similar to: snow: hierarchical parallelization

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

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

R Version : 2.14.1 x64 Running on Windows 7 Connecting to a database on a remote Microsoft SQL Server 2012 The short form of my problem is the following. I have an unordered vectors of names, say: names<-c("A", "B", "A", "C","C") each of which have an id in a table in my db. I need to convert the names to their corresponding ids. I

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, 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, 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

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

I am trying to do parallel programming and I tried this library(doSNOW) library(foreach) testfunc<-function(x){ x<-x+1 x } noc<-2 cl <- makeCluster(do.call(rbind,rep(list("localhost"),noc)), type = "SOCK") registerDoSNOW(cl) clusterExport(cl=cl,c("testfunc.r")) testl<-foreach(pp=1:2) %dopar% { testfunc(pp) } And this works but if I try to

The runtime of parallel::parLapply depends on variables unrelated to the parLapply call. However, this is not clearly documented. Therefore I would like to suggest expanding the relevant documentation to explain this behaviour. Consider this example: parallel_demo <- function(random_values_count) { some_data <- runif(random_values_count) dummy_function <- function(x) { x }

Greetings: In R parallel's vignette, there is a comment "It would however take only slightly more work to allocate a stream to each task." (p.6). I've written down a working example that can allocate not just one, but several separate seeds for each task. (We have just a few project here that need multiple streams). I would like to help work that up for inclusion in the

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)

This is all normal, a fork cluster works with processes, that do not share memory. When you create a fork cluster, you create a new process, that has the same memory layout as the parent. But from this moment its memory is independent of the parent process. When parLapply is done, the results are serialized and copied back to the parent process. The serialized environment is independent of the

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

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,

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.

In pvec(list(1, 2), FUN, mc.cores=2) FUN sees integer() arguments whereas pvec(list(1, 2, 3), FUN, mc.cores=2) FUN sees list() arguments; the latter seems consistent with pvec's description. This came up in a complicated Bioconductor thread about generics and parallel evaluation https://stat.ethz.ch/pipermail/bioc-devel/2012-October/003745.html One relevant point is that a

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,

There are a few points I found unclear or unmentioned in the snow documentation (mostly I looked at the cluster.html web page). I thought I'd mention them here. What is the start up environment for the children? -------------------------------------------------- My best guess at the answer is in parentheses Do they inherit shell variables? (no) Do they inherit variables set in R or other

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)

Dear All, I am learning parallel in R and start with the package "snow". I did a test about running time and the parallel version is much slower than the regulat code. My laptop is X200s with dual core intel L9400 cpu. Should I make more clusters than 2? Or how to improve the performance? # install.packages("snow") library(snow) cl <- makeCluster(2) t1 <- proc.time() a