On multi-core machines your analyses can be divided into smaller parts, all running simultaneously, enabling you to achieve much faster results. ASReml 4.2 has been optimised for multi-threaded processing, enabling you to use up to 16 threads. This delivers significant gains in processing speed and on occasion can be up to 70x faster. (These gains depend upon a variety of factors including: microprocessor, machine power, dataset size and type of analysis, among others. For example, in ASReml 4.1 an 18,144 record MET trial took over 21 hours to process in a machine with 16 GB RAM and a CPU Intel® Core™ i7-3770K. ASReml 4.2 ran the same job in 2.9 hours. Using 8-thread multiprocessing the time was reduced again to 26 minutes: an almost 50x increase in speed.)
Pedigree trimming / absorbing parents
Pedigree trimming and the absorbing of parents without data can save computation time. This pedigree pre-processing removes unnecessary individuals from a pedigree, speeding up likelihood evaluation while maintaining proper relationships among the core members.
Memory access (workspace)
Memory access has been increased to a maximum of 96 GB, enabling you to analyse larger problems in less time. On multi-user systems, memory efficiency is maximised by allowing each user to specify the amount of memory needed for their current session. (The maximum allocation depends on what is available on your PC. We recommend that the workspace requested does not exceed the RAM available on your machine.)
Optimisation of internal routines
Many of ASReml 4.2s core routines have been re-worked, making it much faster than the previous version. In jobs with relatively dense G matrices computation times are often reduced by more than 40%.
Fitting bivariate GLM(M) models
Previous versions of ASReml allowed a bivariate analysis of a binomially distributed variate and a linear model variate. ASReml 4.2 has extended the bivariate analysis of generalized linear models. Both variates are now allowed to be distributed with Normal, Binomial, Poisson, Gamma or Negative Binomial distributions.