similar to: revolution foreach oddity

Hi, I want to impute a data set multiple times with Amelia, but the data set is large so it takes a long time. As a result, I'm trying to run the multiple imputation with parallel processors in Windows, but am having trouble. Here is a quick example: ###### library(foreach) library(doSNOW) registerDoSNOW(makeCluster(4, type = "SOCK")) getDoParWorkers() getDoParName()

Hi all, Within a foreach loop with doSNOW, we cant call functions which come from the non-default package. We need to load(require/library) the package once more within the foreach loop. Anyone knows why would happen like this? Is it caused by the snow package and something happened when "snow" parallelize the job? Other than load the package once more with in the foreach loop, is

I am trying to do parallel programming and I tried this library(doSNOW) library(foreach) testfunc<-function(x){ x<-x+1 x } noc<-2 cl <- makeCluster(do.call(rbind,rep(list("localhost"),noc)), type = "SOCK") registerDoSNOW(cl) clusterExport(cl=cl,c("testfunc.r")) testl<-foreach(pp=1:2) %dopar% { testfunc(pp) } And this works but if I try to

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 Uses the superseded package: ?doSNOW?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,

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 The CRAN package snow is superseded by the parallel package which is 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

Hello, I was using snow to parallel-process some code in R-2.10 (32-bit windows. ). The code is as follows: require(foreach) require(doSNOW) cl <- makeCluster(6, type='SOCK') registerDoSNOW(cl) bl2 <- foreach(i=icount(length(unqmrno))) %dopar% { (some code here) } stopCluster(cl) When I run the same code in Windows R-2.11 (either 32-bit or 64-bit), R hangs at

Hello group, I am experimenting with parallel processing on my quad core Win 7 32 bit machine. Using these packages for the first time. I can see all my processor running at full performance when I use a smaller dataset require(snow) require(doSNOW) require(foreach) #change the 8 to however many cores\phys processors you have on your machine cl.tmp = makeCluster(rep("localhost",4),

I'm using bigkmeans in 'biganalytics' to cluster my 60,000 by 600,000 matrix. I'm using a 8 core Linux VM. I have register parallel backend with >registerDoMC() And I checked how many cores registered with >getDoParWorkers() It returns 8, which is the number of cores I have on my machine. And I run the test below, whose results shows improved speed due to parallel. check

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)

Dear expeRts, This is a similar post as on R-SIG-MAC [I didn't receive an answer there; not sure if it was the right place to post either]. I'm running R version 2.12.1 (Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)) on a MacBook Pro under Mac OS X 10.6.6. The following minimal example runs fine under this setup. However, if I am connected to a server via a VPN client [Cisco

Dear expeRts, I struggle with the following problem using snow clusters for parallel computing: I would like to specify l'Ecuyer's random number generator. Base R creates a .Random.seed of length 7, the first value indicating the kind fo random number generator. I would thus like to use the components 2 to 7 as the seed for l'Ecuyer's random number generator. By doing so, I

Shouldn't -n be 4 in the bsub command? One master+3 slaves. This was required for snowfall, but I think doSNOW is similar. Hope it helps mario On 16-Dec-10 23:09, Marius Hofert wrote: > Dear expeRts, > > I try to get a minimal example for parallel computing via "foreach" + "doSNOW" to run on a computer cluster (Brutus from

Dear Tomas, thank you very much. I installed r-devel r75201 and tested. The machine with 88 cores has NUMA disabled. It therefore has 2 processor groups with 64 and 24 processors each. require(parallel) detectCores() # [1] 88 This is great! Then I went on to test with a simple 'foreach()' loop. I started with 64 processors (max limit of 1 processor group). I ran with a simple function

Whas missing the R in the command line: "mpirun -n --hostfile /home/hostfile R --no-save -f rtest.R" Hope this helps mario On 13-Jan-11 22:08, Justin Moriarty wrote: > Hi, > Just wanted to share a working example of doSNOW and foreach for an openMPI cluster. The function eddcmp() is just an example and returns some inocuous warnings. The

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 revised) copies of packages \CRANpkg{multicore} and \CRANpkg{snow} (excluding MPI, PVM and NWS clusters). Code written to use the higher-level API functions in those packages should

Good Morning: I've read many, many posts on the r-help system and I feel compelled to quickly admit that I am relatively new to R, I do have several reference books around me, but I cannot count myself among the fortunate who seem to strong programming intuition. I have a data set consisting of 1637 observations of five variables: tensile strength, yield strength, elongation, hardness and a

I have a data set that has 4 columns and 53860858 rows. I was able to read this into R with: cc <- rep("character",4) myData <- read.table("myData.csv",header=FALSE,skip=1,colClasses=cc,nrow=53860858,sep=",") I need to reshape this data from long to wide. On a small data set the following lines work. But on the real data set, it didn't finish even when I

Hello, I'm running some code that requires untaring many files in the first step. This takes a lot of time and I'd like to do this in parallel, if possible. If it's the disk reading speed that is the bottleneck I guess I should not expect an improvement, but perhaps it's the processor. So I want to try this out. I'm working on windows 7 with R 2.15.1 and the latest foreach

A summary for reference: the new detectCores() for Windows in R-devel seems to be working both for logical and physical cores on systems with >64 logical processors? (thanks to Arun for testing!). If the feature is important for anyone particularly using an older version of Windows and/or on a system with >64 logical processors, it would be nice if you could test and report any

dear R experts--- I am experimenting with multicore processing, so far with pretty disappointing results. Here is my simple example: A <- 100000 randvalues <- abs(rnorm(A)) minfn <- function( x, i ) { log(abs(x))+x^3+i/A+randvalues[i] } ?## an arbitrary function ARGV <- commandArgs(trailingOnly=TRUE) if (ARGV[1] == "do-onecore") { ?library(foreach) ?discard <-