License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagSemProc.09061.13
URN: urn:nbn:de:0030-drops-20781
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2009/2078/
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Davis, Timothy

Multifrontral multithreaded rank-revealing sparse QR factorization

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09061.DavisTim.ExtAbstract.2078.pdf (0.08 MB)

Abstract

SuiteSparseQR is a sparse multifrontal QR factorization algorithm.
Dense matrix methods within each frontal matrix enable
the method to obtain high performance on multicore architectures. Parallelism
across different frontal matrices is handled with Intel's Threading Building
Blocks library.
Rank-detection is performed within each
frontal matrix using Heath's method, which does not require column pivoting.
The resulting sparse QR factorization obtains a substantial fraction of the
theoretical peak performance of a multicore computer.

BibTeX - Entry

@InProceedings{davis:DagSemProc.09061.13,
  author =	{Davis, Timothy},
  title =	{{Multifrontral multithreaded rank-revealing sparse QR factorization}},
  booktitle =	{Combinatorial Scientific Computing},
  pages =	{1--3},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9061},
  editor =	{Uwe Naumann and Olaf Schenk and Horst D. Simon and Sivan Toledo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2009/2078},
  URN =		{urn:nbn:de:0030-drops-20781},
  doi =		{10.4230/DagSemProc.09061.13},
  annote =	{Keywords: Sparse matrix algorithms, QR factorization, multifrontal}
}

Keywords: Sparse matrix algorithms, QR factorization, multifrontal
Collection: 09061 - Combinatorial Scientific Computing
Issue Date: 2009
Date of publication: 24.07.2009

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