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