalize the concept of patm!kZfitorhztim as a set of scalar factorizations. By means of this co...
AbstractA parallel version of the cyclic reduction algorithm for the solution of tridiagonal linear ...
While parallel computers offer significant computational performance, it is generally necessary to e...
13 pages, 6 figures.A block tridiagonal matrix is factored with minimal fill-in using a cyclic reduc...
13 pages, 6 figures.A block tridiagonal matrix is factored with minimal fill-in using a cyclic reduc...
13 pages, 6 figures.A block tridiagonal matrix is factored with minimal fill-in using a cyclic reduc...
13 pages, 6 figures.A block tridiagonal matrix is factored with minimal fill-in using a cyclic reduc...
Parallel computing a b s t r a c t A block tridiagonal matrix is factored with minimal fill-in using...
textabstractSolution of large sparse systems of linear equations continues to be a major research ar...
Two-level parallelization is introduced to solve a massive block-tridiagonal matrix system. One-leve...
this paper, the wrap-around partitioning methodology, originally proposed by Hegland [1], is conside...
While parallel computers offer significant computational performance, it is generally nec-essary to ...
Engineering, scientific, and financial applications often require the simultaneous solution of a lar...
Tridiagonal diagonally dominant linear systems arise in many scientific and engineering applications...
AbstractA parallel version of the cyclic reduction algorithm for the solution of tridiagonal linear ...
We formalize the concept of patm!kZfitorhztim as a set of scalar factorizations. By means of this co...
AbstractA parallel version of the cyclic reduction algorithm for the solution of tridiagonal linear ...
While parallel computers offer significant computational performance, it is generally necessary to e...
13 pages, 6 figures.A block tridiagonal matrix is factored with minimal fill-in using a cyclic reduc...
13 pages, 6 figures.A block tridiagonal matrix is factored with minimal fill-in using a cyclic reduc...
13 pages, 6 figures.A block tridiagonal matrix is factored with minimal fill-in using a cyclic reduc...
13 pages, 6 figures.A block tridiagonal matrix is factored with minimal fill-in using a cyclic reduc...
Parallel computing a b s t r a c t A block tridiagonal matrix is factored with minimal fill-in using...
textabstractSolution of large sparse systems of linear equations continues to be a major research ar...
Two-level parallelization is introduced to solve a massive block-tridiagonal matrix system. One-leve...
this paper, the wrap-around partitioning methodology, originally proposed by Hegland [1], is conside...
While parallel computers offer significant computational performance, it is generally nec-essary to ...
Engineering, scientific, and financial applications often require the simultaneous solution of a lar...
Tridiagonal diagonally dominant linear systems arise in many scientific and engineering applications...
AbstractA parallel version of the cyclic reduction algorithm for the solution of tridiagonal linear ...
We formalize the concept of patm!kZfitorhztim as a set of scalar factorizations. By means of this co...
AbstractA parallel version of the cyclic reduction algorithm for the solution of tridiagonal linear ...
While parallel computers offer significant computational performance, it is generally necessary to e...