pvec {parallel}  R Documentation 
Parallelize a Vector Map Function using Forking
Description
pvec
parallelizes the execution of a function on vector elements
by splitting the vector and submitting each part to one core. The
function must be a vectorized map, i.e. it takes a vector input and
creates a vector output of exactly the same length as the input which
doesn't depend on the partition of the vector.
It relies on forking and hence is not available on Windows unless
mc.cores = 1
.
Usage
pvec(v, FUN, ..., mc.set.seed = TRUE, mc.silent = FALSE,
mc.cores = getOption("mc.cores", 2L), mc.cleanup = TRUE)
Arguments
v 
vector to operate on 
FUN 
function to call on each part of the vector 
... 
any further arguments passed to 
mc.set.seed 
See 
mc.silent 
if set to 
mc.cores 
The number of cores to use, i.e. at most how many child processes will be run simultaneously. Must be at least one, and at least two for parallel operation. The option is initialized from environment variable MC_CORES if set. 
mc.cleanup 
See the description of this argument in

Details
pvec
parallelizes FUN(x, ...)
where FUN
is a
function that returns a vector of the same length as
x
. FUN
must also be pure (i.e., without sideeffects)
since sideeffects are not collected from the parallel processes. The
vector is split into nearly identically sized subvectors on which
FUN
is run. Although it is in principle possible to use
functions that are not necessarily maps, the interpretation would be
casespecific as the splitting is in theory arbitrary (a warning is
given in such cases).
The major difference between pvec
and mclapply
is
that mclapply
will run FUN
on each element separately
whereas pvec
assumes that c(FUN(x[1]), FUN(x[2]))
is
equivalent to FUN(x[1:2])
and thus will split into as many
calls to FUN
as there are cores (or elements, if fewer), each
handling a subset vector. This makes it more efficient than
mclapply
but requires the above assumption on FUN
.
If mc.cores == 1
this evaluates FUN(v, ...)
in the
current process.
Value
The result of the computation – in a successful case it should be of
the same length as v
. If an error occurred or the function was
not a map the result may be shorter or longer, and a warning is given.
Note
Due to the nature of the parallelization, error handling does not
follow the usual rules since errors will be returned as strings and
results from killed child processes will show up simply as
nonexistent data. Therefore it is the responsibility of the user to
check the length of the result to make sure it is of the correct size.
pvec
raises a warning if that is the case since it does not
know whether such an outcome is intentional or not.
See mcfork
for the inadvisability of using this with
GUI frontends and multithreaded libraries.
Author(s)
Simon Urbanek and R Core.
Derived from the multicore package formerly on CRAN.
See Also
mcparallel
, mclapply
,
parLapply
, clusterMap
.
Examples
x < pvec(1:1000, sqrt)
stopifnot(all(x == sqrt(1:1000)))
# One use is to convert date strings to unix time in large datasets
# as that is a relatively slow operation.
# So let's get some random dates first
# (A small test only with 2 cores: set options("mc.cores")
# and increase N for a largerscale test.)
N < 1e5
dates < sprintf('%04d%02d%02d', as.integer(2000+rnorm(N)),
as.integer(runif(N, 1, 12)), as.integer(runif(N, 1, 28)))
system.time(a < as.POSIXct(dates))
# But specifying the format is faster
system.time(a < as.POSIXct(dates, format = "%Y%m%d"))
# pvec ought to be faster, but system overhead can be high
system.time(b < pvec(dates, as.POSIXct, format = "%Y%m%d"))
stopifnot(all(a == b))
# using mclapply for this would much slower because each value
# will require a separate call to as.POSIXct()
# as lapply(dates, as.POSIXct) does
system.time(c < unlist(mclapply(dates, as.POSIXct, format = "%Y%m%d")))
stopifnot(all(a == c))