The code runs in my laptop (only two cores) but I still have to test its performance in my desktop (4 cores) and see if it really makes a difference in time versus running the analyses sequentially. # Run the bivariate example in parallel for two pairs of sitesįoreach(trial1=rep("FR38_1",2), trial2=c("FR38_2", "FR38_3")) %dopar% # Returns only log-Likelihood for this example # heterogeneous variances and diagonal matrix for heterogeneousĭen.bi = asreml(cbind(den1,den2) ~ trait, # Fits overall mean, random correlated additive effects with T2$den2 = ifelse(t2$Trial_id = trial2, t2$DEN, NA) T2$den1 = ifelse(t2$Trial_id = trial1, t2$DEN, NA) # (This one uses the same model for all sites # Wrap call to a generic bivariate analysis # Inverse of the numerator relationship matrix Ped = read.table('pedigree.txt', header = TRUE)
RegisterDoMC() # to start a "parallel backend"ĭat = read.table('density09.txt', header = TRUE) Require(asreml) # Multivariate mixed models
Short answer, I haven’t, but there is always a first time.
In another unrelated post, Kevin asked if I have ever considered using ASReml-R to run in parallel using a computer with multiple cores. This meant leaving the computer running for over a week.
The best next thing was to run over 2,000 bivariate analyses to build a large genetic correlation matrix (which has all sort of issues, I know). Besides requiring huge amounts of memory and being subject to all sort of estimation problems there were all sort of connectedness issues that precluded the use of Factor Analytic models to model the covariance matrix. If you think about it, with 70 sites and thousands of genotypes one is trying to fit a 70×70 additive genetic covariance matrix, which requires 70*69/2 = 2,415 covariance components. A while ago I wanted to run a quantitative genetic analysis where the performance of genotypes in each site was considered as a different trait.
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