similar to: multicore/mcparallel error

I stumbled upon the following: f <- parallel::mcparallel(raw(0L)) parallel::mccollect(f) # $`77083` # NULL but f <- parallel::mcparallel(raw(1L)) parallel::mccollect(f) # Error in unserialize(r) : read error traceback() # 2: unserialize(r) # 1: parallel::mccollect(f) (restarting because the above appears to corrupt the R session) f <- parallel::mcparallel(raw(2L))

Hi, I am facing a very weird problem with parallel::mclapply. I have a script which does some data wrangling on an input dataset in parallel and then writes the results to disk. I have been using this script daily for more than one year always on an EC2 instance launched from the same AMI (no updates installed after launch) and processed thousands of different input data sets successfully. I now

Hi Andreas, the error is reported when some child process cannot send results to the master process, which originates from an error returned by write() - when write() returns -1 or 0. The logic around the writing has not changed since R 3.5.2. It should not be related to the printing in the child, only to returning the value. The problem may be originating from the execution environment,

Hi Andreas, thank you very much, good job finding it was EBADF. Now the question is why the pipe has been closed prematurely; it could be accidentally by R (a race condition in the cleanup code in fork.c) or possibly by some other code running in the same process (maybe the R program itself or some other code it runs). Maybe we can take this off the list and come back when we know the cause

I'm trying to understand how to use the multicore package. In particular, I'm trying to work out what is covered where it says this in the help file for the parallel function: expr: expression to evaluate (do _not_ use any on-screen devices or GUI elements in this code) Can a function that produces an EPS file be considered an "expression" in this case? I've

Hi, I am experiencing a problem related to serialization behavior in 2.15.0 beta (binary installed from Debian unstable) and 2.16.0 (from svn) that is not present in 2.14.2 (binary from Debian testing). I don't fully understand the problem. Also, I tried but have not yet been able to create a small, self-contained example that reproduces the problem. However, I do have a large, not

Hi again, One important correction of my first message: I misinterpreted the output. Actually in that R session 2 input files were processed one after the other in a loop. The first (with 88 parts went fine). The second (with 85 parts) produced the sendMaster errors and failed. If (in a new session via Rscript) I only process the second input file it will work. The other observations on R vs

Hi Tomas, Thanks for your prompt reply and your offer to help. I might need to get back to this since I am not too experienced in debugging these kinds of issues. Anyway, I gave it a try and I think I have found the immediate cause: I installed the debug symbols (r-base-core-dbg), placed https://github.com/wch/r-source/blob/tags/R-3-5-2/src/library/parallel/src/fork.c in cwd and changed the

Hi all, With the help of Tomas, I was able to track the issue down: Prior to R v3.6.0 the parallel package passes an uninitialized variable as the file descriptor argument to the close system call. In my particular R session this uninitialized variable (reproducibly) was holding the value 7, which corresponded to the file descriptor of the write end of the pipe the second child would use to

Hi all, With the help of Tomas, I was able to track the issue down: Prior to R v3.6.0 the parallel package passes an uninitialized variable as the file descriptor argument to the close system call. In my particular R session this uninitialized variable (reproducibly) was holding the value 7, which corresponded to the file descriptor of the write end of the pipe the second child would use to

On 20/06/2015 22:21, Henrik Bengtsson wrote: > QUESTION: > Is it possible to query number of active jobs running after launching > them with parallel::mcparallel()? > > For example, if I launch 3 jobs using: > >> library(parallel) >> f <- lapply(1:3, FUN=mcparallel) > > then I can inspect them as: > >> str(f) > List of 3 > $ :List of 2 >

QUESTION: Is it possible to query number of active jobs running after launching them with parallel::mcparallel()? For example, if I launch 3 jobs using: > library(parallel) > f <- lapply(1:3, FUN=mcparallel) then I can inspect them as: > str(f) List of 3 $ :List of 2 ..$ pid: int 142225 ..$ fd : int [1:2] 8 13 ..- attr(*, "class")= chr [1:3] "parallelJob"

Dear list, I am running into 2 problems when using the optimize function from the stats package (note: I've also tried unsuccessfully to use optim, nlm, & nlminb). The second problem is caused by my solution to the first, so I am asking if anyone has a better solution to the first question, or if there exists a solution to the second problem. I should also mention that what I am

Hi there, I've tried to implement an asynchronous job scheduler using parallel::mcparallel() and parallel::mccollect(..., wait=FALSE). My goal was to send processes to the background, leaving the R session open for interactive use while all jobs store their results on the file system. To keep track of the running jobs I've stored the process ids and written a little helper to not spawn

Dear All, Since upgrading to 3.6.0, I've been getting a strange error messages from the child process when using mcparallel/mccollect. Before filing a report in the Bugzilla, I want to figure out whether I had been doing something wrong all this time and R 3.6.0 has exposed it, or whether something else is going on. # Background # Ultimately, what I want to do is to be able to set a time

Dear R developers: ... 7: lapply(seq_len(cores), inner.do) 8: FUN(1:3[[3]], ...) 9: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) Selection: .....................Error in sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) : long vectors not supported yet: memory.c:3100 admittedly, my outcome will be a very big list, with 30,000 elements, each containing data frames

I've been hacking with parallel package for some time and built a parallel processing framework with it. However, although very rarely, I did notice "ignoring SIGPIPE signal" error every now and then. After a deep dig into the source code, I think I found something worth noticing. In short, wring to pipe in the C function mc_exit(SEXP sRes) may cause a SIGPIPE. Code from

Hi all, I'm trying to use the package read.table within a foreach loop. I'm grabbing 500M rows of data at a time from two different files and then doing an aggregate/tapply like function in read.table after that. I had planned on doing a foreach loop 39 times at once for the 39 files I have, but obviously that won't work until I figure out why the segfault is occurring. The

Have read the latest code, but I still don't understand why mc_exit needs to write zero on exit. If a child closes its pipe, parent will know that on next select. Best, Yijiang Tomas Kalibera <tomas.kalibera at gmail.com> ?2019?5?20??? ??10:52??? > > This issue has already been addressed in 76462 (R-devel) and also ported > to R-patched. In fact rmChild() is used in

I have an lapply function call that I want to parallelize. Below is a very simplified version of the code: url_base <- "https://cloud.r-project.org/src/contrib/" files <- c("A3_1.0.0.tar.gz", "ABC.RAP_0.9.0.tar.gz") res <- parallel::mclapply(files, function(s) download.file(paste0(url_base, s), s)) Instead of download a couple of files in parallel, I get a