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

Frontiers in Genetics

Genetics

Jeremie Vandenplas¹  Jan Ten Napel¹  Torsten Pook¹  Alexander Freudenberg²  Martin Schlather²  Ross Evans³ 

[1] Animal Breeding and Genomics, Wageningen UR, Wageningen, Netherlands;Chair of Applied Stochastics, University of Mannheim, Mannheim, Germany;Irish Cattle Breeding Federation, Ballincollig, Ireland;

关键词: GPU;    SNP;    high-performance computing;    genomic data;    single-step model;    quantitative genomics;   

DOI  :  10.3389/fgene.2023.1220408

 received in 2023-05-10, accepted in 2023-08-03,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 Freudenberg, Vandenplas, Schlather, Pook, Evans and Ten Napel.