similar to: embarrassingly parallel problem - simple loop solution

Hello, The problem I'm working now requires to operate on big matrices. I've noticed that there are some packages that allows to run some commands in parallel. I've tried snow and NetWorkSpaces, without much success (they are far more slower that the normal functions) My problem is very simple, it doesn't require any communication between parallel tasks; only that it divides

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

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

All, We are researching approaches to parallel R with the end goal of running R in a distributed manner on a Linux cluster. We expect of course to do some work decomposing our problems to be task-parallel or data-parallel, but wouldn't mind getting an initial boost working with "embarrassingly parallel" code sections and one of the approaches below. Incidentally our environment

Hi, I am working on a 64-bit rocks cluster and am relatively new to the R package. I am trying to get Snow working with R and Rmpi and have run into the following issue. R is able to load the Rmpi and snow libraries and is able to run simple commands both interactively and batch as follows: -------------------------------------------------------------------------------------------------------

Dear R-user, I am trying to use the function 'parLapply' from the 'snow' package which is supposed to work the same wys as 'lapply' but for a parallelized cluster of computers. The function I am trying to call in parallel is 'dudi.pca' (from the 'ade4' package) which performs principal component analyses. When I call this function on a list of

Hello I am fortunate (or in really big trouble) in that the research group I work with will soon be receiving several high end dual quad core machines. We will use the Ubuntu OS on these. We intend to use this cluster for some extensive modeling applications. Our programming guru has demonstrated the ability to link much simpler machines to share CPUs and we purchased the new ones to take

Has anyone succeeded in getting a parallel BLAS, such as Goto's BLAS, installed on a machine running Ubuntu 11.10? I currently have the libatlas3gf-base package installed on a machine with an AMD Athlon X4 processor. I tried to install the libopenblas-base and libopenblas-dev packages but there seemed to be a glich related to having both libopenblas and libatlas3gf installed so that when I

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

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

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.

?I want to take advantage of my multicore CPU to speed up a loop in a simulation program.? I didn?t write the code, but the iterations appear independent to me, at least in the sense that the results of one loop do not depend on previous ones.? Right now I?m relegated to a Windows box that runs Windows 7.? These appear to be the options: ? Pnmath-appears to parallelize non-BLAS routine but

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

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]]

Rmpi is not currently available on CRAN, and I don't think it has been for a few months. It is available at http://www.stats.uwo.ca/faculty/yu/Rmpi/ Does anyone know its current status? A few months ago I corresponded with the author, who noted some build problems (specifically on Debian) were the hang up, and seemed to be working on it. I wasn't able to get it to work then (on a

Dear R gurus, I have a very embarrassingly parallelizable job that I am trying to speed up with snow on our local cluster. Basically, I am doing ~50,000 t.test for a series of micro-array experiments, one gene at a time. Thus, I can easily spread the load across multiple processors and nodes. So, I have a master list object that tells me what rows to pick up for each genes to do the t.test from

Almost all of the heavy crunching I do in R is like: > for(i in long.list){ + do.something(i) + } > collect.results() Since all the invocations of do.something are independent of one another, there is no reason that I can't run them in parallel. Since my machine has four processors, a natural way to do this is to divide up long.list into 4 pieces and then start 4 jobs, each of which

Hello, We have recently added a 64-bit 2 x Dual-Core Athlon server running Red Hat Enterprise Linux AS release 3 to allow for processing of large data sets (>4GB) in R. To integrate this server into our Linux cluster, I have been trying (unsuccessfully) to use Rmpi/lam-mpi to parallel process some of our scripts. While I have successfully compiled R 2.4.0 and lam-mpi 7.1.2 on this server using

Dear Sir or Madam, I've been using R for one year and I really appreciate it. I would like to know if a version performing parallel computations on multicore computers and computer clusters exists. Thank you very much. Yours sincerely. Enrico Giorgi _________________________________________________________________ [[alternative HTML version deleted]]

Hi all, I'm trying to get the snow package with mpi working on my linux ubuntu 10.04 machine. When I tried to install it (see below) it already had problems with the dependencies. R version 2.14.0 (2011-10-31) Copyright (C) 2011 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You