Fecha/hora: 28/04/2024 23:52:30

Biblioteca(s): 	INIA Las Brujas.	
Fecha corriente: 	31/03/2021	
Fecha de la última actualización: 	31/03/2021	
Autoria: 	MASUDA, Y.; AGUILAR, I.; TSURUTA, S.; MISZTAL, I.	
Afiliación: 	YATUKA MASUDA, Obihiro University of Agriculture and Veterinary Medicine, Obihiro, Japan; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SHOGO TSURUTA; IGNACY MISZTAL, University of Georgia, Athens, USA.	
Título: 	Acceleration of computations in AI REML for single-step GBLUP models.	
Complemento del título: 	Volume Methods and Tools: Statistical methods - linear and nonlinear models (Posters), 703.	
Año de publicación: 	2014	
Fuente/Imprenta: 	In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.703.	
Idioma: 	Inglés	
Contenido: 	ABSTRACT.
The objective of this study was to evaluate the advantage of the YAMS package over the FSPAK package in average-information (AI) REML for single-step GBLUP models. Data sets from broiler and Holsteins were used in this study. (Co)variance components were estimated with the AIREMLF90 program which could switch YAMS and FSPAK for sparse operations. The YAMS package used the BLAS and LAPACK libraries using all the 16 cores on CPU. For a single-trait model applied to the data contained over 15,000 genotyped animals, FSPAK took over 4 hours to finish the first 5 rounds while YAMS took 20 minutes. For a 4-trait model applied to the same data set, FSPAK failed in the sparse factorization while YAMS took 5 hours to finish the first 5 rounds. The use of YAMS can dramatically increase speed and stability of AIREMLF90 for single-step GBLUP models.	
Palabras-Clave: 	Single step GBLUP; Supernodal methods; Variance component estimation.	
Categoría del tema: 	L10 Genética y mejoramiento animal	
URL: 	http://www.ainfo.inia.uy/digital/bitstream/item/15447/1/Masuda-et-al.-2014.-WCGALP.pdf	
Registro original: 	INIA Las Brujas (LB)	
Ejemplares: 	LB - (102602-1) - PXI - PC - DD - WCGALP/10/2014 -  -  - DIS