Abstract
The innovation of this work is a simple vectorizable algorithm for performing sparse matrix vector multiply in compressed sparse row (CSR) storage format. Unlike the vectorizable jagged diagonal format (JAD), this algorithm requires no data rearrangement and can be easily adapted to a sophisticated library framework such as PETSc. Numerical experiments on the Cray X1 show an order of magnitude improvement over the non-vectorized algorithm.
This Research sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory (ORNL). This research used resources of the Center for Computational Sciences at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.
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D’Azevedo, E.F., Fahey, M.R., Mills, R.T. (2005). Vectorized Sparse Matrix Multiply for Compressed Row Storage Format. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J.J. (eds) Computational Science – ICCS 2005. ICCS 2005. Lecture Notes in Computer Science, vol 3514. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11428831_13
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DOI: https://doi.org/10.1007/11428831_13
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