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. © 2013 Springer-Verlag.
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CITATION STYLE
Kuznetsov, S. V. (2013). An approach of the QR factorization for tall-and-skinny matrices on multicore platforms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7782 LNCS, pp. 235–249). https://doi.org/10.1007/978-3-642-36803-5_17
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