similar to: How to do parallel computation?

Hi Everybody, I know that R has snow package which can be used for Parallel Computing. However, every time I try making a cluster, the only type of cluster I'm able to make is the "SOCK" (that too because I disabled the firewalls). For the rest (i.e. MPI, NWS, and PVM), I get error every time I try making one. I get the following errors

Dear all, I usually run R on my laptop with Windows XP Professional. Now I really want to run R on a computer cluster (4 processors) with Suse Linux Enterprise ver. 10. But I am new with computer cluster. Should I modify my functions in order to use the greater performance and availability than that provided by my laptop? Is there any R manual on parallel computations on

Anyone using or has access to ParallelR? I was looking at the page and found nothing really useful! http://www.revolution-computing.com/sitegenius/topic.php?id=195 I want to see if I can run R on a cluster of workstation, and use batch systems like Grid Engine or Xgrid: http://gridengine.sunsource.net/ http://ww.apple.com/acg/xgrid/ --Chi

Dear R People: I was wondering if there were any packages for parallel programming in R. According to the R-help, at one time there was a package called SNOW. It doesn't seem to exist anymore. Any help would be much appreciated! Sincerely, Erin Hodgess mailto: hodgess@gator.uhd.edu [[alternative(swapped) HTML version deleted]]

Dear R users, I have access to a Sun cluster with multiple processors , a lot of RAM and with RedHat installed. I want to take advantage of its power for a R routine very time consuming. Whick package do I have to use? I know there are snow,snowFT and others package.Which is the best for my purpose? Do someone have experiences with this? Thanck in advance. Moreno

Dear list, When I run an R CMD check --as-cran on my package (pROC) I get the following note: > Uses the superseded package: ?doSNOW? The fact that it uses the doSNOW package is correct as I have the following example in an .Rd file: > #ifdef windows > if (require(doSNOW)) { > registerDoSNOW(cl <- makeCluster(2, type = "SOCK")) > ci(roc2,

Guys, I'm running R on both Windows & Linux. I'm looking at a number of packages for parallel execution. It seems that the most used packages are "snow" and "Rmpi". snow seems more user friendly, but it doesn't run on windows. I see from searching the mailing list that I'm not the first one to try it on Windows. There was a message that kind of shed some

I am trying to use the optimizing function genoud() with the snow package on a couple of i686 machines running Redhat Linux WS4 . I don't know anything about PVM or MPI, so I just followed the directions in snow and rgenoud for the simplest method and started a socket cluster. My function fn for genoud involves updating an input file for a separate numerical model with the latest parameter

Oh, I completely missed that one. It's very neat as it seems to work both on Windows and Unix. Thanks! Xavier On 10/02/15 10:52, Martyn Plummer wrote: > The CRAN package snow is superseded by the parallel package which is > distributed with R since version 2.14.0. Here are the release notes > > \item There is a new package \pkg{parallel}. > > It incorporates (slightly

I have an "embarrassingly parallel" routine that I need to run 24000^2/2 times (based on some microarray data). All I really need to do is parallelize a nested for-loop. But I haven't found a clear list of what packages/commands I'd need to do this. I've got a dual quad core xeon system running RHEL5, so if I could use hyperthreading to increase the number of (virtual)

Dear List, the advent of multicore machines in the consumer segment makes me wonder whether it would, at least in principle, be possible to divide a computational task into more slave R processes running on the different cores of the same processor, more or less in the way package SNOW would do on a cluster. I am thinking of simple 'embarassingly parallel' problems, just like inverting

Dear All, The "clients.txt" file of the latest Rserve package, by Simon Urbanek, says, regarding its R client, "(...) a simple R client, i.e. it allows you to connect to Rserve from R itself. It is very simple and limited, because Rserve was not primarily meant for R-to-R communication (there are better ways to do that), but it is useful for quick interactive connection to an

Hi, does anybody know how to use the dual processors in the machines of a cluster? I am using R with rpvm and snow packages. I usually start pvm daemon and add host machines first, and then run R to start my computing work. But I find that only one processor in each machine is used in this way and the other one always stays idle. Is there any simple way to tell pvm to use the two processors at

Hi all, I'm trying to get the snow package with mpi working on my linux ubuntu 10.04 machine. When I tried to install it (see below) it already had problems with the dependencies. R version 2.14.0 (2011-10-31) Copyright (C) 2011 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You

Dear R-devel, Inspired by Michael Li's talk at JSM, I decided to try rpvm and snow on our two linux boxes. It only took me a couple of hours of screwing around to get it working (sooner if I had RTFM). Our setup is: 2 dual PIII-866 Xeons, one with 2GB RDRAM, the other with 1.28GB RDRAM. The first machine is acting as the NIS/NFS server. both /usr and /home are exported to the second

hello, I'm using R 1.8.1 with the lastest snow package on FreeBSD 4.9. However, when I try to using socket clusters, it's very unstable. Sometimes it dies half way when I run parSapply(), sometimes it dies when cluster connection is idle. I create a socket cluster by following cmd cl = makeCluster("foo", type = "SOCK", outfile="/tmp/rafanlog");

Hi All, I have k identical parallel pieces of code running, each using n.rand random numbers.? I would like to use the same RNG (for now), and set the seeds so that I can guarantee that there are no overlaps in the random numbers sampled by the k pieces of code.? Another side goal is to have reproducibility of my results.? In?the past I have used C with SPRNG for this task, but I'm hoping

... can be used to represent unknown number of parameters passed into a function. For example, I write a function g. g calls another function f1. For example f1 could be different random number generation function. when f1=rnorm(), it has 3 parameters n, mean and standard deviation. when f1=rexp(), it has 2 parameters n and rate. g can be defined as g <- function(f1, ...) { f1(...) }

Hi; I have a R script that includes a call to genoud(); genoud process lasts about 4 seconds, what would be OK if I hadn't have to call it about 2000 times. This yields about 2 hours of processing. And I would like to use this script operationally; so that it should be run twice a day. It seems to me that the parallel processing option included in genoud() divides the task inside the function

Hi, I had access to an hpc cluster, and wanted to parallelize some of my R code. I looked at the snow,nws, rscalapack documentation but was unable to make out how I should submit my job to the hpc, and how I should code a simple program. For example, if I had 10 matrices, and 10 processor how should I write the R (and the hpc submit code) so that I run the calculations (e.g. rowsums) for each