Use threads for within-chain parallelization in Stan via the brms
interface. Within-chain parallelization is experimental! We recommend its use
only if you are experienced with Stan's
reduce_sum function and have a
slow running model that cannot be sped up by any other means.
threading(threads = NULL, grainsize = NULL, static = FALSE)
Number of threads to use in within-chain parallelization.
Number of observations evaluated together in one chunk on
one of the CPUs used for threading. If
Logical. Apply the static (non-adaptive) version of
The adaptive scheduling procedure used by
prevent the results to be exactly reproducible even if you set the random
seed. If you need exact reproducibility, you have to set argument
static = TRUE which may reduce efficiency a bit.
To ensure that chunks (whose size is defined by
roughly the same amount of computing time, we recommend storing
observations in random order in the data. At least, please avoid sorting
observations after the response values. This is because the latter often
cause variations in the computing time of the pointwise log-likelihood,
which makes up a big part of the parallelized code.
brmsthreads object which can be passed to the
threads argument of
brm and related functions.
## Not run: # this model just serves as an illustration # threading may not actually speed things up here fit <- brm(count ~ zAge + zBase * Trt + (1|patient), data = epilepsy, family = negbinomial(), chains = 1, threads = threading(2, grainsize = 100), backend = "cmdstanr") summary(fit) ## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.