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Parallel implementation of the Sherman–Morrison matrix inverse algorithm
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
2013 (English)In: Applied Parallel and Scientific Computing, Berlin: Springer-Verlag , 2013, 206-219 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Berlin: Springer-Verlag , 2013. 206-219 p.
Series
Lecture Notes in Computer Science, 7782
National Category
Computer Science Computational Mathematics
Identifiers
URN: urn:nbn:se:uu:diva-198520DOI: 10.1007/978-3-642-36803-5_15ISI: 000343867800015ISBN: 978-3-642-36802-8 (print)OAI: oai:DiVA.org:uu-198520DiVA: diva2:616633
Conference
PARA 2012: State of the Art in Scientific and Parallel Computing
Projects
UPMARCeSSENCE
Available from: 2013-02-18 Created: 2013-04-17 Last updated: 2014-12-12Bibliographically approved
In thesis
1. Scientific computing on hybrid architectures
Open this publication in new window or tab >>Scientific computing on hybrid architectures
2013 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Modern computer architectures, with multicore CPUs and GPUs or other accelerators, make stronger demands than ever on writers of scientific code. As a rule of thumb, the fastest, most efficient program consists of labor-intensive code written by expert programmers for a certain application on a particular computer. This thesis deals with several algorithmic and technical approaches towards effectively satisfying the demand for high-performance parallel programming without incurring such a high cost in expert programmer time. Effective programming is accomplished by writing performance-portable code where performance-critical functionality is provided either by external software or at least a balance between maintainability/generality and efficiency.

Place, publisher, year, edition, pages
Uppsala University, 2013
Series
Information technology licentiate theses: Licentiate theses from the Department of Information Technology, ISSN 1404-5117 ; 2013-002
National Category
Computer Science Computational Mathematics
Research subject
Scientific Computing
Identifiers
urn:nbn:se:uu:diva-200242 (URN)
Supervisors
Projects
UPMARCeSSENCE
Available from: 2013-05-31 Created: 2013-05-23 Last updated: 2017-08-31Bibliographically approved

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He, XinHolm, MarcusNeytcheva, Maya

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