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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1220408/full |
| _version_ | 1797741370142621696 |
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| author | Alexander Freudenberg Jeremie Vandenplas Martin Schlather Torsten Pook Ross Evans Jan Ten Napel |
| author_facet | Alexander Freudenberg Jeremie Vandenplas Martin Schlather Torsten Pook Ross Evans Jan Ten Napel |
| author_sort | Alexander Freudenberg |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-12T14:26:47Z |
| format | Article |
| id | doaj.art-e6092cfa555147debdfb86e45e4ae2bc |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-03-12T14:26:47Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-e6092cfa555147debdfb86e45e4ae2bc2023-08-18T07:29:28ZengFrontiers Media S.A.Frontiers in Genetics1664-80212023-08-011410.3389/fgene.2023.12204081220408Accelerated matrix-vector multiplications for matrices involving genotype covariates with applications in genomic predictionAlexander Freudenberg0Jeremie Vandenplas1Martin Schlather2Torsten Pook3Ross Evans4Jan Ten Napel5Chair of Applied Stochastics, University of Mannheim, Mannheim, GermanyAnimal Breeding and Genomics, Wageningen UR, Wageningen, NetherlandsChair of Applied Stochastics, University of Mannheim, Mannheim, GermanyAnimal Breeding and Genomics, Wageningen UR, Wageningen, NetherlandsIrish Cattle Breeding Federation, Ballincollig, IrelandAnimal Breeding and Genomics, Wageningen UR, Wageningen, NetherlandsIn 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.https://www.frontiersin.org/articles/10.3389/fgene.2023.1220408/fullGPUSNPhigh-performance computinggenomic datasingle-step modelquantitative genomics |
| spellingShingle | Alexander Freudenberg Jeremie Vandenplas Martin Schlather Torsten Pook Ross Evans Jan Ten Napel Accelerated matrix-vector multiplications for matrices involving genotype covariates with applications in genomic prediction Frontiers in Genetics GPU SNP high-performance computing genomic data single-step model quantitative genomics |
| 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 | GPU SNP high-performance computing genomic data single-step model quantitative genomics |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2023.1220408/full |
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