[R] Parallel R

[Martin Morgan]

"Juan Pablo Romero Méndez" <jpablo.romero at gmail.com> writes:

> Hello,
>
> The problem I'm working now requires to operate on big matrices.
>
> I've noticed that there are some packages that allows to run some
> commands in parallel. I've tried snow and NetWorkSpaces, without much
> success (they are far more slower that the normal functions)

Do you mean like this?

> library(Rmpi)
> mpi.spawn.Rslaves(nsl=2) # dual core on my laptop
> m <- matrix(0, 10000, 1000)
> system.time(x1 <- apply(m, 2, sum), gcFirst=TRUE)
   user  system elapsed
  0.644   0.148   1.017
> system.time(x2 <- mpi.parApply(m, 2, sum), gcFirst=TRUE)
   user  system elapsed
  5.188   2.844  10.693
          
? (This is with Rmpi, a third alternative you did not mention;
'elapsed' time seems to be relevant here.)

The basic problem is that the overhead of dividing the matrix up and
communicating between processes outweighs the already-efficient
computation being performed.

One solution is to organize your code into 'coarse' grains, so the FUN
in apply does (considerably) more work.

A second approach is to develop a better algorithm / use an
appropriate R paradigm, e.g.,

> system.time(x3 <- colSums(m), gcFirst=TRUE)
   user  system elapsed
  0.060   0.000   0.088
     
(or even faster, x4 <- rep(0, ncol(m)) ;)

A third approach, if your calculations make heavy use of linear
algebra, is to build R with a vectorized BLAS library; see the R
Installation and Administration guide.

A fourth possibility is to use Tierney's 'pnmath' library mentioned in
this thread

https://stat.ethz.ch/pipermail/r-help/2007-December/148756.html

The README file needs to be consulted for the not-exactly-trivial (on
my system) task of installing the package. Specific functions are
parallelized, provided the length of the calculation makes it seem
worth-while.

> system.time(exp(m), gcFirst=TRUE)
   user  system elapsed
  0.108   0.000   0.106
> library(pnmath)
> system.time(exp(m), gcFirst=TRUE)
   user  system elapsed
  0.096   0.004   0.052

(elapsed time about 2x faster). Both BLAS and pnmath make much better
use of resources, since they do not require multiple R instances.

None of these approaches would make a colSums faster -- the work is
just too small for the overhead.

Martin

> My problem is very simple, it doesn't require any communication
> between parallel tasks; only that it divides simetricaly the task
> between the available cores. Also, I don't want to run the code in a
> cluster, just my multicore machine (4 cores).
>
> What solution would you propose, given your experience?
>
> Regards,
>
>   Juan Pablo
>
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-- 
Martin Morgan
Computational Biology / Fred Hutchinson Cancer Research Center
1100 Fairview Ave. N.
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