similar to: Snow & rvpm

Dear Friends, I'm trying to learn to use parallel computation using snow & rpvm Now, I have two boxes: 1) Pentium 4, pvm 3.4.5, R 2.2.0 with FreeBSD 5.4 (box uffbsd); 2) PowerPC, pvm 3.4.5, R 2.1.1 with Debian Linux latest stable (box powerpclinux); I'm using the instructions in (found in snow.pdf) http://www.stat.uiowa. edu/~luke/R/cluster/cluster.html. Now ** from the box

I am trying to run a monte carlo process using snow with a MPI cluster. I have ~thirty processors to run the algorithm on and I want to run it 5000 times and take the average of the output. A very simple way to do this is to divide 5000 by the number of processors to get a number n and tell each processor to run the algorithm n times. I realize there are more efficient ways to manage the

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.

Hi all, I am trying to use snow package to do a parallel MCMC. I have read a few guides and articles, the following is that I came up with. When I run it I got the error message: Error in checkForRemoteErrors(lapply(cl, recvResult)) : 4 nodes produced errors; first error: could not find function "ui.Next" The data is a longitudinal data with few repeated readings on a number of

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)

Hi, The following function use to work, but now it doesn't giving the error "> CallSnow(, 100) Using snow package, asking for 2 nodes 2 slaves are spawned successfully. 0 failed. Error in checkForRemoteErrors(val) : 2 nodes produced errors; first error: no applicable method for 'lapply' applied to an object of class "list" ". Where this is the

Hello, I am trying to do model autocalibration using the snow and rgenoud packages. The function I want to run in task-parallel fashion across multiple machines is one that pre- and post-processes data and runs an external model code. My problem is that external file I/O is happening only in the master node and not in the slaves. I have followed Jasjeet Sekhon's suggestion to test the

Hello, After getting help to solve part of my problem and some delay on my part, I am posting a more refined version to see if someone can help me further. I am trying to autocalibrate a model in my subject area using the snow and rgenoud packages. I want to use the key function "fn" that is called by genoud() to finalize input, run the model executable, and compute the objective

Dear R Users, I am attempting to use snow with rmpi. My configuration is: - R Version 2.7.2, rmpi - RMPI 0.5-6 - DeinoMPI 1.1.0 - Windows Xp SP2 I can sucessfully create a cluster and execute simple commands but for some reason, I cannot change the working directory in my nodes. > noclusters<-2 > cl <- makeCluster(noclusters, type = "MPI") 2 slaves are spawned

On Mon, Feb 26, 2018 at 3:25 PM, Gary Black <gwblack001 at sbcglobal.net> wrote: (Sorry to be a bit slow responding.) You have not supplied a complete example, which would be good in this case because what you are suggesting could be a serious bug in R or a package. Serious journals require reproducibility these days. For example, JSS is very clear on this point. To your question >

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

Hello everybody, I've got a bit of a problem with parLapply that's left me scratching my head today. I've tried this in R 2.11 and the 23 bit Revolution R Enterprise and gotten the same result, OS in question is Windows XP, the package involved is the snow package. I've got a list of 20 rain/no rain (1/0) situations for these two stations i and j, all the items in this list look

The following helps identify when .GlobalEnv$.Random.seed has changed: rng_tracker <- local({ last <- .GlobalEnv$.Random.seed function(...) { curr <- .GlobalEnv$.Random.seed if (!identical(curr, last)) { warning(".Random.seed changed") last <<- curr } TRUE } }) addTaskCallback(rng_tracker, name = "RNG tracker") EXAMPLE: >

Dear R-devel, Inspired by Michael Li's talk at JSM, I decided to try rpvm and snow on our two linux boxes. It only took me a couple of hours of screwing around to get it working (sooner if I had RTFM). Our setup is: 2 dual PIII-866 Xeons, one with 2GB RDRAM, the other with 1.28GB RDRAM. The first machine is acting as the NIS/NFS server. both /usr and /home are exported to the second

Thank you, everybody, who replied! I appreciate your valuable advise! I will move the location of the set.seed() command to after all packages have been installed and loaded. Best regards, Gary Sent from my iPad > On Mar 4, 2018, at 12:18 PM, Paul Gilbert <pgilbert902 at gmail.com> wrote: > > On Mon, Feb 26, 2018 at 3:25 PM, Gary Black <gwblack001 at sbcglobal.net> >

Hello, I'm trying to do some bootstrapping in a parallel environment (Linux cluster) in order to estimate confidence intervals for a certain parameter. Following the example in the documentation of the "snow" package (http://www.stat.uiowa.edu/~luke/R/cluster/cluster.html), I launch my computations by something like > cl.nuke.boot <- +

On 04/03/2018 5:54 PM, Henrik Bengtsson wrote: > The following helps identify when .GlobalEnv$.Random.seed has changed: > > rng_tracker <- local({ > last <- .GlobalEnv$.Random.seed > function(...) { > curr <- .GlobalEnv$.Random.seed > if (!identical(curr, last)) { > warning(".Random.seed changed") > last <<- curr

Hello all, I am re-posting my previous question with a simpler, more transparent, commented code. I have been ramming my head against this problem, and I wondered if anyone could lend a hand. I want to make parallel a bootstrap of a linear mixed model on my 8-core mac. Below is the process that I want to make parallel (namely, the boot.out<-boot(dat.res,boot.fun, R = nboot) command).

Dear All, Suppose I have a program as below: Outside is a loop for simulation (with random generated data), inside there are several sapply()'s (10~100) over the data and something else, but these sapply's have to be sequential. And each sapply do not involve very intensive calculation (a few seconds only). So the outside loop takes minutes to finish one iteration. I guess the better way

Optim's Nelder-Mead works correctly for this example. > optim(par=10, fn=fn, method="Nelder-Mead") x=10, ret=100.02 (memory) x=11, ret=121 (calculate) x=9, ret=81 (calculate) x=8, ret=64 (calculate) x=6, ret=36 (calculate) x=4, ret=16 (calculate) x=0, ret=0 (calculate) x=-4, ret=16 (calculate) x=-4, ret=16 (memory) x=2, ret=4 (calculate) x=-2, ret=4 (calculate) x=1, ret=1