**Super Nerdy Cool » R**, and kindly contributed to R-bloggers)

I finally have time to try parallel computing in R using snowfall/snow thanks to this article in the 1st issue of R journal, which replaces R news. I didn’t try it before because i didn’t have a good toy example, and it *seemed* like a steep learning curve (i only guessed what parallel computing was). So snow/snowfall works when what you want to do is ‘embarrassingly parallel,’ eg, a simulation study, bootstrap, or a cross-validation. I do simulation studies a lot, eg, assessing the properties of a statistical methodology, so implementing parallel computing will be very useful.

I got the toy example to work, but it was parallel on a single computer with multiple cores. Thanks to Michael Zeller, I got it to work on multiple machines. If we use multiple nodes, make sure we enable passwordless ssh.

Credit for getting snowfall to work on the BDUC servers (uci-nacs) goes to Harry Mangalam.

Here is a script, with a few examples:

## Example 1 - Multi-core on a single computer sink('SnowFallExample.Rout', split=TRUE) .Platform .Machine R.version Sys.info() library(snowfall) # 1. Initialisation of snowfall. # (if used with sfCluster, just call sfInit()) sfInit(parallel=TRUE, cpus=2) # 2. Loading data. require(mvna) data(sir.adm) # 3. Wrapper, which can be parallelised. wrapper <- function(idx) { # Output progress in worker logfile cat( "Current index: ", idx, "\n" ) index <- sample(1:nrow(sir.adm), replace=TRUE) temp <- sir.adm[index, ] fit <- crr(temp$time, temp$status, temp$pneu) return(fit$coef) } # 4. Exporting needed data and loading required # packages on workers. sfExport("sir.adm") sfLibrary(cmprsk) # 5. Start network random number generator # (as "sample" is using random numbers). sfClusterSetupRNG() # 6. Distribute calculation start <- Sys.time(); result <- sfLapply(1:1000, wrapper) ; Sys.time()-start # Result is always in list form. mean(unlist(result)) # 7. Stop snowfall sfStop() ## Example 2 - Multiple nodes on a cluster (namely, the family-guy cluster at uci-ics) sink('SnowFallExample.Rout', split=TRUE) .Platform .Machine R.version Sys.info() library(snowfall) # 1. Initialisation of snowfall. # (if used with sfCluster, just call sfInit()) sfInit(socketHosts=rep(c('peter-griffin.ics.uci.edu','stewie-griffin.ics.uci.edu', 'neil-goldman.ics.uci.edu', 'mort-goldman.ics.uci.edu','lois-griffin.ics.uci.edu'),each=2), cpus=10,type='SOCK',parallel=T) # 2. Loading data. require(mvna) data(sir.adm) # 3. Wrapper, which can be parallelised. wrapper <- function(idx) { # Output progress in worker logfile cat( "Current index: ", idx, "\n" ) index <- sample(1:nrow(sir.adm), replace=TRUE) temp <- sir.adm[index, ] fit <- crr(temp$time, temp$status, temp$pneu) return(fit$coef) } # 4. Exporting needed data and loading required # packages on workers. sfExport("sir.adm") sfLibrary(cmprsk) # 5. Start network random number generator # (as "sample" is using random numbers). sfClusterSetupRNG() # 6. Distribute calculation start <- Sys.time(); result <- sfLapply(1:1000, wrapper) ; Sys.time()-start # Result is always in list form. mean(unlist(result)) # 7. Stop snowfall sfStop() ## Example 3 - Multiple nodes on a cluster (namely, the BDUC servers of uci-ics) ## ssh to bduc, then ssh to one of their claws (the head node is 32bit whereas the other wones are 64) ## put something like ## export LD_LIBRARY_PATH=/home/vqnguyen/lib:/usr/local/lib:/usr/lib:/lib:/sge62/lib/lx24-x86 in .bashrc ## or ## Sys.setenv(LD_LIBRARY_PATH="/home/vqnguyen/lib:/usr/local/lib:/usr/lib:/lib:/sge62/lib/lx24-x86") ## in an R session. Note: modify path to your home directory ## might have to install required packages elsewhere, like ~/Rlib, and use .libPaths() to add library path. Put this in .Rprofile sink('SnowFallExample.Rout', split=TRUE) .Platform .Machine R.version Sys.info() # 1. Initialisation of snowfall. # (if used with sfCluster, just call sfInit()) library(snowfall) sfInit(socketHosts=rep(c('claw1', 'claw2'),each=4), cpus=8,type='SOCK',parallel=T) # 2. Loading data. require(mvna) data(sir.adm) # 3. Wrapper, which can be parallelised. wrapper <- function(idx) { # Output progress in worker logfile cat( "Current index: ", idx, "\n" ) index <- sample(1:nrow(sir.adm), replace=TRUE) temp <- sir.adm[index, ] fit <- crr(temp$time, temp$status, temp$pneu) return(fit$coef) } # 4. Exporting needed data and loading required # packages on workers. sfExport("sir.adm") sfLibrary(cmprsk) # 5. Start network random number generator # (as "sample" is using random numbers). sfClusterSetupRNG() # 6. Distribute calculation start <- Sys.time(); result <- sfLapply(1:1000, wrapper) ; Sys.time()-start # Result is always in list form. mean(unlist(result)) # 7. Stop snowfall sfStop()

This is a good general reference for snowfall. Next thing to try is getting rpvm (PVM) to work for snowfall!

**leave a comment**for the author, please follow the link and comment on their blog:

**Super Nerdy Cool » R**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...