similar to: Rsge: recursive parallelization

Dear all In previous R versions I ran R jobs on our cluster managed by SUN Grid Engine using the Rsge package. With the R-3.0 release, the package Rsge has been discontinued. What would be the equivalent way of running jobs on the cluster using current packages? I understand that the parallel package has been updated. What would be the equivalent to the lines below: require(Rsge)

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

I've been programming in one directory and recently switched to another directory. It appears that in doing so I've uncovered a problem. My environment was saving something so that my code would work and now I don't know how to fix it. I have the following bit of code: for(i in 1:index){ indexList = lst[i] for(j in 2:jobs-1){ indexList =

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

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 <-

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

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 =

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

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

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

I've some trouble with installing numpy in python 3.3 on Centos 6.8 as installation request a different python version... [root at sge ~]$ yum install python33 python33-python-tools [root at sge ~]$ scl enable python33 bash ===== *this load Python 3.3.2* === [root at sge ~]$ easy_install pip [root at sge ~]$ pip install numpy Collecting numpy Using cached numpy-1.12.0.zip

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

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,

Hi, GridEngine (http://gridengine.sunsource.net, aka. SGE) is an opensource batch system for clusters. They have an integration with SSH: http://gridengine.sunsource.net/project/gridengine/howto/qrsh_ssh.html The idea is that instead of using a modified rsh/rshd, they wanted to OpenSSH. However, in order to provide full job control, they need to add a few hooks in OpenSSH. Question: - Is it OK

Dear knowledgeable experts :-), I am trying to get openmpi, Rmpi and SNOW running on a Myrinet/GM cluster. I'm not an IT expert, but I surely could use a working installation of Rmpi and SNOW. I try to load the Rmpi library and get the following: > library(Rmpi) [master:07230] mca: base: component_find: unable to open osc pt2pt: file not found (ignored)

http://bugzilla.mindrot.org/show_bug.cgi?id=1070 Summary: Enchancement: Integration with GridEngine Product: Portable OpenSSH Version: 4.1p1 Platform: All OS/Version: All Status: NEW Severity: enhancement Priority: P2 Component: sshd AssignedTo: bitbucket at mindrot.org ReportedBy:

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

Hello R-devel, A function to be run inside lapply() or one of its friends is trivial to augment with side effects to show a progress bar. When the code is intended to be run on a 'parallel' cluster, it generally cannot rely on its own side effects to report progress. I've found three approaches to progress bars for parallel processes on CRAN: - Importing 'snow' (not

Hi all, I've been writing a package and I've run into a problem that I'm unsure how to solve. I am looking to pass a C++ class object to R so that it may be passed back to another C++ function later on to be used. I'm quite new to R and this is my first time writing a package, so I hope you can bear with me. The following is how I create the class and use