similar to: double boostrap with clusterApplyLB

Dear R-users, we are using library(snow) for computation on a linux cluster with RMPI. We have a problem with clusterEvalQ: after launching clusterEvalQ it seems loading the required library on each node but if we type a function belonging to the loaded package R doesn't find it. > library(snow) # making cluster with 3 nodes > cl <- makeCluster(3, type = "MPI") Loading

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 have this function and want to run it parallel with different sets of data. Using SNOW and clusterApplyLB. system.time(out <- mclapply(cData, plotGraph)) #each cData contains 100X6000 doubles system.time(out <- mclapply(cData2, plotGraph)) system.time(out <- mclapply(cData3, plotGraph)) system.time(out <- mclapply(cData4, plotGraph)) system.time(out <- mclapply(cData5,

Dear R-Users, I have a model with a latent variable for a spatio-temporal process. I would like to use EM algorithm to estimate the parameters. Does anybody know how to implement the algorithm in R? Thank you very much in advance, Michela

Hi, I am an astronomer and somewhat new to boostrap statistics. I understand the basic idea of bootstrap resampling, but am uncertain if it would be useful in my case or not. My problem consists of maximizing a likelihood function based on the velocities of a number of stars. My assumed distribution of velocities of these stars is:

Hello there I have the following model based on the hollings disc equation for the type II functional response for 2 data sets: nls(eaten~(a*suppl)/(1+a*h*suppl) where eaten is the number of prey eaten by a predator and suppl is the number of prey initially supplied to the same predator. I have parameter estimates of 'a' and 'h' for the two populations studied and would like

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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

Hello, I've installed R on my RHEL3 cluster and I am trying to get Rmpi to work properly. R is installed using the following ./configure --prefix=/home/apps/R-2.2.0 I installed snow using R CMD INSTALL /home/apps/snow And finaly Rmpi R CMD INSTALL /home/apps/Rmpi --configure-args=--with-mpi=/path/to/lam There were no errors or warnings upon installation. However when i perform the test

Sorry for wasting your time with an incorrect diagnosis in my previous mail! I'm running my script using mpirun -mp 4 and using snowfall+Rmpi on Linux 64bits. I receive the following error, that terminates the run. Any idea? I'm able to reproduce the problem with a minimal script (below). If n < 16371 it completes 9 iterations without problems. Already at 16371 it dies at iteration 7

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: -------------------------------------------------------------------------------------------------------

I see about 7 different R packages for multi-process parallel programming. Which do you think is the best, most complete, and most robust to pick for general purpose Erlang-style message-passing programming in R, and why? First here's my use case, and then my analysis so far. I often have code whose basic organization looks something like this: 1. Fetch step: For each date, gather up or

Many thanks to Dr Yu for updating Rmpi for R 2.6.0, and for starting to make the changes to support Open MPI. I have just built the updated Debian package of Rmpi (i.e. r-cran-rmpi) under R 2.6.0 but I cannot convince myself yet whether it works or not. Simple tests work. E.g. on my Debian testing box, with Rmpi installed directly using Open Mpi 1.2.3-2 (from Debian) and using 'r' from

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

Dear R-users, I have a little problem that I can't solve by myself. I have a data frame with 2 factors and 8 observations (see the following code): y <- c(1,1,1,2,2,3,3,3) y <- factor(y) levels(y) <- c("a","b","c") x <- c(1,2,3,1,2,1,2,3) x <- factor(x) levels(x) <- c("x","y","z") X <-

Hi, I have few high-level questions about the Snow and Rmpi packages . I understand that Snow uses Rmpi as one of possible transport layers, yet my questions about user experience, not technical details: 1. Does Snow install and work well in Windows? 2. Interruptibility. I understand that currently it is impossible to interrupt a running top-level command in Snow ( Ctl-c or the likes), the

Dear R-users, I am trying to make Snow and Rmpi working on an heterogenous cluster of linux computers. The master computer is built on a 64 bit architecture whereas all nodes are built on a 32 bit architecture. LAM/MPI was installed successfully on all machine. LAM boots correctly on the master computer and it recognizes all nodes defined in the lamhosts file. However, when starting R,

The proper use of SPRNG is a little unclear to me in each of these packages, even more so when I put them all together. My immediate question: in snow I did clusterSetupSRNG. If a later want to (re) set the system to a known seed, how do I do it? That is, I want each distributed process to recreate exactly the same random numbers that it did before; I assume SPRNG assures the streams are

Hi, I need to understand when is it best to use /rmpi/ and when is it best to use /snow/ for parallel programming in R? I understand snow can be used for a group of non-clustered work stations also. But I wish to understand from the point of view of using both on clusters for a problem which has few chunks of straightforward data-parallelism interleaved with some communication. Since both are

I'm running my script using mpirun -mp 4 and using snowfall+Rmpi on Linux 64bits. I receive the following error, but apparently without consequences on the results. Any idea? I'm able to reproduce it with a minimal script (below). Seems the critical issue is the for loop. Without it no error. Thanks for your help! TERM: Undefined variable. TERM: Undefined variable. TERM: Undefined