Maintaining high performance in the QR factorization while scaling both problem size and parallelism

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dc.contributor.advisorWhaley, Clint
dc.contributor.authorSamuel, Siju
dc.contributor.committeeMemberYi, Qing
dc.contributor.committeeMemberZhu, Dakai
dc.date.accessioned2024-02-12T20:03:00Z
dc.date.available2024-02-12T20:03:00Z
dc.date.issued2011
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractQR factorization is an extremely important linear algebra operation used in solving multiple linear equations, particularly least-square-error problems, and in finding eigenvalues and eigen-vectors. This thesis details the author's contributions to the field of computer science by providing performance-efficient QR routines to ATLAS (Automatically Tuned Linear Algebra Software). ATLAS is an open source linear algebra library, intended for high performance computing. The author has added new implementations for four types/precisions (single real, double real, single complex, and double complex) in four different variants of matrix factorization (QR, RQ, QL and LQ). QR factorization involves a panel factorization and a trailing matrix update operation. A statically blocked algorithm is used for the full matrix factorization. A recursive formulation is implemented for the QR panel factorization, providing more robust performance. Together these techniques result in substantial performance improvement over the LAPACK version.
dc.description.departmentComputer Science
dc.format.extent68 pages
dc.format.mimetypeapplication/pdf
dc.identifier.isbn9781124628875
dc.identifier.urihttps://hdl.handle.net/20.500.12588/5431
dc.languageen
dc.subjectATLAS Software
dc.subjectLinear Algebra Software
dc.subjectLQ Factorization
dc.subjectParallel Computing
dc.subjectQR Factorization
dc.subject.classificationComputer science
dc.titleMaintaining high performance in the QR factorization while scaling both problem size and parallelism
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentComputer Science
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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