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We present an iterative algorithm which asymptotically scales the ∞-norm of each row and each column of a matrix to one. This scaling algorithm preserves symmetry of the original matrix and shows fast linear convergence with an asymptotic rate of 1/2. We discuss extensions of the algorithm to the 1-norm, and by inference to other norms. For the 1-norm case, we show again that convergence is linear, with the rate dependent on the spectrum of the scaled matrix. We demonstrate experimentally that the scaling algorithm improves the conditioning of the matrix and that it helps direct solvers by reducing the need for pivoting. In particular, for symmetric matrices the theoretical and experimental results highlight the potential of the proposed algorithm over existing alternatives. [ABSTRACT FROM AUTHOR]

The elimination tree model for sparse unsymmetric matrices and an algorithm for constructing it have been recently proposed by Eisenstat and Liu [SIAM J. Matrix Anal. Appl., 26 (2005), pp. 686-705] and [SIAM J. Matrix Anal. Appl., 29 (2008), pp. 1363-1381]. The construction algorithm has a worst-case time complexity of Θ(mn) for an n X n unsymmetric matrix having m off-diagonal nonzeros. We propose another algorithm that has a worst-case time complexity of ...(mlogn). We compare the two algorithms experimentally and show that both algorithms are efficient in general. The algorithm of Eisenstat and Liu is faster in many practical cases, yet there are instances in which there is a significant difference between the running time of the two algorithms in favor of the one proposed here. [ABSTRACT FROM AUTHOR]

We consider the following task assignment problem. Communicating tasks are to be assigned to heterogeneous processors interconnected with a heterogeneous network. The objective is to minimize the total sum of the execution and communication costs. The problem is NP-hard. We present an exact algorithm based on the well-known A* search. We report simulation results over a wide range of parameters where the largest solved instance contains about three hundred tasks to be assigned to eight processors. [ABSTRACT FROM AUTHOR]

We propose two heuristics for the bipartite matching problem that are amenable to shared-memory parallelization. The first heuristic is very intriguing from a parallelization perspective. It has no significant algorithmic synchronization overhead and no conflict resolution is needed across threads. We show that this heuristic has an approximation ratio of around 0.632 under some common conditions. The second heuristic is designed to obtain a larger matching by employing the well-known Karp–Sipser heuristic on a judiciously chosen subgraph of the original graph. We show that the Karp–Sipser heuristic always finds a maximum cardinality matching in the chosen subgraph. Although the Karp–Sipser heuristic is hard to parallelize for general graphs, we exploit the structure of the selected subgraphs to propose a specialized implementation which demonstrates very good scalability. We prove that this second heuristic has an approximation guarantee of around 0.866 under the same conditions as in the first algorithm. We discuss parallel implementations of the proposed heuristics on a multicore architecture. Experimental results, for demonstrating speed-ups and verifying the theoretical results in practice, are provided. [ABSTRACT FROM AUTHOR]

Abstract: -way hypergraph partitioning has an ever-growing use in parallelization of scientific computing applications. We claim that hypergraph partitioning with multiple constraints and fixed vertices should be implemented using direct -way refinement, instead of the widely adopted recursive bisection paradigm. Our arguments are based on the fact that recursive-bisection-based partitioning algorithms perform considerably worse when used in the multiple constraint and fixed vertex formulations. We discuss possible reasons for this performance degradation. We describe a careful implementation of a multi-level direct -way hypergraph partitioning algorithm, which performs better than a well-known recursive-bisection-based partitioning algorithm in hypergraph partitioning with multiple constraints and fixed vertices. We also experimentally show that the proposed algorithm is effective in standard hypergraph partitioning. [Copyright &y& Elsevier]