parallel computing in R

I don't know what would have happened to me and my research if I didn't knew R. It has always been the most co-operative tool. But  RBM was never happy with the speed of programs, specially when I am doing simulations etc. So, one fine day I started googling, the ways to speed up the programs. I invested one complete day and got decent increase in speed. I was very happy and excited that I actually managed something cool; but next day his reactions made me realise that it is not sufficient. 

This led to following two consequences:

i) I was kind of disheartened; so neither I tried going in to greater depth nor did I try formulating and recording the procedure, at least for my record.

ii) I started looking into package making in R (which he always wanted me to do). In last 2 years I have tried doing that at least 2-3 times, but always gave up. But to my surprise, this time I managed within a day (will share that too, soon).

So there I was.. Within 2 days, I had successfully managed implementing 2 reasonably clumsy techniques. But my laziness never allowed me to go ahead with it.

It was only when MGK poked me, I realised that I cannot recall any of it. I didn't knew which files to look into. But eventually I managed. Thanks to ASK, who helped me in reviving. Before I forget again, I made a point to re-search, re-view and document...

Few important notes:

> I have used Revolution R Community 6.0 . One can also try with other versions of R like R 2.15.1, but then you need to make sure if all the required libraries/packages are downloaded/installed. Revolution R has got all of them.

> The version of parallel computing I have used, employs the notion of foreach (a replacement for standard "for statement"). It says "do this to everything in this set", rather than "do this x times".

> We have to register a parallel backend, otherwise foreach will execute tasks sequentially.

> %dopar% executes the R expression using the currently registered backend.

So here is a sample code:

# packages that have to be loaded

library(parallel)
library(foreach)
library(iterators)
library(doParallel)

# to detect the number of CPU cores on the current host
detectCores()  

# Creates a set of copies (say 2) of R running in parallel
cl = makeCluster(2) 

# registers the parallel backend with the ‘foreach’ package

registerDoParallel(cl)

# to check if the multiple core is registered. If not, a warning will be issued that it is running sequentially. However, warning is issued only once

z=foreach(i=1:10)%dopar% 
{  sample(c("H", "T"), 10000, replace=TRUE)
}

# returns the number of execution workers in the currently registered doPar backend; should be same as input to makeCluster

getDoParWorkers()

# sample code
x=foreach(i=1:T,.combine='+')%dopar%
{    # a function which has to be executed T times
     vec=vecofderiv(N,M,orgtpm,orgtP)               
     I=vec%*%t(vec)
}
# x contains the sum of expression, on the last line of the loop (since argument to .combine is '+'). Here x is sum of I’s.

Some more notes:

 >   I am not sure, but my experimentation indicated that it is sufficient to register as many parallel backends as is the number of cores available. There was no improvement if I tried registering more backends than available no. of cores

>  Other arguments to .combine could be 'cbind', 'c', or some user defined function (not very sure about the usage). 

> Since the execution is parallel, make sure that the tasks are not related or dependent.

> This is not the only option available. Though I had to stick to this one as I was not able to exactly understand how to bring others to work, like doSNOW, which is a parallel adapter for SNOW (Simple Network of Workstations) package.  There were few which were not available for Windows, like doMC, which is a parallel adaptor for the multicore package. 

PS-1. Thanks are due to the authors of all the R help files I looked into; and offcourse the R team.
PS-2. Please post your valuable comments, so that we can get a better insight of the procedure.

Post PS: Hope to get back soon, with something light and random ;)