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`grmsem` should be run in parallel by setting the `cores` parameter of `gsem.fit()`. Empirically, good performance was achieved by setting `cores=4`, while sharing memory across as many cores as possible. For this, the entire node should be blocked so that memory across all cores is available for the job (depending on the system ranging usually between 8-24 cores). For example, the fit of a Cholesky model (12 parameters) to simulated trivariate data with 5000 observations per trait and 20,000 SNPs per genetic factor, requires 1h40min using 4 cores, sharing memory across 24 cores with vmem max of 6.9 Gb, using R MKL 3.6.3.
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## Data sets
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All data sets used in the vignette and this wiki can be downloaded from here: `https://gitlab.gwdg.de/beate.stpourcain/grmsem_external`.
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All data sets used in the vignette and this wiki can be downloaded from here:
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* `https://gitlab.gwdg.de/beate.stpourcain/grmsem_external`.
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Note that small data sets are already included in the package.
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# Input files
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