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A Nested Dissection Approach for Sparse Matrix Partitioning
, 2007
"... We consider how to partition and distribute sparse matrices among processors to reduce communication cost in sparse matrix computations, in particular, sparse matrixvector multiplication. We consider 2d distributions, where the distribution is not constrained to just rows or columns. We present a n ..."
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Cited by 4 (3 self)
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We consider how to partition and distribute sparse matrices among processors to reduce communication cost in sparse matrix computations, in particular, sparse matrixvector multiplication. We consider 2d distributions, where the distribution is not constrained to just rows or columns. We present a new model and an algorithm based on vertex separators and nested dissection. Preliminary numerical results for sparse matrices from real applications indicate the new method performs consistently better than traditional 1d partitioning and is often also better than current 2d methods. 1
HYPERGRAPHBASED COMBINATORIAL OPTIMIZATION OF MATRIXVECTOR MULTIPLICATION
, 2009
"... Combinatorial scientific computing plays an important enabling role in computational science, particularly in high performance scientific computing. In this thesis, we will describe our work on optimizing matrixvector multiplication using combinatorial techniques. Our research has focused on two di ..."
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Combinatorial scientific computing plays an important enabling role in computational science, particularly in high performance scientific computing. In this thesis, we will describe our work on optimizing matrixvector multiplication using combinatorial techniques. Our research has focused on two different problems in combinatorial scientific computing, both involving matrixvector multiplication, and both are solved using hypergraph models. For both of these problems, the cost of the combinatorial optimization process can be effectively amortized over many matrixvector products. The first problem we address is optimization of serial matrixvector multiplication for relatively small, dense matrices that arise in finite element assembly. Previous work showed that combinatorial optimization of matrixvector multiplication can lead to faster assembly of finite element stiffness matrices by eliminating redundant operations. Based on a graph model characterizing row relationships, a more efficient set of operations can be generated to perform matrixvector multiplication. We improved this graph model by extending the
PARTITIONING HYPERGRAPHS IN SCIENTIFIC COMPUTING APPLICATIONS THROUGH VERTEX SEPARATORS ON GRAPHS
"... Abstract. The modeling flexibility provided by hypergraphs has drawn a lot of interest from the combinatorial scientific community, leading to novel models and algorithms, their applications, and development of associated tools. Hypergraphs are now a standard tool in combinatorial scientific computi ..."
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Abstract. The modeling flexibility provided by hypergraphs has drawn a lot of interest from the combinatorial scientific community, leading to novel models and algorithms, their applications, and development of associated tools. Hypergraphs are now a standard tool in combinatorial scientific computing. The modeling flexibility of hypergraphs however, comes at a cost: algorithms on hypergraphs are inherently more complicated than those on graphs, which sometimes translates to nontrivial increases in processing times. Neither the modeling flexibility of hypergraphs, nor the runtime efficiency of graph algorithms can be overlooked. Therefore, the new research thrust should be how to cleverly tradeoff between the two. This work addresses one method for this tradeoff by solving the hypergraph partitioning problem by finding vertex separators on graphs. Specifically, we investigate how to solve the hypergraph partitioning problem by seeking a vertex separator on its net intersection graph (NIG), where each net of the hypergraph is represented by a vertex, and two vertices share an edge if their nets have a common vertex. We propose a vertexweighting scheme to attain good nodebalanced hypergraphs, since the NIG model cannot preserve node balancing information. Vertexremoval and vertexsplitting techniques are described to optimize cutnet and connectivity metrics, respectively, under the recursive bipartitioning paradigm. We also developed implementations of our proposed hypergraph partitioning formulations by adopting and modifying a stateoftheart graph partitioning by vertex separator tool onmetis. Experiments conducted on a large collection of sparse matrices demonstrate the effectiveness of our proposed techniques. Key words. hypergraph partitioning; combinatorial scientific computing; graph partitioning by vertex separator; sparse matrices. AMS subject classifications.