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Accelerated matrix-vector multiplications for matrices involving genotype covariates with applications in genomic prediction

In the last decade, a number of methods have been suggested to deal with large amounts of genetic data in genomic predictions. Yet, steadily growing population sizes and the suboptimal use of computational resources are pushing the practical application of these approaches to their limits. As an ext...

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Autores principales: Freudenberg, Alexander, Vandenplas, Jeremie, Schlather, Martin, Pook, Torsten, Evans, Ross, Ten Napel, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10470110/
https://www.ncbi.nlm.nih.gov/pubmed/37662837
http://dx.doi.org/10.3389/fgene.2023.1220408
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author Freudenberg, Alexander
Vandenplas, Jeremie
Schlather, Martin
Pook, Torsten
Evans, Ross
Ten Napel, Jan
author_facet Freudenberg, Alexander
Vandenplas, Jeremie
Schlather, Martin
Pook, Torsten
Evans, Ross
Ten Napel, Jan
author_sort Freudenberg, Alexander
collection PubMed
description In the last decade, a number of methods have been suggested to deal with large amounts of genetic data in genomic predictions. Yet, steadily growing population sizes and the suboptimal use of computational resources are pushing the practical application of these approaches to their limits. As an extension to the C/CUDA library miraculix, we have developed tailored solutions for the computation of genotype matrix multiplications which is a critical bottleneck in the empirical evaluation of many statistical models. We demonstrate the benefits of our solutions at the example of single-step models which make repeated use of this kind of multiplication. Targeting modern Nvidia(®) GPUs as well as a broad range of CPU architectures, our implementation significantly reduces the time required for the estimation of breeding values in large population sizes. miraculix is released under the Apache 2.0 license and is freely available at https://github.com/alexfreudenberg/miraculix.
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spelling pubmed-104701102023-09-01 Accelerated matrix-vector multiplications for matrices involving genotype covariates with applications in genomic prediction Freudenberg, Alexander Vandenplas, Jeremie Schlather, Martin Pook, Torsten Evans, Ross Ten Napel, Jan Front Genet Genetics In the last decade, a number of methods have been suggested to deal with large amounts of genetic data in genomic predictions. Yet, steadily growing population sizes and the suboptimal use of computational resources are pushing the practical application of these approaches to their limits. As an extension to the C/CUDA library miraculix, we have developed tailored solutions for the computation of genotype matrix multiplications which is a critical bottleneck in the empirical evaluation of many statistical models. We demonstrate the benefits of our solutions at the example of single-step models which make repeated use of this kind of multiplication. Targeting modern Nvidia(®) GPUs as well as a broad range of CPU architectures, our implementation significantly reduces the time required for the estimation of breeding values in large population sizes. miraculix is released under the Apache 2.0 license and is freely available at https://github.com/alexfreudenberg/miraculix. Frontiers Media S.A. 2023-08-17 /pmc/articles/PMC10470110/ /pubmed/37662837 http://dx.doi.org/10.3389/fgene.2023.1220408 Text en Copyright © 2023 Freudenberg, Vandenplas, Schlather, Pook, Evans and Ten Napel. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Freudenberg, Alexander
Vandenplas, Jeremie
Schlather, Martin
Pook, Torsten
Evans, Ross
Ten Napel, Jan
Accelerated matrix-vector multiplications for matrices involving genotype covariates with applications in genomic prediction
title Accelerated matrix-vector multiplications for matrices involving genotype covariates with applications in genomic prediction
title_full Accelerated matrix-vector multiplications for matrices involving genotype covariates with applications in genomic prediction
title_fullStr Accelerated matrix-vector multiplications for matrices involving genotype covariates with applications in genomic prediction
title_full_unstemmed Accelerated matrix-vector multiplications for matrices involving genotype covariates with applications in genomic prediction
title_short Accelerated matrix-vector multiplications for matrices involving genotype covariates with applications in genomic prediction
title_sort accelerated matrix-vector multiplications for matrices involving genotype covariates with applications in genomic prediction
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10470110/
https://www.ncbi.nlm.nih.gov/pubmed/37662837
http://dx.doi.org/10.3389/fgene.2023.1220408
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