Browsing by Subject "Hypergraph Model"

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    Increasing data reuse in parallel sparse matrix-vector and matrix-transpose-vector multiply on shared-memory architectures
    (2014) Karsavuran, Mustafa Ozan
    Sparse matrix-vector and matrix-transpose-vector multiplications (Sparse AAT x) are the kernel operations used in iterative solvers. Sparsity pattern of the input matrix A, as well as its transpose, remains the same throughout the iterations. CPU cache could not be used properly during these Sparse AAT x operations due to irregular sparsity pattern of the matrix. We propose two parallelization strategies for Sparse AAT x. Our methods partition A matrix in order to exploit cache locality for matrix nonzeros and vector entries. We conduct experiments on the recently-released Intel R Xeon PhiTM coprocessor involving large variety of sparse matrices. Experimental results show that proposed methods achieve higher performance improvement than the state-of-the-art methods in the literature.
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    Minimizing communication cost in fine-grain partitioning of sparse matrices
    (Springer, 2003) Uçar, B.; Aykanat, Cevdet
    We show a two-phase approach for minimizing various communication-cost metrics in fine-grain partitioning of sparse matrices for parallel processing. In the first phase, we obtain a partitioning with the existing tools on the matrix to determine computational loads of the processor. In the second phase, we try to minimize the communication-cost metrics. For this purpose, we develop communication-hypergraph and partitioning models. We experimentally evaluate the contributions on a PC cluster. © Springer-Verlag Berlin Heidelberg 2003.