similar to: Snow Package and R: Exported Variable Problem

Hello, Is the first time I am using SNOW package and I am trying to tune the cost parameter for a linear SVM, where the cost (variable cost1) takes 10 values between 0.5 and 30. I have a large dataset and a pc which is not very powerful, so I need to tune the parameters using both CPUs of the pc. Somehow I cannot manage to do it. It seems that both CPUs are fitting the model for the same values

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

Hi all: I get the following error message that I am not able to resolve. Error in if (const(t, min(1e-08, mean(t)/1e+06))) { : missing value where TRUE/FALSE needed It appears right before the last data.frame statement. Below is the program that simulates data from one way random effects model and then computes normality and bootstrap confidence interval for

We got some errors and eventually figured out that parallel::clusterExport second argument is "varlist" while in snow::clusterExport it is "list". The user had loaded parallel first, but did something else which inadvertently loaded snow, then clusterExport failed because we had "varlist" and not "list". Are these different on purpose? pj -- Paul E.

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 List, the advent of multicore machines in the consumer segment makes me wonder whether it would, at least in principle, be possible to divide a computational task into more slave R processes running on the different cores of the same processor, more or less in the way package SNOW would do on a cluster. I am thinking of simple 'embarassingly parallel' problems, just like inverting

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

I'm trying to use snow in my dual-core (hopefully later this is going to run in a cluster). So, at this moment I create a cluster using SOCK connection (MPI in the future). However when I try to use clusterApplyLB I got "Error in socketSelect(socklist) : not a socket connection". Any ideas ? Do you know if that is going to be an isuue too when I swith from SOCK to MPI ? Sample code

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

Hi I try parallelising some code using the snow package and the following lines: cl <- makeSOCKcluster(8) pfunc <- function (x) (if(x <= (-th)) 1 else 0) ###correlation coefficient clusterExport(cl,c("pfunc","th")) cor.c.f <- parApply(cl,tms,c(1,2),FUN=pfunc) The parApply results in the error message: > cor.c.f <- parApply(cl,tms,c(1,2),FUN=pfunc) Error

Hello R-helpers. I've tried to recreate a parallel version of tapply() and table() using a combination of the parallel functions mclapply() and pvec() and papply(), but haven't been successful. In the end, I'm trying to get a cross tab of two vectors. I currently (can) use tapply(..., sum) and table(), and even xtabs() and ftable(), but with tens of millions of words and tens of

Hi everyone, I'm aiming to run an external executable (say filetorun.EXE) in parallel. The external executable collect needed data from a file, say "input.txt" and, in turn,generates several output files, say "output.txt". I need to generate "input.txt", run the executable and keep "input.txt" and "output.txt". I'm using Windows 7, R

I'm parallelizing some computation on hierarchical data, and would find it natural to do something like this (where a call to parLapply is embedded in outer call to parLapply): cl <- makeCluster(rep.int('localhost', 5), type='SOCK') clusterExport(cl, 'cl') parLapply(cl, 1:5, function(i) parLapply(cl, 1:5, function(j) i * j)) Snow

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

Hello, which possibilities are available in R for simultaneous or parallel computing? I only could find biopara (http://cran.r-project.org/src/contrib/Descriptions/biopara.html) Are there other possibilities? Are there special groups working on simultaneous computing with R? Thanks Markus -- Dipl.-Tech. Math. Markus Schmidberger Ludwig-Maximilians-Universit?t M?nchen IBE - Institut f?r

Hi, I am running my code in a cluster at Arizona State University. I have a huge climate data, 66000 X 500 I am not sure if I can find correlation of such a huge data in the cluster. Normally I allocate 20000M and operate on 5 X 20000. Even this is taking lot of time. Is there any way I can find cl = cor(cdata) utilizing the computers in the clusters(I am using 32 nodes ). I am using the

I upgraded from Fedora Core 4 to Fedora Core 5 and I find a lot of previously installed packages won't run because shared libraries or other system things have changed "out from under" the installed R libraries. I do not know for sure if the R version now from Fedora-Extras (2.2.1) is exactly the same one I was using in FC4. I see problems in many packages. Example, Hmisc: unable

Dear R-dev, I would like to accelerate my R computation by using parallel OpenMP compilers (e.g from Pathscale) on a 2-processor AMD server and I would like to know whether R is a tread safe library. The main kernel of the OpenMP parallelization is a C SEXP function that performs the computational routine in parallel with: ******************* SEXP example(SEXP list, SEXP expr, SEXP rho) {

R 3.4.2 OS X Colleagues I would appreciate some help with regular expressions. I have string that looks like: " ITERATION ,THETA1 ,THETA2 ,THETA3 ,THETA4 ,THETA5 ,THETA6 ,THETA7 ,SIGMA(1,1) ,SIGMA(2,1) ,SIGMA(2,2)? In the entries that

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