similar to: Problem with clusterCall, "Error in checkForRemoteErrors(lapply(cl, recvResult)) : "

Dear R Users, I am attempting to use snow with rmpi. My configuration is: - R Version 2.7.2, rmpi - RMPI 0.5-6 - DeinoMPI 1.1.0 - Windows Xp SP2 I can sucessfully create a cluster and execute simple commands but for some reason, I cannot change the working directory in my nodes. > noclusters<-2 > cl <- makeCluster(noclusters, type = "MPI") 2 slaves are spawned

Hello all, I am re-posting my previous question with a simpler, more transparent, commented code. I have been ramming my head against this problem, and I wondered if anyone could lend a hand. I want to make parallel a bootstrap of a linear mixed model on my 8-core mac. Below is the process that I want to make parallel (namely, the boot.out<-boot(dat.res,boot.fun, R = nboot) command).

People of R(th), I have been ramming my head against this problem, and I wondered if anyone could lend a hand. I want to parallelize a bootstrap of a linear model on my 8-core mac. Below is the process that I want to parallelize (namely, the m2.ph.rlm.boot<-boot(m2.ph,m2.ph.fun, R = nboot) command). This is an extension of the bootstrapping linear models example in Venables and Ripley to

Hello, I'm trying to run a code that uses the snowfall package. Here is the structure of my code. sfInit(parallel=T, cpus = 5, slaveOutfile="ErrorMessage.txt") sfExportAll() sfLibrary(rgdal) sfLibrary(raster) sfLibrary(sp) sfLibrary(rgeos) sfLibrary(snowfall) system.time( sfClusterApplyLB(1:10, function(k) { sfCat(paste("Iteration ", k),

I am trying to run a monte carlo process using snow with a MPI cluster. I have ~thirty processors to run the algorithm on and I want to run it 5000 times and take the average of the output. A very simple way to do this is to divide 5000 by the number of processors to get a number n and tell each processor to run the algorithm n times. I realize there are more efficient ways to manage the

Hi, The following function use to work, but now it doesn't giving the error "> CallSnow(, 100) Using snow package, asking for 2 nodes 2 slaves are spawned successfully. 0 failed. Error in checkForRemoteErrors(val) : 2 nodes produced errors; first error: no applicable method for 'lapply' applied to an object of class "list" ". Where this is the

I am interested in running R commands asynchronously. My first choice is in the same R session that I am currently in. Here, the goal would be to run something like RunAsynchSameSession(myfunction(), "outputname.rda") Once RunAsynchSameSession had started myfunction(), RunAsynchSameSession would complete immediately. myfunction would keep going. It is OK if execution of the

Hi, This is my first message to this list. It would be great if people here could help me with the following problem (self-contained code example below): I have a list of matrices and would like to apply a summary function to the matrices in the list. The matrices represent social networks, therefore I need to apply some specialized summary functions provided by the ergm package. These

At office, using the internal LAN at my disposal, I'm having a go at parallel computing - to begin with - with pvm, rpvm & snow. The two boxes are as follows Remote machine uffbsd: CPU: Intel(R) Pentium(R) 4 CPU 2.00GHz (1994.13-MHz 686-class CPU) Origin = "GenuineIntel" Id = 0xf24 Stepping = 4 real memory = 260046848 (248 MB) This machine NbBSD: CPU: Mobile Intel(R)

Hello, After getting help to solve part of my problem and some delay on my part, I am posting a more refined version to see if someone can help me further. I am trying to autocalibrate a model in my subject area using the snow and rgenoud packages. I want to use the key function "fn" that is called by genoud() to finalize input, run the model executable, and compute the objective

Greetings, I am having an interesting problem and I wonder if anyone else has seen this behavior. I am running R 2.11.1 with SNOW 0.3-3 on a Dell cluster running CentOS 5.5. I create my cluster using: cluster<- makeCluster(nodes,type="SOCK",port=10191) # nodes is a vector of compute nodes I then wrap a loop around clusterApplyLB to evaluate my function multiple times, with

Dear All, I have three classes of questions about generating random numbers with different packages (windows xp 32bit R). . 1. Suppose I would like to use package *foreach*, can I use current Sys.time as a seed? Although I can get the time up to1e-6 second precesion, the code below dose not work well on a local machine with two cores. ################# library(foreach) library(snow)

Hello, I am trying to do model autocalibration using the snow and rgenoud packages. The function I want to run in task-parallel fashion across multiple machines is one that pre- and post-processes data and runs an external model code. My problem is that external file I/O is happening only in the master node and not in the slaves. I have followed Jasjeet Sekhon's suggestion to test the

Dear R users, we are trying to do some parallel computing using library(snow). In particular we have a cluster with 3 nodes >cl <- makeCluster(3, type = "MPI") 3 slaves are spawned successfully. 0 failed. and we want to compute the function op_mat (see below) first with the master and then with the cluster using system.time for checking the computational performance.

On Mon, Feb 26, 2018 at 3:25 PM, Gary Black <gwblack001 at sbcglobal.net> wrote: (Sorry to be a bit slow responding.) You have not supplied a complete example, which would be good in this case because what you are suggesting could be a serious bug in R or a package. Serious journals require reproducibility these days. For example, JSS is very clear on this point. To your question >

Hello everybody, I've got a bit of a problem with parLapply that's left me scratching my head today. I've tried this in R 2.11 and the 23 bit Revolution R Enterprise and gotten the same result, OS in question is Windows XP, the package involved is the snow package. I've got a list of 20 rain/no rain (1/0) situations for these two stations i and j, all the items in this list look

Here is the code, assuming 8 cores in the cpu. library('modeest') library('snow') cl = makeCluster(rep('localhost', 8), 'SOCK') x = vector(length=50) x = sapply(x, function(i) i=sample(c(1,0), 1)) pastK = function(n, x, k) { if (n>k) { return(x[(n-k):(n-1)]) } else {return(NA)} } predR = function(x, k) { pastList = lapply(1:length(x), function(n)

Hello, I have a program that used to run well in October, it uses library snow. Since then, one change has ocurred (snow library has been updated) and another could have ocurred (I've unadvertently modified something). Anyway, now when I make the call: parallel.model.results <- clusterApply(cl,processors.struct,MCexe) exactly as I used to do, where MCexe is my function and

I'm working with snow and created a local cluster. So far, the same code has always worked (please see below). However, now I receive a message that the connection with the nodes cannot be opened. I restarted my workstation but that didn't help. Is there a known solution for this problem? Thanks a lot for any help. bram foubert library(snow) cl =

Hi R-Core, i am using nnet and DEoptim, Xcc=matrix(rnorm(100,0.5,0.08),50,2) Ycr=matrix(rnorm(50,0.2,0.05),50,1) pred_regm1 <- function(A) { A1=A[1] A2=A[2] A3=A[3] regm1 <- nnet(Xcc,Ycr,entropy=T,size=A1,decay=A2,maxit=2000,trace=F,Hess=T,rang=A3,skip=T) dif=sum((predict(regm1,Xcc)-Ycr)^2) return(dif) } somar=DEoptim(pred_regm1,c(1,0.00001,0.01), c(25,0.999,0.95),