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ÐIn this work, we show that the standard graph-partitioning-based decomposition of sparse matrices does not reflect the actual communication volume requirement for parallel matrix-vector multiplication. We propose two computational hypergraph models which avoid this crucial deficiency of the graph model. The proposed models reduce the decomposition problem… (More)

Many emerging large-scale data science applications require searching large graphs distributed across multiple memories and processors. This paper presents a distributed breadth-first search (BFS) scheme that scales for random graphs with up to three billion vertices and 30 billion edges. Scalability was tested on IBM BlueGene/L with 32,768 nodes at the… (More)

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. To represent the nonzero structure of a matrix, we propose… (More)

1. Introduction. Partitioning and load-balancing are important issues in parallel scientific computing. The goal is to distribute data (and work) evenly among processors such as to reduce communication cost and achieve maximal performance. Graph partitioning has long served as a useful model for load balancing in parallel computing. Data are represented as… (More)

A k-core of a graph is a maximal connected subgraph in which every vertex is connected to at least k vertices in the subgraph. k-core decomposition is often used in large-scale network analysis, such as community detection, protein function prediction, visualization, and solving NP-Hard problems on real networks efficiently, like maximal clique finding. In… (More)

Subsetting through Range Queries a hyperbox defined in the multi-dimensional space underlying the dataset items whose multi-dimensional coordinates fall into the box are retrieved.

We propose a new hypergraph model for the decomposition of irregular computational domains. This work fo-cuses on the decomposition of sparse matrices for parallel matrix-vector multiplication. However, the proposed model can also be used to decompose computational domains of other parallel reduction problems. We propose a " fine-grain " hypergraph model… (More)

We propose a new two-phase method for the coarse-grain decomposition of irregular computational domains. This work focuses on the 2D partitioning of sparse matrices for parallel matrix-vector multiplication. However, the proposed model can also be used to decompose computational domains of other parallel reduction problems. This work also introduces the use… (More)