similar to: foreach() package for parallel computing

some hints for the search engines. I just did install.packages("foreach") install.packages("doMC") library(doMC) registerDoMC() library(foreach) > foreach(i = 1:3) %dopar% sqrt(i) The process has forked and you cannot use this CoreFoundation functionality safely. You MUST exec(). Break on

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 <-

Dear all, I am converting a large process to a parallel backhend using doMC and foreach. Basically, I havea long list of input graph files and each of them calls soem basic igraph package functions. I am parallelizing the run, in order to save time. All works fine, and each %dopar% call ends with a vector of results that at the end got fed into a data frame and saved as a csv table. When I

Hello, I am trying to run a small example with foreach, but I am having some problems. Here is the code: *library(doMC) registerDoMC() zappa = list() frank = list() foreach (i = 1:4) %dopar% { zappa[[i]] = kmeans (iris[-5],4) frank[[i]] = warnings() }* The code runs without error. However the zappa and frank will be empty lists. If I use regular *for *instead, the list will be filled up

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

Thank you very much Steve. Your suggestion works perfectly -- at least with doSEQ, doMC and doMPI. Bests, Renaud On 28/06/2011 15:35, Stephen Weston wrote: > I think that the result of the concatenation should be a call object, > rather than an expression object. How about something along the > lines of: > > '%dopar2%'<- function(obj, ex) { > ex<-

Hi, this post is about foreach operators, the compiler package and the last update of doMC that includes support for the compiler functionality. I am using a home-made %dopar%-like operator that adds some custom expression to be executed before the foreach loop expression itself (see sample code below). It used to work perfectly with doMC 1.2.1, but with the introduction of the compiler

Your code generates an error that has nothing to do with dopar. I have no idea what your function stack is supposed to do; you may be inadvertently calling utils::stack which would produce this kind of error: > stack(1:25, RAT = FALSE) Error in data.frame(values = unlist(unname(x)), ind, stringsAsFactors = FALSE) : arguments imply differing number of rows: 25, 0 HTH, Peter On Wed, Dec 6,

Dear R helpers, I have what another member on this forum described as an embarrassingly parallel problem. I am trying to fit models on subsets of some data based on unique combinations of two id factors in the dataset. Total number of combinations is 30^5, and this takes a long time. So, I would like fit models for each of the datasets produced by subsetting on the unique combinations, splitting

REvolution Computing has just released three new packages for R to CRAN (under the open-source Apache 2.0 license): foreach, iterators, and doMC. Together, they provide a simple, scalable parallel computing framework for R that lets you take advantage of your multicore or multiprocessor workstation to program loops that run faster than traditional loops in R. The three packages build on each

REvolution Computing has just released three new packages for R to CRAN (under the open-source Apache 2.0 license): foreach, iterators, and doMC. Together, they provide a simple, scalable parallel computing framework for R that lets you take advantage of your multicore or multiprocessor workstation to program loops that run faster than traditional loops in R. The three packages build on each

Hi I'm processing sequencing data trying to collapsing the locations of each unique sequence and write the results to a file (as storing that in a table will require 10GB mem at least) so I wrote a function that, given a sequence id, provide the needed line to be stored library(doMC) # load library registerDoMC(12) # assign the Number of CPU

foreach (or virtually anything you might use for concurrent programming) only really makes sense if the work the "clients" are doing is substantial enough to overwhelm the communication overhead. And there are many ways to accomplish the same task more or less efficiently (for example, doing blocks of tasks in chunks rather than passing each one as an individual job). But more to the

Hi all, I would like to parallelize some R code and would like to use the 'foreach' package with a foreach loop. However, whenever I call a function from an enabled package outside of MASS, I get an error message that a number of the functions aren't recognized (even though the functions should be defined). For example: library(foreach) library(doSMP) library(survival) # Create the

Dear all, Just wondering if someone could help me out converting my code from a for() loop into a foreach() loop or using one of the apply() function. I have a very large dataset and so I'm hoping to make use of a parallel backend to speed up the processing time. I'm having trouble getting selecting three variables in the dataset to use in the foreach() loops. My for() loop code is:

Hi all, I'm trying to use the package read.table within a foreach loop. I'm grabbing 500M rows of data at a time from two different files and then doing an aggregate/tapply like function in read.table after that. I had planned on doing a foreach loop 39 times at once for the 39 files I have, but obviously that won't work until I figure out why the segfault is occurring. The

Hi, in R 2.12.1, R CMD check hangs when building a vignette that uses a foreach loop with the doMC parallel backend. This does not happen in R 2.13.1, nor if I use doSEQ instead of doMC. All versions of multicore, doMC and foreach are the same on both my R installations. Has anybody encountered a similar issue? Thank you. Renaud ### UNIVERSITY OF CAPE TOWN This e-mail is subject to the

I have used foreach() for parallel computing but in the current problem, it is not working. Given the volume and type of the data involved in the analysis, I will try to give below the complete code without reproducible example. In short, each R environment will draw a set of separate files, perform the analysis and dump in separate folders. splist <- c("juoc", "juos",

Hi everyone, I'm working on a script trying to use foreach %dopar% but without success, so I manage to run the code with foreach %do% and looks like this: The code is part of a MCMC model for projects valuation, returning the most important results (VPN, TIR, EVA, etc.) of the simulation. foreach (simx = NsimT, .combine=cbind, .inorder=FALSE, .verbose=TRUE) %do% { MCPVMPA = MCVAMPA[simx]

Hi, I am trying to parallel computing with foreach function, but not able to get the result. I know that in parallel processing, all result is collected in list format, but I am not able to get input there. Any help is really appreciated. esf.m <-foreach (i = 1:n.s, .combine=rbind) %dopar% { EV <- as.data.frame(eig$vectors[,1:n.candid[i]]) colnames(EV) <- paste("EV",