search for: parlappli

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

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

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 <-

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

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 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 }

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

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

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, 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

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-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,

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 = 1) > shouldn't give an error. > > This could be easily avoided by

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,

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

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

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

[cross-posted on R-devel and Bioc-devel, since the functions from the parallel package discussed here are mirrored in the BiocGenerics package] Hi, I am currently running a lengthy simulation study (no details necessary) on a large multi-core system. The simulated data sets are stored in a long list and they are unevenly sized (hence, the computation times vary greatly between data sets), so