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      Spatiotemporal graph and hypergraph partitioning models for sparse matrix-vector multiplication on many-core architectures

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      Author(s)
      Abubaker, Nabil
      Akbudak, K.
      Aykanat, Cevdet
      Date
      2019
      Source Title
      IEEE Transactions on Parallel and Distributed Systems
      Print ISSN
      1045-9219
      Publisher
      IEEE Computer Society
      Volume
      30
      Issue
      2
      Pages
      445 - 458
      Language
      English
      Type
      Article
      Item Usage Stats
      110
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      80
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      Abstract
      There exist graph/hypergraph partitioning-based row/column reordering methods for encoding either spatial or temporal locality 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.
      Keywords
      Sparse matrix
      Sparse matrix-vector multiplication
      Data locality
      Spatial locality
      Temporal locality
      Hypergraph model
      Bipartite graph model
      Graph model
      Hypergraph partitioning
      Graph partitioning
      Intel many integrated core architecture
      Intel Xeon Phi
      Permalink
      http://hdl.handle.net/11693/53082
      Published Version (Please cite this version)
      https://dx.doi.org/10.1109/TPDS.2018.2864729
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      • Department of Computer Engineering 1435
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