Enable parallel computing provided by 'future' package within the context

Usage

with_future_parallel(
  expr,
  env = parent.frame(),
  quoted = FALSE,
  on_failure = "multisession",
  max_workers = NA,
  ...
)

Arguments

expr: the expression to be evaluated
env: environment of the expr
quoted: whether expr has been quoted; default is false
on_failure: alternative 'future' plan to use if forking a process is disallowed; this usually occurs on 'Windows' machines; see details.
max_workers: maximum of workers; default is automatically set by raveio_getopt("max_worker",1L)
...: additional parameters passing into make_forked_clusters

Value

The evaluation results of expr

Details

Some 'RAVE' functions such as prepare_subject_power support parallel computing to speed up. However, the parallel computing is optional. You can enable it by wrapping the function calls within with_future_parallel (see examples).

The default plan is to use 'forked' R sessions. This is a convenient, fast, and relative simple way to create multiple R processes that share the same memories. However, on some machines such as 'Windows' the support has not yet been implemented. In such cases, the plan fall backs to a back-up specified by on_failure. By default, on_failure is 'multisession', a heavier implementation than forking the process, and slightly longer ramp-up time. However, the difference should be marginal for most of the functions.

When parallel computing is enabled, the number of parallel workers is specified by the option raveio_getopt("max_worker", 1L).

Examples


library(raveio)

demo_subject <- as_rave_subject("demo/DemoSubject", strict = FALSE)

if(dir.exists(demo_subject$path)) {
  with_future_parallel({
    prepare_subject_power("demo/DemoSubject")
  })
}