Enhancing the Scalability of Selected Inversion Factorization Algorithms in Genomic Prediction

Verbosio, Fabio; Coninck, Arne De; Kourounis, Drosos; Schenk, Olaf
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.
Year:
2017
Type of Publication:
Article
Journal:
Elsevier Journal of Computational Science
Pages:
-
Month:
September
ISSN:
1877-7503
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