#
#
### Simple example of parallel processing.
#
# The example below simply illustrates how to use multiple cores on a
# a desktop computer -- to increase the speed of time consuming
# iterated analysis.
#
# An article offering more explanation can be found here:
# http://researchsupport.unt.edu/class/Jon/Benchmarks/Parallel_L_JDS_Mar2014.pdf
#
# First, write a 'slow' function: keep in mind, we are intentionally
# making this slow so that we can show an improvement from the use
# of parallel processing (i.e. using multiple cores rather than a
# single processor which is the default).
lockpick.fun <- function(combination, choices){
ch <- seq(1:choices)
solution.matrix <- expand.grid(ch, ch, ch)
i <- 0
picked <- "FALSE"
while(picked == "FALSE"){
i <- i + 1
draw <- solution.matrix[i,]
if(combination[1] == draw[1] & combination[2] == draw[2] &
combination[3] == draw[3]){
picked <- "TRUE"; print("PICKED!!")}
}
out <- paste("Number of iterations =", i, sep = " ")
return(out)
}
# Next, set the number of possible values for each of the lock's combination
# spaces.
choi <- 50; choi
# Next, randomly draw a combination -- which 'opens' the lock.
combin <- sample(seq(1:choi), 3, replace = T); combin
# Next, apply the function from above to find our combination
# using 'brute force'.
system.time(test.1 <- lockpick.fun(combination = combin, choices = choi))
test.1
rm(test.1)
# Comparison Test:
library(doParallel)
# Next, register two cores -- obviously more cores can be registered if
# the machine has more than two cores; however, it may be important to
# leave one core not registered so that the user can continue to do
# other tasks while R is processing an analysis.
registerDoParallel(cores = 2)
# For the comparison, we're going to run the same application (lockpick) as
# we did above, but we're going to run it three times for each of
# two tests -- the first test (baseline = b) using a simple for-loop and
# the second test (test = t) using the foreach multi-core method.
# First, run the baseline for comparision (without using multiple cores).
b.results <- as.list(0)
b.time <- system.time(for (i in 1:3){
b.results[[i]] <- lockpick.fun(combination = combin, choices = choi)
})[3]
b.results
b.time
# Next, run the testing function(s).
t.time <- system.time(t.results <- foreach(i = 1:3) %dopar%
lockpick.fun(combination = combin, choices = choi))[3]
t.results
t.time
# To estimate the percentage decrease in time elapsed....
(b.time - t.time)/b.time
# A second way of doing the baseline version....using only one core.
s.time <- system.time(s.results <- foreach(i = 1:3) %do%
lockpick.fun(combination = combin, choices = choi))[3]
s.results
s.time
# Notice below, the 's.time' and 'b.time' are virtually identical.
combin
b.time
s.time
t.time
# Finally, clean up the workspace.
rm(b.results, b.time, choi, combin, i, lockpick.fun, s.results, s.time,
t.results, t.time)
detach("package:doParallel")
detach("package:foreach")
detach("package:iterators")
detach("package:parallel")
#
# References & Resources:
#
# Eddelbuettel, D. (2013). CRAN Task View: High-Performance and Parallel Computing with R. Available at: http://cran.r-project.org/web/views/HighPerformanceComputing.html
#
# Hornik, K. (2014). R FAQ (section 5.1.1 R Add-On Packages): List of packages which are included with a the R distribution. Available at: http://cran.r-project.org/faqs.html
#
# Brian Ripley, Luke Tierney and Simon Urbanek. (2014). Package parallel. Manual available at: http://stat.ethz.ch/R-manual/R-devel/library/parallel/doc/parallel.pdf
#
# Weston, S., [Revolution Analytics]. (2014). Package doParallel. Documentation available at CRAN: http://cran.r-project.org/web/packages/doParallel/index.html
#
# Weston, S., [Revolution Analytics]. (2013). Package foreach. Documentation available at CRAN: http://cran.r-project.org/web/packages/foreach/index.html
# End script.