xcmsPapply | R Documentation |
This function is deprecated, use bplapply
instead.
An apply-like function which uses Rmpi to distribute the processing evenly across a cluster. Will use a non-MPI version if distributed processing is not available.
xcmsPapply(arg_sets, papply_action, papply_commondata = list(),
show_errors = TRUE, do_trace = FALSE, also_trace = c())
arg_sets |
a list, where each item will be given as an argument to papply\_action |
papply_action |
A function which takes one argument. It will be called on each element of arg\_sets |
papply_commondata |
A list containing the names and values of variables to be accessible to the papply\_action. 'attach' is used locally to import this list. |
show_errors |
If set to TRUE, overrides Rmpi's default, and messages for errors which occur in R slaves are produced. |
do_trace |
If set to TRUE, causes the papply\_action function to be traced. i.e. Each statement is output before it is executed by the slaves. |
also_trace |
If supplied an array of function names, as strings, tracing will also occur for the specified functions. |
Similar to apply and lapply, applies a function to all items of a list, and returns a list with the corresponding results.
Uses Rmpi to implement a pull idiom in order to distribute the processing evenly across a cluster. If Rmpi is not available, or there are no slaves, implements this as a non-parallel algorithm.
xcmsPapply
is a modified version of the papply function from package papply 0.2 (Duane Currie).
Parts of the slave function were wrapped in try() to make it failsafe and progress output was added.
A list of return values from papply\_action. Each value corresponds to the element of arg\_sets used as a parameter to papply\_action
Does not support distributing recursive calls in parallel. If papply is used inside papply\_action, it will call a non-parallel version
Duane Currie <duane.currie@acadiau.ca>, modified by Ralf Tautenhahn <rtautenh@ipb-halle.de>.
## Not run:
library(Rmpi)
library(xcms)
number_lists <- list(1:10,4:40,2:27)
mpi.spawn.Rslaves(nslaves=2)
results <- xcmsPapply(number_lists,sum)
results
mpi.close.Rslaves()
## End(Not run)
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