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A survey of sparse matrix-vector multiplication performance on large matrices (1608.00636v1)

Published 1 Aug 2016 in cs.PF, cs.DC, and math.NA

Abstract: We contribute a third-party survey of sparse matrix-vector (SpMV) product performance on industrial-strength, large matrices using: (1) The SpMV implementations in Intel MKL, the Trilinos project (Tpetra subpackage), the CUSPARSE library, and the CUSP library, each running on modern architectures. (2) NVIDIA GPUs and Intel multi-core CPUs (supported by each software package). (3) The CSR, BSR, COO, HYB, and ELL matrix formats (supported by each software package).

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Authors (6)
  1. Max Grossman (2 papers)
  2. Christopher Thiele (3 papers)
  3. Mauricio Araya-Polo (26 papers)
  4. Florian Frank (28 papers)
  5. Faruk O. Alpak (7 papers)
  6. Vivek Sarkar (16 papers)
Citations (28)

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