similar to: Parallel programming packages iterators, foreach and doMC released

On 2009-07-01, David M Smith <david at revolution-computing.com> wrote: > 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

I write about R every weekday at http://blog.revolution-computing.com . In case you missed them, here are some articles from last month of particular interest to R users. http://bit.ly/tygLz announced the release of the "foreach" and "iterators" packages on CRAN, for simple scalable parallel programming in R. http://bit.ly/FDS67 linked to Thomas Levin's Joy-Division-esque

Hi all, Sometimes I'll find myself "ctrl-c"-ing like a madman to kill some code that's parallelized via foreach/doMC when I realized that I just set my cpu off to do something boneheaded, and it will keep doing that thing for a while. In these situations, since I interrupted its normal execution, foreach/doMC doesn't "clean up" after itself by killing the

dear R experts---I have been experimenting with the foreach package (with doMC) for a while. my first impression is that it is a very easy way to acquire parallel processing capabilities. (thanks, revolution R.) the only two gotchas were about installation (it required an exit and restart), and the precedence order of the foreach (higher than '+', I think), but once I understood this,

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

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

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

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

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

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

I know this is not a revolution support forum, but as anyone noticed the following? I have a foreach loop to generate random samples. If I run the exact code below in normal r (2.14.1) it works as expected, but if I run it from revolution 4.2.0 each loop returns the same numbers. The only way I can get revolution to give different numbers is using 1 instead of 8 in registerDoSNOW(makeCluster(8,

I write about R every weekday at the Revolutions blog: http://blog.revolution-computing.com In case you missed them, here are some articles from last month of particular interest to R users. http://bit.ly/11YkB0 listed seven reasons of an anthropology professor for using R. http://bit.ly/9sbno linked to an intro of the ply package for performing SQL-like "group by" operations on data

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

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<-

I write about R every weekday at http://blog.revolution-computing.com?.?In case you missed them, here are some articles from last month of particular interest to R users. Some of the best stories this month came from reader tips -- thanks, and please keep the emails coming in! http://bit.ly/19T3Hj links to info on how Bump Charts are done in ggplot2 -- with a Kentucky Derby example.

Dear all, I know this topic has already been covered in other posts (at least the for loop Vs apply family of function), but I am looking for fresh / up-to-date opinion and feedback on those 3 methods to run unavoidable loops in R. I realise that it may be too general question for many, so any feedback appreciated. 1. apply Vs for loop >> Seems apply is (was?) supposed to be faster than

Hi list, My attention was drawn to the foreach package by recent posts...I decided to have a look... I'm using R.2.9.1 on Windows, I have downloaded the foreach package today (v 1.2.1), together with iterators (v. 1.0.1) and codetools (v.0.2-2). Full of hope I try the most simple thing of all out of the package vignette : > x <- foreach(i = 1:3) %do% sqrt(i) and get : > Erreur

I write about R every weekday at the Revolutions blog: http://blog.revolution-computing.com . In case you missed them, here are some articles from last month of particular interest to R users. http://bit.ly/WYAnM shows how to create a URL for a Google Spreadsheet so you can read the data directly from the cloud in R with read.csv. http://bit.ly/FuUNw reported on Google's use of R to predict

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:

Hello dear R-help group (and David Smith from REvolution), I would like to perform parallel computing using R with Condor (hopefully using foreach or other recommended solutions, if available) for some "Embarrassingly parallel" problem. I will start by listing what I found so far, and then go on asking for help. So far I found the a manual by Xianhong Xie from Rnews_2005-2 (see page