similar to: makeCluster hangs

Dear Henrik, thank you, for the quick reply. Bizarrely enough, the problem vanished when I woke the computer from sleep (I had previously replicated the problem after several restarts of both R and the MacOS). I will follow-up if I can again replicate the problem. Florian On 2/10/18 4:39 PM, Henrik Bengtsson wrote: > A few quick comments: > > * You mention R --vanilla, but make sure

A few quick comments: * You mention R --vanilla, but make sure to try with parallel::makeCluster(), so that you don't happen to pick up snow::makeCluster() if 'snow' is attached and ahead of parallel on the search() path. * Try creating a single background worker, i.e. parallel::makeCluster(1L). * Try with cl <- future::makeClusterPSOCK(1L, verbose = TRUE), which gives the same

Dear R Users, I have developed the following code for importing a series of zipped CSV by parallel computing. My problems are that: A) Some ZIP Files (Which contain CSVs inside) are corrupted, and cannot be opened. B) After executing parRapply I can only see the last.warning variable error, for knowing which CSV have failed in each node, but I cannot see all warnings, only 1 at a time. So: *

Hi all, The call parallel::makeCluster(1L) hangs infinitely on my MacOS machine which seems to be already reported by some people (e.g., https://stat.ethz.ch/pipermail/r-devel/2018-February/075565.html). However, the solutions posted on SO, GH or R-devel do not work in my case. So far, I unsuccessfully tested ? 1. Couple of reboots 2. Adding 192.0.0.1 to /etc/hosts 3. Using R.app

Hi Thomas, thanks for your reply (and thanks for your patience...). I am now using the following minimal reprex: > library(parallel) > cl <- makeCluster(2L) I freshly started the machine and did not open any other app. Just R.app (3.6.1). After executing the second line of code, R seems to hang infinitely and does not respond. The R process itself uses almost no CPU. Unfortunately,

Hi all, I am quite new in the world of parallelization and I wonder if there is a way to increase the speed of creation of a parallel socket cluster. The time spend to include threads increase exponentially with the number of thread considered and I use of computer with two 8 cores CPU and thus showing a total of 32 threads in windows 7. Currently, I use the default parameters (type =

Dear expeRts, I struggle with the following problem using snow clusters for parallel computing: I would like to specify l'Ecuyer's random number generator. Base R creates a .Random.seed of length 7, the first value indicating the kind fo random number generator. I would thus like to use the components 2 to 7 as the seed for l'Ecuyer's random number generator. By doing so, I

Dear Tomas, thank you very much. I installed r-devel r75201 and tested. The machine with 88 cores has NUMA disabled. It therefore has 2 processor groups with 64 and 24 processors each. require(parallel) detectCores() # [1] 88 This is great! Then I went on to test with a simple 'foreach()' loop. I started with 64 processors (max limit of 1 processor group). I ran with a simple function

Hi all, Is it possible to use the 'renice' option together with parallel clusters of type 'PSOCK'? The help page for parallel::makeCluster is not specific about which options are supported on which types and I am getting the following message when passing renice = 19 : > cl <- parallel::makeCluster(2, renice = 19) nice: ?+19?: No such file or directory Kind regards,

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

Hi Henrik, Thanks for the detailed in fast reply! My guess would be that the confusion comes from the different use of nice and renice. The workraund you provided work fine! Thanks a lot. Best, Andreas Henrik Bengtsson <henrik.bengtsson at gmail.com> writes: > Looks like a bug to me due to wrong assumptions about 'nice' > arguments, but could be because a

Hi Dominik, from the output, the master process could not "listen" on the port where it expects a connection from the worker. We need to find out why. I'd recommend first to create a minimal reproducible example (and one that does not use future, only parallel, and a minimal number of threads, ideally just 2). Then I'd recommend to check if the problem still exists with

Dear Tomas, thank you for looking into this. Here's the output: # number of logical processors - what detectCores() should return out <- system("wmic cpu get numberoflogicalprocessors", intern=TRUE) [1] "NumberOfLogicalProcessors \r" "22 \r" "22 \r" [4] "20 \r"

Looks like a bug to me due to wrong assumptions about 'nice' arguments, but could be because a "non-standard" 'nice' is used. If we do: > trace(system, tracer = quote(print(command))) Tracing function "system" in package "base" we see that the system call used is: > cl <- parallel::makePSOCKcluster(2L, renice = 19) Tracing system(cmd, wait

This is the specific error messsage from R CMD check --as-cran Error in .check_ncores(length(names)) : 16 simultaneous processes spawned Calls: prepost -> makeCluster -> makePSOCKcluster -> .check_ncores Execution halted Thanks, Ravi ________________________________ From: Ravi Varadhan Sent: Saturday, July 1, 2023 1:15 PM To: R-Help <r-help at r-project.org> Subject: Number

Good day, I'm trying the example cl <- makeCluster(2, type = "SOCK") from the makeCluster documentation and the R command prompt never returns. I am using a 32-bit Windows 10 computer. The problem doesn't happen on another 64-bit Windows 10 computer but does happen on another 32-bit Windows 7 computer. Can anyone reproduce it? Are there any other options that can be passed to

I've started to use the parallel package and it works very well speeding things up. Thank you for making this easy to do. Should I have expected that parCapply would return a vector when parApply returns a matrix? library(parallel) x <- matrix(rnorm(8), nc = 2) apply(x, 2, function(y) y) [,1] [,2] [1,] -0.9649685 0.91339851 [2,] -1.4313140 0.13457671 [3,] 1.0499248

Dear list, I just realized that one of the examples given in the help page to the function makeCluster of the library(snow) has a small syntax error : ## to get started library(snow) ?makeCluster .. will open a halp page containing the command towards the end of the examples : cl <- makeCluster(c(rep(list(macOptions), 2), rep(list(lnxOptions), 2), rep(list(winOptions),

Hello, I was using snow to parallel-process some code in R-2.10 (32-bit windows. ). The code is as follows: require(foreach) require(doSNOW) cl <- makeCluster(6, type='SOCK') registerDoSNOW(cl) bl2 <- foreach(i=icount(length(unqmrno))) %dopar% { (some code here) } stopCluster(cl) When I run the same code in Windows R-2.11 (either 32-bit or 64-bit), R hangs at

The command which hangs: I'm hoping there is a simple explanation, but I searched on-line and nothing jumped out. > cl <- makeCluster(type="SOCK",c("localhost"),manual=TRUE) Manually start worker on localhost with C:/PROGRA~1/R/R-214~1.2/bin/Rscript.exe "C:/Program Files/R/R-2.14.2/library/snow/RSOCKnode.R" MASTER=localhost PORT=11944 OUT=/dev/null