Abstract
Sparse matrix vector multiplication is one of the most often used functions in scientific and engineering computing. Though, various storage schemes for sparse matrices have been proposed, the optimal storage scheme is dependent upon the matrix being stored. In this paper, we will propose an auto-selecting algorithm for sparse matrix vector multiplication on GPUs that automatically selects the optimal storage scheme. We evaluated our algorithm using a solver for systems of linear equations. As a result, we found that our algorithm was effective for many sparse matrices.
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Kubota, Y., Takahashi, D. (2011). Optimization of Sparse Matrix-Vector Multiplication by Auto Selecting Storage Schemes on GPU. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds) Computational Science and Its Applications - ICCSA 2011. ICCSA 2011. Lecture Notes in Computer Science, vol 6783. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21887-3_42
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DOI: https://doi.org/10.1007/978-3-642-21887-3_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21886-6
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