Spatiotemporal Graph and Hypergraph Partitioning Models for Sparse Matrix-Vector Multiplication on Many-Core Architectures
KAUST DepartmentApplied Mathematics and Computational Science Program
Extreme Computing Research Center
Online Publication Date2018-08-10
Print Publication Date2018
Permanent link to this recordhttp://hdl.handle.net/10754/628848
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AbstractThere 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.
CitationAbubaker 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.
SponsorsThis work was partially supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant EEEAG-115E212.
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