similar to: socket clusters on snow dies easily

BACKGROUND: While troubleshooting random, occasionally occurring, errors from parallel::makePSOCKcluster("localhost", port = 11000); Error in socketConnection("localhost", port = port, server = TRUE, blocking = TRUE, : cannot open the connection I had another look at parallel:::newPSOCKnode(), which is used internally to set up each background worker. It is designed to,

A solution is to have parallel:::.slaveRSOCK() attempt to connect multiple times before failing, e.g. makeSOCKmaster <- function(master, port, timeout, useXDR, maxTries = 10L, interval = 1.0) { port <- as.integer(port) for (i in seq_len(maxTries)) { con <- tryCatch({ socketConnection(master, port = port, blocking = TRUE,

Great. For the record of this thread, I've submitted patch PR17391 (https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=17391). I've patched it against the latest R-devel on the SVN, passes 'make check-all', and I've verified it works with the above tests. /Henrik On Fri, Mar 9, 2018 at 4:37 AM, <luke-tierney at uiowa.edu> wrote: > I'm happy to look at a

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

Dear All, Is the following expected behaviour? set.seed(1) library(parallel) cl = makeCluster(5) clusterSetRNGStream(cl, iseed = NULL) parSapply(cl, 1:5, function(i) sample(1:10, 1)) # 7 4 2 10 10 clusterSetRNGStream(cl, iseed = NULL) # 7 4 2 10 10 parSapply(cl, 1:5, function(i) sample(1:10, 1)) stopCluster(cl) The documentation could be read either way, e.g. * iseed: An integer to be

(Bcc: CRAN) This is a proposal helping CRAN and alike as well as individual developers to avoid stray R processes being left behind that might be produced when an example or a package test fails to set up a parallel::makeCluster(). ISSUE If a package test sets up a PSOCK cluster and then the master process dies for one reason or the other, the PSOCK worker processes will remain running for 30

Hello, I am trying to do model autocalibration using the snow and rgenoud packages. The function I want to run in task-parallel fashion across multiple machines is one that pre- and post-processes data and runs an external model code. My problem is that external file I/O is happening only in the master node and not in the slaves. I have followed Jasjeet Sekhon's suggestion to test the

Hi, I want to compute a distribution of the intersection of a graph and 'randomized' graphs induced by the permutations of node labels (to preserve the graph topology). Since I ll have many permutations to perform, I was thinking of using the snow package and in particular "parSapply" to divide the work between my 4 CPUs. But I get the following error message : Error in

Hello, After getting help to solve part of my problem and some delay on my part, I am posting a more refined version to see if someone can help me further. I am trying to autocalibrate a model in my subject area using the snow and rgenoud packages. I want to use the key function "fn" that is called by genoud() to finalize input, run the model executable, and compute the objective

On Mon, Feb 26, 2018 at 3:25 PM, Gary Black <gwblack001 at sbcglobal.net> wrote: (Sorry to be a bit slow responding.) You have not supplied a complete example, which would be good in this case because what you are suggesting could be a serious bug in R or a package. Serious journals require reproducibility these days. For example, JSS is very clear on this point. To your question >

Here is the code, assuming 8 cores in the cpu. library('modeest') library('snow') cl = makeCluster(rep('localhost', 8), 'SOCK') x = vector(length=50) x = sapply(x, function(i) i=sample(c(1,0), 1)) pastK = function(n, x, k) { if (n>k) { return(x[(n-k):(n-1)]) } else {return(NA)} } predR = function(x, k) { pastList = lapply(1:length(x), function(n)

Dear Luke and others, I have many R versions on my machine and want to start a particular one when snow builds its cluster. (The same version I start snow from.) It seems that everything is set up correctly in defaultClusterOptions: > mget(ls(defaultClusterOptions), defaultClusterOptions) $homogeneous [1] TRUE $manual [1] FALSE $master nodename "maya.unil.ch" $outfile [1]

Hello, I am using function makePSOCKcluster to make parallel computation on 3 EC2 Amazon machines. I have a passwordless between machines and ssh is correct. In the R 2.15.1 release this function works correctly. Installing R 3.0.1 on my EC2 machines makePSOCKcluster does not produce the cluster. If I run the function with outfile="" option, I obtain this message Error in

Dear All I have sucessfully created cluster of four nodes using localhost in my local machine by executing the following command > cl<-makePSOCKcluster(c(rep("localhost",4)),outfile='',homogeneous=FALSE,port=11001) starting worker pid=4271 on localhost:11001 at 12:12:26.164 starting worker pid=4280 on localhost:11001 at 12:12:26.309 starting worker pid=4289 on

The following helps identify when .GlobalEnv$.Random.seed has changed: rng_tracker <- local({ last <- .GlobalEnv$.Random.seed function(...) { curr <- .GlobalEnv$.Random.seed if (!identical(curr, last)) { warning(".Random.seed changed") last <<- curr } TRUE } }) addTaskCallback(rng_tracker, name = "RNG tracker") EXAMPLE: >

Thank you, everybody, who replied! I appreciate your valuable advise! I will move the location of the set.seed() command to after all packages have been installed and loaded. Best regards, Gary Sent from my iPad > On Mar 4, 2018, at 12:18 PM, Paul Gilbert <pgilbert902 at gmail.com> wrote: > > On Mon, Feb 26, 2018 at 3:25 PM, Gary Black <gwblack001 at sbcglobal.net> >

Dear R users, we are trying to do some parallel computing using library(snow). In particular we have a cluster with 3 nodes >cl <- makeCluster(3, type = "MPI") 3 slaves are spawned successfully. 0 failed. and we want to compute the function op_mat (see below) first with the master and then with the cluster using system.time for checking the computational performance.

I am running R.And I want to make parallel computations. When using the snow package under R-2.13.1patched or the parallel package under R 2.14.0dev, R isn't able to create a cluster. When typing: cl <- makeCluster(4) R doesn't respond. It doesn't crash either. specs: OS: 32 bit, Windows XP Prof, SP3. Processor: Intel Core i3 cpu, quad core. My colleague works on exactly the

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 all, I have a piece of code that  I want to run in parallel (I am working in system of 16 cores) foreach (i=(seq(-93,-73,length.out=21))) %dopar%  {           threshold<-i            print(i)          do_analysis1(i,path)          do_analysis2(i,path)            do_something_else_analysis1(i,path)            something_else_now(i,path)  } as you can see I have already tried to make