Learn R Programming

xcms (version 1.48.0)

xcmsPapply: xcmsPapply

Description

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.

Usage

xcmsPapply(arg_sets, papply_action, papply_commondata = list(), show_errors = TRUE, do_trace = FALSE, also_trace = c())

Arguments

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.

Value

A list of return values from papply\_action. Each value corresponds to the element of arg\_sets used as a parameter to papply\_action

Details

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.

Make sure Rmpi was installed properly by executing the example below. Rmpi was tested with

References

http://ace.acadiau.ca/math/ACMMaC/software/papply/

Examples

Run this code
## 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)

Run the code above in your browser using DataLab