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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
12/10/2015 |
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Actualizado : |
12/10/2015 |
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Autor : |
HELLMUTH, M.E.; MASON, S.J.; VAUGHAN, C.; VAN AALST, M.K.; CHOULARTON, R. (Ed.). |
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Título : |
A better climate for disaster risk management. |
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Fecha de publicación : |
2011 |
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Fuente / Imprenta : |
Palisades, NY: IRI, 2011. |
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Páginas : |
118 p. |
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Serie : |
(Climate and Society, 3). |
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ISBN : |
978-0-9729252-7-3. |
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Idioma : |
Inglés |
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Palabras claves : |
MANEJO DE RIESGOS; PRONÓSTICOS METEOROLÓGICOS. |
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Thesagro : |
CAMBIO CLIMÁTICO. |
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Asunto categoría : |
P40 Meteorología y climatología |
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Marc : |
LEADER 00554nam a2200205 a 4500
001 1053589
005 2015-10-12
008 2011 bl uuuu u00u1 u #d
100 1 $aHELLMUTH, M.E.
245 $aA better climate for disaster risk management.
260 $aPalisades, NY: IRI$c2011
300 $a118 p.
490 $a(Climate and Society, 3).
650 $aCAMBIO CLIMÁTICO
653 $aMANEJO DE RIESGOS
653 $aPRONÓSTICOS METEOROLÓGICOS
700 1 $aMASON, S.J.
700 1 $aVAUGHAN, C.
700 1 $aVAN AALST, M.K.
700 1 $aCHOULARTON, R.
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Registro original : |
INIA La Estanzuela (LE) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
31/03/2021 |
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Actualizado : |
31/03/2021 |
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Tipo de producción científica : |
Trabajos en Congresos/Conferencias |
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Autor : |
MASUDA, Y.; AGUILAR, I.; TSURUTA, S.; MISZTAL, I. |
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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. |
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Título : |
Acceleration of computations in AI REML for single-step GBLUP models. |
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Complemento del título : |
Volume Methods and Tools: Statistical methods - linear and nonlinear models (Posters), 703. |
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Fecha de publicación : |
2014 |
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Fuente / Imprenta : |
In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.703. |
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Idioma : |
Inglés |
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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. |
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Palabras claves : |
Single step GBLUP; Supernodal methods; Variance component estimation. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/15447/1/Masuda-et-al.-2014.-WCGALP.pdf
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Marc : |
LEADER 01492nam a2200181 a 4500
001 1061923
005 2021-03-31
008 2014 bl uuuu u01u1 u #d
100 1 $aMASUDA, Y.
245 $aAcceleration of computations in AI REML for single-step GBLUP models.$h[electronic resource]
260 $aIn: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.703.$c2014
520 $aABSTRACT. 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.
653 $aSingle step GBLUP
653 $aSupernodal methods
653 $aVariance component estimation
700 1 $aAGUILAR, I.
700 1 $aTSURUTA, S.
700 1 $aMISZTAL, I.
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INIA Las Brujas (LB) |
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