期刊
JOURNAL OF COMPUTATIONAL SCIENCE
卷 22, 期 -, 页码 99-108出版社
ELSEVIER
DOI: 10.1016/j.jocs.2017.08.013
关键词
Selected inversion; Distributed-memory computation; Parallel computation; Schur-complement decomposition; Genomic prediction
资金
- Swiss National Science Foundation [149454]
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. (C) 2017 Elsevier B.V. All rights reserved.
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