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Enhancing the Scalability of Selected Inversion Factorization Algorithms in Genomic Prediction

Informazioni aggiuntive

Autori
Verbosio F., De Coninck A., Kourounis D., Schenk O.
Tipo
Articolo pubblicato in rivista scientifica
Anno
2017
Lingua
Inglese
Abstract
A parallel distributed-memory approach for the exact calculation of selected entries of the inverse of a matrix arising in a Best Linear Unbiased Estimation (BLUE) problem in Genomic Prediction is presented. The particular structure of the matrices involved in this stochastic process, consisting of sparse and dense blocks, requires a framework coupling sparse and dense linear algebra algorithms. Our approach exploits direct sparse techniques based on the Takahashi equations, coupled with distributed LU dense factorizations and Schur-complement computations. The algorithm is validated on several matrices on a Cray XC40 supercomputer.
Rivista
Elsevier Journal of Computational Science
Mese
settembre
ISSN
1877-7503
Parole chiave
Selected inversion; Distributed-memory computation; genomic prediction;