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  • Sparse matrix-vector multiplication (shortly spMV) is one of the most common subroutines in the numerical linear algebra. The parallelization of this task looks easy and straightforward, but it is not optimal in general case. This paper discuss some matrix-processor mappings and their impact on parallel spMV execution on massively parallel systems. We try to balance the performance and the overhead of the required transformation. We also present algorithms for redistribution. We propose four quality measures and derive lower and upper bound for different mappings. Our $spMV$ algorithms are scalable for almost all matrices arising from various technical areas.
  • Sparse matrix-vector multiplication (shortly spMV) is one of the most common subroutines in the numerical linear algebra. The parallelization of this task looks easy and straightforward, but it is not optimal in general case. This paper discuss some matrix-processor mappings and their impact on parallel spMV execution on massively parallel systems. We try to balance the performance and the overhead of the required transformation. We also present algorithms for redistribution. We propose four quality measures and derive lower and upper bound for different mappings. Our $spMV$ algorithms are scalable for almost all matrices arising from various technical areas. (en)
Title
  • The study of impact of matrix-processor mapping on the parallel sparse matrix-vector multiplication
  • The study of impact of matrix-processor mapping on the parallel sparse matrix-vector multiplication (en)
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  • The study of impact of matrix-processor mapping on the parallel sparse matrix-vector multiplication
  • The study of impact of matrix-processor mapping on the parallel sparse matrix-vector multiplication (en)
skos:notation
  • RIV/68407700:21240/14:00213823!RIV15-MSM-21240___
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  • 48284
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  • RIV/68407700:21240/14:00213823
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  • parallel execution; sparse matrix-vector multiplication; sparse matrix representation; matrix-processor mapping; scalability (en)
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  • [5E0EE7E30378]
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  • Temešvár
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  • Los Alamitos
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  • 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
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  • Langr, Daniel
  • Šimeček, Ivan
  • Srnec, E.
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  • 10.1109/SYNASC.2013.49
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  • IEEE Computer Society
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  • 978-1-4799-3035-7
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  • 21240
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