Result: Multi-pass mapping schemes for parallel sparse matrix computations

Title:
Multi-pass mapping schemes for parallel sparse matrix computations
Authors:
MALKOWSKI, Konrad, RAGHAVAN, Padma
Source:
Computational science (Atlanta GA, 22-25 May 2005)Lecture notes in computer science. :245-255
Publisher Information:
Berlin: Springer, 2005.
Publication Year:
2005
Physical Description:
print, 20 ref 3
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Algorithme parallèle, Parallel algorithm, Algoritmo paralelo, Calcul parallèle, Parallel computation, Cálculo paralelo, Factorisation matricielle, Matrix factorization, Factorizacion matricial, Gestion tâche, Task scheduling, Gestión labor, Matrice creuse, Sparse matrix, Matriz dispersa, Modélisation, Modeling, Modelización, Multiprocesseur, Multiprocessor, Multiprocesador, Méthode arborescente, Tree structured method, Método arborescente, Méthode heuristique, Heuristic method, Método heurístico, Méthode itérative, Iterative method, Método iterativo, Méthode sousespace Krylov, Krylov subspace method, Método subespacio Krylov, Méthode élément fini, Finite element method, Método elemento finito, Parallélisme, Parallelism, Paralelismo, Préconditionnement, Preconditioning, Precondicionamiento
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Computer Science and Engineering, The Pennsylvania State University, 343K 1ST Building, University Park, PA 16802-6106, United States
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=16991697
Rights:
Copyright 2005 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.16991697
Database:
PASCAL Archive

Further Information

Consider the solution of a large sparse linear system Ax = b on multiprocessors. A parallel sparse matrix factorization is required in a direct solver. Alternatively, if Krylov subspace iterative methods are used, then incomplete forms of parallel sparse factorization are required for preconditioning. In such schemes, the underlying parallel computation is tree-structured, utilizing task-parallelism at lower levels of the tree and data-parallelism at higher levels. The proportional heuristic has typically been used to map the data and computation to processors. However, for sparse systems from finite-element methods on complex domains, the resulting assignments can exhibit significant load-imbalances. In this paper, we develop a multi-pass mapping scheme to reduce such load imbalances and we demonstrate its effectiveness for a test suite of large sparse matrices. Our scheme can also be used to generate improved mappings for tree-structured applications beyond those considered in this paper.