Rdsm-package: Adds a threaded parallel programming paradigm to R.
In Rdsm: Threads Environment for R

Description Details Author(s) See Also Examples

This package provides a parallel shared-memory programming paradigm for R, very similar to threaded programming in C/C++. This enables the programmer to write simpler, clearer code. Furthermore, in some applications this package produces significantly faster code, compared to versions written for other parallel R libraries. It also allows placing very large matrices in secondary storage, while treating them as being in shared memory.

Package:	Rdsm
Type:	Package
Version:	2.1.1
Date:	2014-02-16
License:	GPL (>= 2)

List of functions:

   initialization, run at manager:  

      mgrinit():  initialize system
      mgrmakevar():  create a shared variable 
      mgrmakelock():  create a lock
      makebarr():  create a barrier

   called by applications:  

      barr():  barrier call
      rdsmlock():  lock operation (via realrdsmlock())
      rdsmunlock():  unlock operation (via realrdsmunlock())

   application utilities:  

      getidxs():  partition a set of indices for work assignment
      getmatrix():  allow a matrix to be referenced regardless of 
                    whether it is specified as a bigmemory object, 
                    a bigmemory descriptor, or via a quoted name
      stoprdsm()  shut down cluster and clean up files

Built-in variables accessible by the threads, at the worker nodes:

1
2
3

   myinfo$nwrkrs:  total number of threads
   myinfo$id:  this thread's ID number

To run, set up a cluster via the parallel package); we'll refer to the R process from which this is done as the manager; the processes running in the cluster will be called workers. Create the application's shared variables from the manager, using mgrmakevar(). Launch the worker threads, again from the manager, by the parallel call clusterEvalQ() or clusterCall(). One typically codes so that the results are in shared variables. See examples below, and more in the examples/ directory in this distribution.

The shared variables are read to/written by any of the workers and the manager. In fact, while an Rdsm application is running, other R processes on the same machine (or a different machine sharing the same file system, if the variables are filebacked) can access the shared variables. See the file ExternalAccess.txt in the doc/ directory.

Rdsm uses the bigmemory library to store its shared variables. Though the latter can work on a (physical) cluster of several machines sharing a file system, Rdsm does not run on such systems at this time.

Further documentation in the doc/ directory.

Norm Matloff <matloff@cs.ucdavis.edu>

mgrinit, mgrmakevar, mgrmakelock, barr, rdsmlock, rdsmunlock, getidxs, getmatrix

library(parallel)
c2 <- makeCluster(2)  # form 2-thread Snow cluster
mgrinit(c2)  # initialize Rdsm
mgrmakevar(c2,"m",2,2)  # make a 2x2 shared matrix
m[,] <- 3  # 2x2 matrix of all 3s
# example of shared memory:
# at each thread, set id to Rdsm built-in ID variable for that thread
clusterEvalQ(c2,id <- myinfo$id)
clusterEvalQ(c2,m[1,id] <- id^2)  # assignment executed by each thread
m[,]  # top row of m should now be (1,4)

# matrix multiplication; the product u %*% v is computed, product
# placed in w

# note again:  mmul() call will be executed by each thread

mmul <- function(u,v,w) {
   require(parallel)
   # decide which rows of u this thread will work on
   myidxs <- splitIndices(nrow(u),myinfo$nwrkrs)[[myinfo$id]]
   # multiply this thread's part of u with v, placing the product in the
   # corresponding part of w
   w[myidxs,] <- u[myidxs,] %*% v[,]
   invisible(0)  
}

# create test matrices
mgrmakevar(c2,"a",6,2)
mgrmakevar(c2,"b",2,6)
mgrmakevar(c2,"c",6,6)
# give them values
a[,] <- 1:12
b[,] <- 1  # all 1s
clusterExport(c2,"mmul")  # send mmul() to the threads
clusterEvalQ(c2,mmul(a,b,c)) # run the threads
c[,]  # check results