similar to: suppress output of makeCluster()

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 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

Hi all, I can't get the functionality of the package parallel to work. Specifically, makeCluster() hangs when I run it. I first noticed the problem when trying to run Rstan with multiple cores and the traced it back to the core package parallel. The following results in R hanging after the call to makeCluster. library(parallel) # Calculate the number of cores no_cores <-

A summary for reference: the new detectCores() for Windows in R-devel seems to be working both for logical and physical cores on systems with >64 logical processors? (thanks to Arun for testing!). If the feature is important for anyone particularly using an older version of Windows and/or on a system with >64 logical processors, it would be nice if you could test and report any

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 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: I try to construct a Document-Term Meatrix from a corpus. The commands I used are: > library(parallel)> library(tm)> library(RWeka)> library(topicmodels)> library(RTextTools)> cl=makeCluster(detectCores())> invisible(clusterEvalQ(cl, library(tm)))> invisible(clusterEvalQ(cl, library(RWeka))) > invisible(clusterEvalQ(cl, library(topicmodels)))>

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: *

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 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),

Dear R-ers, I am using getGEO to download expression data from the Gene Expression Omnibus. With default settings, when a file is downloaded and parsed, lots of dotted lines are printed in the terminal, like this: .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... ..........

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

Hi Dario, On 2018-06-14 14:00, Dario Strbenac wrote: > Good day, > > I'm trying the example cl <- makeCluster(2, type = "SOCK") Why use the 'snow' package and not 'parallel'? > 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

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

The problem causing the stray worker processes when the master fails to open a server socket to listen to connections from workers is not related to timeout in socketConnection(), because socketConnection() will fail right away. It is caused by a bug in checking the setup timeout (PR 17391). Fixed in 76275. Best Tomas On 3/18/19 2:23 AM, Henrik Bengtsson wrote: > (Bcc: CRAN) > >

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

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, all-- I've been working on a doParallel package to provide a foreach parallel backend for the new parallel package, but I am having trouble building the vignette. On my system, if I use the following minimal vignette (call it buggyVignette.Rnw): \documentclass[a4paper]{article} \title{Sweave Example 1} \author{Rich Calaway} \begin{document} \maketitle In this example

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

Hi all, I would like to know what would happen if using snow I create a cluster of size 50, for example using makeCluster(50,type='SOCK') on a machine with 2 Cores and run a function. Does snow run 25 and 25 functions on each of my 2 "real" processors or it just run 50 functions in one processor ? Thanks.