02649naa a2200241 a 450000100080000000500110000800800410001902400310006010000150009124501710010626000090027750004490028652014280073565000270216365300290219065300160221965300180223565300200225370000160227370000160228970000160230577300860232110540072019-11-25       2015    bl uuuu     u00u1 u    #d7 a10.2527/jas.2015-93952DOI1 aMASUDA, Y.  aTechnical notebAcceleration of sparse operations for average-information REML analyses with supernodal methods and sparse-storage refinements.h[electronic resource]  c2015  aArticle history: Received June 8, 2015.; Accepted August 7, 2015. 1. We acknowledge the work by François Guillaume in programming a hash function. We greatly appreciate the work of the two anonymous reviewers. 2. The AIREMLF90 program with a sparse package YAMS, along with a manual, is available at http://nce.ads.uga.edu. The YAMS package is available on request for academic or noncommercial purposes by contacting the corresponding author.  aABSTRACT.  The objective of this study was to remove bottlenecks generally found in a computer program for average-information REML. The refinements included improvements to setting-up mixed-model equations on a hash table with a faster hash function as sparse matrix storage, changing sparse structures in calculation of traces, and replacing a sparse matrix package using traditional methods (FSPAK) with a new package using supernodal methods (YAMS); the latter package quickly processed sparse matrices containing large, dense blocks. Comparisons included 23 models with data sets from broiler, swine, beef, and dairy cattle. Models included single-trait, multiple-trait, maternal, and random regression models with phenotypic data; selected models used genomic information in a singlestep approach. Setting-up mixed model equations was completed without abnormal termination in all analyses. Calculations in traces were accelerated with a hash format, especially for models with a genomic relationship matrix, and the maximum speed was 67 times faster. Computations with YAMS were, on average, more than 10 times faster than with FSPAK and had greater advantages for large data and more complicated models including multiple traits, random regressions, and genomic effects. These refinements can be applied to general average-information REML programs.  © 2015 American Society of Animal Science. All rights reserved.  aMODELOS DE SIMULACIÓN  aAVERAGE-INFORMATION REML  aHASH FORMAT  aSPARSE MATRIX  aSPARSE PACKAGES1 aAGUILAR, I.1 aTSURUTA, S.1 aMISZTAL, I.  tJournal of Animal Science, 2015gv. 93, p. 4670 - 4674. Published October 9, 2015