similar to: R communication

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

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

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

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 R, I have two R instances running at the same time, say instance A and instance B. Is there a simpler way to pass the data in A to B? More precise, I have a stupid example: In instance A, I am running a function "test1" test1 <- function (x1) { x2 <- sin(x1) return(x2) } In instance B, another function "test2" test2 <- function (x2) { x3 <-

Dear all, I am using the functions 'optim' and 'nlminb'. For both, you can provide a function which computes the gradient of the objective function (to enhance speed and precision). In my case, both the objective function and the gradient take time to be computed and share many common computations (similar matrix, products, etc...). Therefore, I have to compute these

Hi, I need to understand when is it best to use /rmpi/ and when is it best to use /snow/ for parallel programming in R? I understand snow can be used for a group of non-clustered work stations also. But I wish to understand from the point of view of using both on clusters for a problem which has few chunks of straightforward data-parallelism interleaved with some communication. Since both are

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]]

Hello, I have a question that I was wondering if anyone had a fairly straightforward answer to: what is the quickest and easiest way to take advantage of the extra cores / processors that are now commonplace on modern machines? And how do I do that in Windows? I realise that this is a complex question that is not answered easily, so let me refine it some more. The type of scripts that I'm

Hello, which possibilities are available in R for simultaneous or parallel computing? I only could find biopara (http://cran.r-project.org/src/contrib/Descriptions/biopara.html) Are there other possibilities? Are there special groups working on simultaneous computing with R? Thanks Markus -- Dipl.-Tech. Math. Markus Schmidberger Ludwig-Maximilians-Universit?t M?nchen IBE - Institut f?r

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

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 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, 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

Dear R-users, we are using library(snow) for computation on a linux cluster with RMPI. We have a problem with clusterEvalQ: after launching clusterEvalQ it seems loading the required library on each node but if we type a function belonging to the loaded package R doesn't find it. > library(snow) # making cluster with 3 nodes > cl <- makeCluster(3, type = "MPI") Loading

Dear all, I very often use the R function "apply", for speedup purposes. I am now also using MATLAB, and would like to use the same kind of function. I have already asked MATLAB people, and the answer is : "vectorize"... but of course, this is not always possible. So, instead of using a FOR loop all the time, I tried using the bsxfun. So you R people, who might also use MATLAB,

Many thanks to Dr Yu for updating Rmpi for R 2.6.0, and for starting to make the changes to support Open MPI. I have just built the updated Debian package of Rmpi (i.e. r-cran-rmpi) under R 2.6.0 but I cannot convince myself yet whether it works or not. Simple tests work. E.g. on my Debian testing box, with Rmpi installed directly using Open Mpi 1.2.3-2 (from Debian) and using 'r' from

Dear R-help, Has anyone tried running R on a Beowulf-type cluster? I can get R to run in batch (using R CMD BATCH) on a cluster, but am wondering if it is possible to get an interactive R session on a compute node. Right now, if I run: beorun --nolocal R I just get the R start-up message and back to the shell prompt. If I try bpsh 0 R I can get R started (but the R prompt does not

Hello, I've installed R on my RHEL3 cluster and I am trying to get Rmpi to work properly. R is installed using the following ./configure --prefix=/home/apps/R-2.2.0 I installed snow using R CMD INSTALL /home/apps/snow And finaly Rmpi R CMD INSTALL /home/apps/Rmpi --configure-args=--with-mpi=/path/to/lam There were no errors or warnings upon installation. However when i perform the test