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    Spatiotemporal Graph and Hypergraph Partitioning Models for Sparse Matrix-Vector Multiplication on Many-Core Architectures

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    Type
    Article
    Authors
    Abubaker, Nabil F. T.
    Akbudak, Kadir
    Aykanat, Cevdet
    KAUST Department
    Applied Mathematics and Computational Science Program
    Extreme Computing Research Center
    Date
    2018-08-10
    Online Publication Date
    2018-08-10
    Print Publication Date
    2018
    Permanent link to this record
    http://hdl.handle.net/10754/628848
    
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    Abstract
    There exist graph/hypergraph partitioning-based row/column reordering methods for encoding either spatial or temporal locality separately for sparse matrix-vector multiplication (SpMV) operations. Spatial and temporal hypergraph models in these methods are extended to encapsulate both spatial and temporal localities based on cut/uncut net categorization obtained from vertex partitioning. These extensions of spatial and temporal hypergraph models encode the spatial locality primarily and the temporal locality secondarily, and vice-versa, respectively. However, the literature lacks models that simultaneously encode both spatial and temporal localities utilizing only vertex partitioning for further improving the performance of SpMV on shared-memory architectures. In order to fill this gap, we propose a novel spatiotemporal hypergraph model that leads to a one-phase spatiotemporal reordering method which encodes both types of locality simultaneously. We also propose a framework for spatiotemporal methods which encodes both types of locality in two dependent phases and two separate phases. The validity of the proposed spatiotemporal models and methods are tested on a wide range of sparse matrices and the experiments are performed on both a 60-core Intel Xeon Phi processor and a Xeon processor. Results show the validity of the methods via almost doubling the Gflop/s performance through enhancing data locality in parallel SpMV operations.
    Citation
    Abubaker NFT, Akbudak K, Aykanat C (2018) Spatiotemporal Graph and Hypergraph Partitioning Models for Sparse Matrix-Vector Multiplication on Many-Core Architectures. IEEE Transactions on Parallel and Distributed Systems: 1–1. Available: http://dx.doi.org/10.1109/TPDS.2018.2864729.
    Sponsors
    This work was partially supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant EEEAG-115E212.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Parallel and Distributed Systems
    DOI
    10.1109/TPDS.2018.2864729
    Additional Links
    https://ieeexplore.ieee.org/document/8432126
    ae974a485f413a2113503eed53cd6c53
    10.1109/TPDS.2018.2864729
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Extreme Computing Research Center

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