Enhancing the Scalability of Selected Inversion Factorization Algorithms in Genomic Prediction
Additional information
Authors
Verbosio F.,
De Coninck A.,
Kourounis D.,
Schenk O.
Type
Journal Article
Year
2017
Language
English
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.
Keywords
Distributed-memory computation, genomic prediction, Selected inversion
Journal
Elsevier Journal of Computational Science
Number ( Month )
September
ISSN
1877-7503
