On 09/03/2014 03:25 PM, Jim Leek wrote:> I'm a programmer at a high-performance computing center. I'm not
very
> familiar with R, but I have used MPI from C, C++, and Python. I have to
run
> an R code provided by a guy who knows R, but not MPI. So, this fellow used
> the R snow library to parallelize his R code (theoretically, I'm not
> actually sure what he did.) I need to get this code running on our
> machines.
>
> However, Rmpi and snow seem to require mpi spawn, which our computing
center
> doesn't support. I even tried building Rmpi with MPICH1 instead of 2,
> because Rmpi has that option, but it still tries to use spawn.
>
> I can launch plenty of processes, but I have to launch them all at once at
> the beginning. Is there any way to convince Rmpi to just use those
processes
> rather than trying to spawn its own? I haven't found any documentation
on
> this issue, although I would've thought it would be quite common.
This script
spawn.R
======# salloc -n 12 orterun -n 1 R -f spawn.R
library(Rmpi)
## Recent Rmpi bug -- should be mpi.universe.size()
nWorkers <- mpi.universe.size()
mpi.spawn.Rslaves(nslaves=nWorkers)
mpiRank <- function(i)
c(i=i, rank=mpi.comm.rank())
mpi.parSapply(seq_len(2*nWorkers), mpiRank)
mpi.close.Rslaves()
mpi.quit()
can be run like the comment suggests
salloc -n 12 orterun -n 1 R -f spawn.R
uses slurm (or whatever job manager) to allocate resources for 12 tasks and
spawn within that allocation. Maybe that's 'good enough' -- spawning
within the
assigned allocation? Likely this requires minimal modification of the current
code.
More extensive is to revise the manager/worker-style code to something more like
single instruction, multiple data
simd.R
=====## salloc -n 4 orterun R --slave -f simd.R
sink("/dev/null") # don't capture output -- more care needed here
library(Rmpi)
TAGS = list(FROM_WORKER=1L)
.comm = 0L
## shared `work', here just determine rank and host
work = c(rank=mpi.comm.rank(.comm),
host=system("hostname", intern=TRUE))
if (mpi.comm.rank(.comm) == 0) {
## manager
mpi.barrier(.comm)
nWorkers = mpi.comm.size(.comm)
res = list(nWorkers)
for (i in seq_len(nWorkers - 1L)) {
res[[i]] <- mpi.recv.Robj(mpi.any.source(), TAGS$FROM_WORKER,
comm=.comm)
}
res[[nWorkers]] = work
sink() # start capturing output
print(do.call(rbind, res))
} else {
## worker
mpi.barrier(.comm)
mpi.send.Robj(work, 0L, TAGS$FROM_WORKER, comm=.comm)
}
mpi.quit()
but this likely requires some serious code revision; if going this route then
http://r-pbd.org/ might be helpful (and from a similar HPC environment).
It's always worth asking whether the code is written to be efficient in R --
a
typical 'mistake' is to write R-level explicit 'for' loops that
"copy-and-append" results, along the lines of
len <- 100000
result <- NULL
for (i in seq_len(len))
## some complicated calculation, then...
result <- c(result, sqrt(i))
whereas it's much better to "pre-allocate and fill"
result <- integer(len)
for (i in seq_len(len))
result[[i]] = sqrt(i)
or
lapply(seq_len(len), sqrt)
and very much better still to 'vectorize'
result <- sqrt(seq_len(len))
(timing for me are about 1 minute for "copy-and-append", .2 s for
"pre-allocate
and fill", and .002s for "vectorize").
Pushing back on the guy providing the code (grep for "for" loops, and
look for
that copy-and-append pattern) might save you from having to use parallel
evaluation at all.
Martin
>
> Thanks,
> Jim
>
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