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86
Permuting Sparse Rectangular Matrices into BlockDiagonal Form
 SIAM Journal on Scientific Computing
, 2002
"... We investigate the problem of permuting a sparse rectangular matrix into block diagonal form. Block diagonal form of a matrix grants an inherent parallelism for solving the deriving problem, as recently investigated in the context of mathematical programming, LU factorization and QR factorization. W ..."
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Cited by 57 (18 self)
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We investigate the problem of permuting a sparse rectangular matrix into block diagonal form. Block diagonal form of a matrix grants an inherent parallelism for solving the deriving problem, as recently investigated in the context of mathematical programming, LU factorization and QR factorization. We propose bipartite graph and hypergraph models to represent the nonzero structure of a matrix, which reduce the permutation problem to those of graph partitioning by vertex separator and hypergraph partitioning, respectively. Our experiments on a wide range of matrices, using stateoftheart graph and hypergraph partitioning tools MeTiS and PaToH, revealed that the proposed methods yield very effective solutions both in terms of solution quality and runtime.
Robust Ordering of Sparse Matrices using Multisection
 Department of Computer Science, York University
, 1996
"... In this paper we provide a robust reordering scheme for sparse matrices. The scheme relies on the notion of multisection, a generalization of bisection. The reordering strategy is demonstrated to have consistently good performance in terms of fill reduction when compared with multiple minimum degree ..."
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Cited by 48 (2 self)
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In this paper we provide a robust reordering scheme for sparse matrices. The scheme relies on the notion of multisection, a generalization of bisection. The reordering strategy is demonstrated to have consistently good performance in terms of fill reduction when compared with multiple minimum degree and generalized nested dissection. Experimental results show that by using multisection, we obtain an ordering which is consistently as good as or better than both for a wide spectrum of sparse problems. 1 Introduction It is well recognized that finding a fillreducing ordering is crucial in the success of the numerical solution of sparse linear systems. For symmetric positivedefinite systems, the minimum degree [38] and the nested dissection [11] orderings are perhaps the most popular ordering schemes. They represent two opposite approaches to the ordering problem. However, they share a common undesirable characteristic. Both schemes produce generally good orderings, but the ordering qua...
SPOOLES: an objectoriented sparse matrix library
 In Proceedings of the Ninth SIAM Conference on Parallel Processing for Scientific Computing
, 1999
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Parallel Decomposition of Unstructured FEMMeshes
 Concurrency: Practice & Experience
, 1995
"... . We present a massively parallel algorithm for static and dynamic partitioning of unstructured FEMmeshes. The method consists of two parts. First a fast but inaccurate sequential clustering is determined which is used, together with a simple mapping heuristic, to map the mesh initially onto the pr ..."
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Cited by 41 (14 self)
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. We present a massively parallel algorithm for static and dynamic partitioning of unstructured FEMmeshes. The method consists of two parts. First a fast but inaccurate sequential clustering is determined which is used, together with a simple mapping heuristic, to map the mesh initially onto the processors of a massively parallel system. The second part of the method uses a massively parallel algorithm to remap and optimize the mesh decomposition taking several cost functions into account. It first calculates the amount of nodes that have to be migrated between pairs of clusters in order to obtain an optimal load balancing. In a second step, nodes to be migrated are chosen according to cost functions optimizing the amount and necessary communication and other measures which are important for the numerical solution method (like for example the aspect ratio of the resulting domains). The parallel parts of the method are implemented in C under Parix to run on the Parsytec GCel systems. R...
Graph Partitioning Algorithms With Applications To Scientific Computing
 Parallel Numerical Algorithms
, 1997
"... Identifying the parallelism in a problem by partitioning its data and tasks among the processors of a parallel computer is a fundamental issue in parallel computing. This problem can be modeled as a graph partitioning problem in which the vertices of a graph are divided into a specified number of su ..."
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Cited by 40 (0 self)
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Identifying the parallelism in a problem by partitioning its data and tasks among the processors of a parallel computer is a fundamental issue in parallel computing. This problem can be modeled as a graph partitioning problem in which the vertices of a graph are divided into a specified number of subsets such that few edges join two vertices in different subsets. Several new graph partitioning algorithms have been developed in the past few years, and we survey some of this activity. We describe the terminology associated with graph partitioning, the complexity of computing good separators, and graphs that have good separators. We then discuss early algorithms for graph partitioning, followed by three new algorithms based on geometric, algebraic, and multilevel ideas. The algebraic algorithm relies on an eigenvector of a Laplacian matrix associated with the graph to compute the partition. The algebraic algorithm is justified by formulating graph partitioning as a quadratic assignment p...
Using Helpful Sets to Improve Graph Bisections
 Univ. of Paderborn
, 1995
"... We describe a new, linear time heuristic for the improvement of graph bisections. The method is a variant of local search with sophisticated neighborhood relations. It is based on graphtheoretic observations that were used to find upper bounds for the bisection width of regular graphs. Efficiently ..."
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Cited by 37 (20 self)
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We describe a new, linear time heuristic for the improvement of graph bisections. The method is a variant of local search with sophisticated neighborhood relations. It is based on graphtheoretic observations that were used to find upper bounds for the bisection width of regular graphs. Efficiently implemented, the new method can serve as an alternative to the commonly used local heuristics, not only in terms of the quality of attained solutions, but also in terms of space and time requirements. We compare our heuristic with a number of well known bisection algorithms. Extensive measurements show that the new method is a real improvement for graphs of certain types. Keywords: Graph Partitioning, Graph Bisection, Recursive Bisection, Edge Separators, Mapping, Local Search, Parallel Processing. This work was partly supported by the German Research Foundation (DFG Forschergruppe "Effiziente Nutzung massiv paralleler Systeme") and by the ESPRIT Basic Research Action No. 7141 (ALCOM II)....
Towards a tighter coupling of bottomup and topdown sparse matrix ordering methods
 BIT
, 2001
"... Most stateoftheart ordering schemes for sparse matrices are a hybrid of a bottomup method such as minimum degree and a top down scheme such as George's nested dissection. In this paper we present an ordering algorithm that achieves a tighter coupling of bottomup and topdown methods. In our ..."
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Cited by 32 (0 self)
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Most stateoftheart ordering schemes for sparse matrices are a hybrid of a bottomup method such as minimum degree and a top down scheme such as George's nested dissection. In this paper we present an ordering algorithm that achieves a tighter coupling of bottomup and topdown methods. In our methodology vertex separators are interpreted as the boundaries of the remaining elements in an unfinished bottomup ordering. As a consequence, we are using bottomup techniques such as quotient graphs and special node selection strategies for the construction of vertex separators. Once all separators have been found, we are using them as a skeleton for the computation of several bottomup orderings. Experimental results show that the orderings obtained by our scheme are in general better than those obtained by other popular ordering codes.
PTScotch: A tool for efficient parallel graph ordering
"... The parallel ordering of large graphs is a difficult problem, because neither minimumdegree algorithms, nor the best graph partitioning methods that are necessary to nested dissection, parallelize or scale well. This paper presents a set of algorithms, implemented in the PTScotch software package, ..."
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Cited by 31 (5 self)
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The parallel ordering of large graphs is a difficult problem, because neither minimumdegree algorithms, nor the best graph partitioning methods that are necessary to nested dissection, parallelize or scale well. This paper presents a set of algorithms, implemented in the PTScotch software package, which allows one to order large graphs in parallel, yielding orderings the quality of which is equivalent to the one of stateoftheart sequential algorithms.
Load Balancing Strategies For Distributed Memory Machines
 MultiScale Phenomena and Their Simulation
, 1997
"... Load balancing in large parallel systems with distributed memory is a difficult task often influencing the overall efficiency of applications substantially. A number of efficient distributed load balancing strategies have been developed in the recent years. Although they are currently not generally ..."
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Cited by 29 (1 self)
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Load balancing in large parallel systems with distributed memory is a difficult task often influencing the overall efficiency of applications substantially. A number of efficient distributed load balancing strategies have been developed in the recent years. Although they are currently not generally available as part of parallel operating systems, it is often not difficult to integrate them into applications. This paper gives a classification of different load balancing problems based on application characteristics. For the case of applications out of the field of scientific computing, useful methods are described in more detail.