This paper presents a discussion on 2D block mappings for the sparse Cholesky factorization on parallel MIMD architectures with distributed memory. It introduces the fan-in algorithm in a general manner and proposes several mapping strategies. The grid mapping with row balancing, inspired from Rothberg's work [21, 22] proved to be more robust than the original fan-out algorithm. Even more efficient is the proportional mapping, as show the experiments on a 32 processors IBM SP1 and on a Cray T3D. Subforest-tosubcube mappings are also considered and give good results on the T3D
Abstract. A style for programming problems from matrix algebra is developed with a familiar example ...
Systems of linear equations arise at the heart of many scientific and engineering applications. Many...
AbstractA parallel algorithm is developed for Cholesky factorization on a shared-memory multiprocess...
This paper presents a discussion on 2D block mappings for the sparse Cholesky factorization on paral...
Programme 1 - Architectures paralleles, bases de donnees, reseaux et systemes distribues. Projet PAM...
Compared to the customary column-oriented approaches, block-oriented, distributed-memory sparse Chol...
We develop an algorithm for computing the symbolic and numeric Cholesky factorization of a large sp...
As sequential computers seem to be approaching their limits in CPU speed there is increasing intere...
The bottleneck of most data analyzing systems, signal processing systems, and intensive computing sy...
We describe the design, implementation, and performance of a new parallel sparse Cholesky factoriza...
We describe a parallel algorithm for finding the Cholesky factorization of a sparse symmetric posit...
International audienceSolving large sparse linear systems by iterative methods has often been quite ...
Systems of linear equations of the form $Ax = b,$ where $A$ is a large sparse symmetric positive de...
This paper presents a comparative study of two data mapping schemes for parallel sparse LU factoriza...
Several fine grained parallel algorithms were developed and compared to compute the Cholesky factori...
Abstract. A style for programming problems from matrix algebra is developed with a familiar example ...
Systems of linear equations arise at the heart of many scientific and engineering applications. Many...
AbstractA parallel algorithm is developed for Cholesky factorization on a shared-memory multiprocess...
This paper presents a discussion on 2D block mappings for the sparse Cholesky factorization on paral...
Programme 1 - Architectures paralleles, bases de donnees, reseaux et systemes distribues. Projet PAM...
Compared to the customary column-oriented approaches, block-oriented, distributed-memory sparse Chol...
We develop an algorithm for computing the symbolic and numeric Cholesky factorization of a large sp...
As sequential computers seem to be approaching their limits in CPU speed there is increasing intere...
The bottleneck of most data analyzing systems, signal processing systems, and intensive computing sy...
We describe the design, implementation, and performance of a new parallel sparse Cholesky factoriza...
We describe a parallel algorithm for finding the Cholesky factorization of a sparse symmetric posit...
International audienceSolving large sparse linear systems by iterative methods has often been quite ...
Systems of linear equations of the form $Ax = b,$ where $A$ is a large sparse symmetric positive de...
This paper presents a comparative study of two data mapping schemes for parallel sparse LU factoriza...
Several fine grained parallel algorithms were developed and compared to compute the Cholesky factori...
Abstract. A style for programming problems from matrix algebra is developed with a familiar example ...
Systems of linear equations arise at the heart of many scientific and engineering applications. Many...
AbstractA parallel algorithm is developed for Cholesky factorization on a shared-memory multiprocess...
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