| parallel {multicore} | R Documentation |
parallel starts a parallel process which evaluates the given expression.
mcparallel is a synonym for parallel that can be used
at top level if parallel is masked by other packages. It
should not be used in other packages since it's just a shortcut for
importing multicore::parallel.
collect collects results from parallel processes.
parallel(expr, name, mc.set.seed = FALSE, silent = FALSE) mcparallel(expr, name, mc.set.seed = FALSE, silent = FALSE) collect(jobs, wait = TRUE, timeout = 0, intermediate = FALSE)
expr |
expression to evaluate (do not use any on-screen devices or GUI elements in this code) |
name |
an optional name (character vector of length one) that can be associated with the job. |
mc.set.seed |
if set to TRUE then the random number
generator is seeded such that it is different from any other
process. Otherwise it will be the same as in the current R session. |
silent |
if set to TRUE then all output on stdout will be
suppressed (stderr is not affected). |
jobs |
list of jobs (or a single job) to collect results
for. Alternatively jobs can also be an integer vector of
process IDs. If omitted collect will wait for all currently
existing children. |
wait |
if set to FALSE it checks for any results that are
available within timeout seconds from now, otherwise it waits
for all specified jobs to finish. |
timeout |
timeout (in seconds) to check for job results - applies
only if wait is FALSE. |
intermediate |
FALSE or a function which will be called while
collect waits for results. The function will be called with one
parameter which is the list of results received so far. |
parallel evaluates the expr expression in parallel to
the current R process. Everything is shared read-only (or in fact
copy-on-write) between the parallel process and the current process,
i.e. no side-effects of the expression affect the main process. The
result of the parallel execution can be collected using
collect function.
collect function collects any available results from parallel
jobs (or in fact any child process). If wait is TRUE
then collect waits for all specified jobs to finish before
returning a list containing the last reported result for each
job. If wait is FALSE then collect merely
checks for any results available at the moment and will not wait for
jobs to finish. If jobs is specified, jobs not listed there
will not be affected or acted upon.
Note: If expr uses low-level multicore functions such
as sendMaster a single job can deliver results
multiple times and it is the responsibility of the user to interpret
them correctly. collect will return NULL for a
terminating job that has sent its results already after which the
job is no longer available.
parallel returns an object of the class parallelJob
which is in turn a childProcess.
collect returns any results that are available in a list. The
results will have the same order as the specified jobs. If there are
multiple jobs and a job has a name it will be used to name the
result, otherwise its process ID will be used.
Simon Urbanek
p <- parallel(1:10) q <- parallel(1:20) collect(list(p, q)) # wait for jobs to finish and collect all results p <- parallel(1:10) collect(p, wait=FALSE, 10) # will retrieve the result (since it's fast) collect(p, wait=FALSE) # will signal the job as terminating collect(p, wait=FALSE) # there is no such job # a naive parallelized lapply can be created using parallel alone: jobs <- lapply(1:10, function(x) parallel(rnorm(x), name=x)) collect(jobs)