Abstract
In this paper we focus primarily on a technique used to parallelize the LAPACK QR factorization of tall-and-skinny matrices. The modifications of the panel QR factorization we suggest neither affect the accuracy nor increase memory consumption. Results for tall-and-skinny matrices on the Intel® Xeon® platforms, and comparisons between the Intel® Math Kernel Library (Intel MKL) QR, PLASMA QR and the method proposed are provided.
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Kuznetsov, S.V. (2013). An Approach of the QR Factorization for Tall-and-Skinny Matrices on Multicore Platforms. In: Manninen, P., Öster, P. (eds) Applied Parallel and Scientific Computing. PARA 2012. Lecture Notes in Computer Science, vol 7782. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36803-5_17
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DOI: https://doi.org/10.1007/978-3-642-36803-5_17
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