similar to: Wish: a way to track progress of parallel operations

Hello, thanks for bringing this topic up, and it would be excellent if we could come of with a generic solution for this in base R. It is one of the top frequently asked questions and requested features in parallel processing, but also in sequential processing. We have also seen lots of variants on how to attack the problem of reporting on progress when running in parallel. As the author

Hi, Here are the issues I found: Typos ----- (a) Found: It a parallel version of ?evalq?, "is" missing. (b) Found: 'parLapplyLB', 'parSapplyLB' are load-balancing versions, intended for use when applying ?FUN? to 'parLapplyLB' has no 'FUN' arg (more on this below). (c) Found: 'clusterApply' calls 'fun' on the first

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

Thank you for your answer! I agree with you except for the 3 (Error) example and I realize now I should have started with that in the explanation. >From my point of view parLapply(cl = clu, X = 1:2, fun = fun, c = 1) shouldn't give an error. This could be easily avoided by using all the argument names in the custerApply call of parLapply which means changing, parLapply <-

Hi R-developers In the package Parallel, the function parLapply(cl, x, f) seems to allow transmission of only one parameter (x) to the function f. Hence in order to compute f(x, y) parallelly, I had to define f(x, y) as f(x) and tried to access y within the function, whereas y was defined outside of f(x). Script: library(parallel) f <- function(x) { z <- 2 * x + .GlobalEnv$y # Try to

On 03/15/2018 05:25 PM, Henrik Bengtsson wrote: > On Thu, Mar 15, 2018 at 3:39 AM, <FlorianSchwendinger at gmx.at> wrote: >> Thank you for your answer! >> I agree with you except for the 3 (Error) example and >> I realize now I should have started with that in the explanation. >> >> From my point of view >> parLapply(cl = clu, X = 1:2, fun = fun, c =

In principle, I'd like to be able to do something like this: sge.parLapply(seq(10), function(x) parLapply(seq(x), function(x) x^2)) In practice, however, I have to resort to acrobatics like this: sge.options(sge.remove.files=FALSE) sge.options(sge.qsub.options='-cwd -V') sge.parLapply(seq(10), function(x) { sge.options(sge.save.global=TRUE)

Hi! I recognized that the argument matching of clusterApply (and therefore parLapply) goes wrong when one of the arguments of the function is called "c". In this case, the argument "c" is used as cluster and the functions give the following error message "Error in checkCluster(cl) : not a valid cluster". Of course, "c" is for many reasons an unfortunate

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

invoking parLapply without a cluster fails to find a previously registered cluster > library(parallel) > setDefaultCluster(makePSOCKcluster(2)) > parLapply(X=1:2, fun=function(...) {}) Error in cut.default(i, breaks) : invalid number of intervals This is because in parLapply length(cl) is determined before defaultCluster(cl) is called. By inspection, this appears to be true of

Dear R users, I have some problems with the parLapply function from the "parallel" package: I use parLapply on a pretty big R object without changing the object within the called function. If I execute parLapply alone, everything works fine. It seems that the object resides only once in the memory. But if I use the same call within another function, the object seems to be multiplied to

Dear Christian and Henrik, thank you for spotting the problem and suggestions for a fix. We'll probably add a chunk.size argument to parLapplyLB and parLapply to follow OpenMP terminology, which has already been an inspiration for the present code (parLapply already implements static scheduling via internal function staticClusterApply, yet with a fixed chunk size; parLapplyLB already

I am slightly confused by the way the standard output is redirected in a R snow cluster environment. I am using parLapply from the snow package to execute a function on my MPI/LAM cluster. How can I redirect standard output (produced using "cat") from this function back to the terminal where I invoked it? I intend to transmit some status information in advance to the final result of the

While using parallelization R seems to clone all environments (that are normally passed by reference) that are returned from a child process. In particular, consider the following example: library(parallel) env1 <- new.env() envs2 <- lapply(1:4, function(x) env1) cl<-makeCluster(2, type="FORK") envs3 <- parLapply(cl, 1:4, function(x) env1) envs4 <- parLapply(cl, 1:4,

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

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 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 using text miner (the "tm" package) to process large numbers of blog and message board postings (about 245,000). Does anyone have any advice for how to efficiently extract the meta data from a corpus of this size? TM does a great job of using MPI for many functions (e.g. tmMap) which greatly speed up the processing. However, the "meta" function that I need does not

Dear All, What is the preferred way for Package A, to initialize a cluster, and load Package B on all nodes? I am writing a package that parallelizes some functions through the use of a cluster if useRs are on a Windows machine (using parLapply and family). I also make use of another package in some of my code, so it is necessary to load the required packages on each slave once the cluster is

Dear R-users I was running into problems with my R code trying to run clh sampling (clhs package) in parallel mode (=on various data sets simultaneously). Here is the code (which I developed with some help:)): ****************************************** library("clhs") library("snow") a <- as.data.frame(replicate(1000, rnorm(20))) b <- as.data.frame(replicate(1000,