Displaying 20 results from an estimated 700 matches similar to: "R 3.4 and mclapply assertion failure - is this a bug?"
2017 May 02
1
R 3.4 and mclapply assertion failure - is this a bug?
Dear all,?
I am not sure if this is a bug, so I prefer to post it here before filing.
After upgrading to 3.4 I encounter the following message with mclapply:
Assertion failure at kmp_runtime.cpp(6480): __kmp_thread_pool == __null.OMP: Error #13: Assertion failure at kmp_runtime.cpp(6480).OMP: Hint: Please submit a bug report with this message, compile and run commands used, and machine
2009 Dec 15
2
R GUI editor window - help field does not return the help topic (PR#14146)
Full_Name: Yan Alperovych
Version: R 2.10.1, GUI 1.31
OS: Mac OS 10.6.2
Submission from: (NULL) (139.165.27.244)
Hello everyone,
I am not sure if I am at the right place to put this question, so don't hesitate
to redirect me to the correct mailing list. My question is about the editor
window under the R GUI on Mac OS 10.6.2. Before the upgrade to R 2.10.1 with GUI
1.31, I was able to call
2023 May 16
1
mclapply enters into an infinite loop....
Dear members,
I am using arfima in an mclapply construction (from the parallel package):
Browse[2]> LYG <- mclapply(LYGH, FUN = arfima, mc.cores = detectCores())
^C
Browse[2]> LYG <- mclapply(LYGH[1:10], FUN = arfima, mc.cores = detectCores())
^C
Browse[2]> LYG <- mclapply(LYGH[1:2], FUN = arfima, mc.cores = detectCores())
^C
You can see that I am
2012 Dec 13
1
possible bug in function 'mclapply' of package parallel
Dear parallel users and developers,
I might have encountered a bug in the function 'mclapply' of package
'parallel'. I construct a matrix using the same input data and code with a
single difference: Once I use mclapply and the other time lapply.
Shockingly the result is NOT the same.
To evaluate please unpack the attached archive and execute
Rscript mclapply_test.R
I put the
2023 May 17
1
mclapply enters into an infinite loop....
Dear Jeff,
There was a problem in LYGH and lapply threw an error, but mclapply got stuck in an infinite loop. The doc for mclapply says that mclapply runs under try() with silent = TRUE. So that means mclapply should run properly, i.e output a try class object and exit. But it didn't. Can you shed some light on why this happened?
THanking you,
Yours sincerely,
AKSHAY M
2015 Jul 24
1
Memory limitations for parallel::mclapply
Hello,
I have been having issues using parallel::mclapply in a memory-efficient
way and would like some guidance. I am using a 40 core machine with 96 GB
of RAM. I've tried to run mclapply with 20, 30, and 40 mc.cores and it has
practically brought the machine to a standstill each time to the point
where I do a hard reset.
When running mclapply with 10 mc.cores, I can see that each process
2014 Jun 29
0
[patch] Fix n arg in mclapply call to ngettext
Regarding the following code,
warning(sprintf(ngettext(has.errors,
"scheduled core %s encountered error in user code, all values of
the job will be affected",
"scheduled cores %s encountered errors in user code, all values
of the jobs will be affected"),
paste(has.errors, collapse = ", ")),
domain = NA)
has.errors is a vector whose elements are the
2012 Nov 04
0
parallel::mclapply list coercion limits opportunities for code re-use?
The attached diff address the following issues in mclapply
mclapply coerces non-lists or objects (S3 or S4) to lists, but a list may not be
an efficient representation and is not required if the object implements length,
[, and [[ methods (lapply must also work on the object, either through coercion
to a list at the 'inner.do' level or through other means, e.g., promoting lapply
to a
2012 Dec 29
1
parallel error message extraction (in mclapply)?
dear R experts---I am looking at a fairly uninformative error in my program:
Error in mclapply(1:nrow(opts), solveme) :
(converted from warning) all scheduled cores encountered errors in user code
the doc on ?mclapply tells me that
In addition, each process is running the job inside try(...,
silent=TRUE) so if error occur they will be stored as try-error
objects in the list.
I looked up
2023 May 18
1
mclapply enters into an infinite loop....
On Wed, 17 May 2023 13:55:59 +0000
akshay kulkarni <akshay_e4 at hotmail.com> wrote:
> So that means mclapply should run properly, i.e output a try class
> object and exit. But it didn't. Can you shed some light on why this
> happened?
What's your sessionInfo()? Are you using a GUI frontend?
mclapply() relies on the fork() system call, which is tricky to get
right in a
2013 Apr 11
1
parallel::mclapply does not return try-error objects with mc.preschedule=TRUE
Hello,
Consider this:
1)
library(parallel)
res <- mclapply(1:2, stop)
#Warning message:
#In mclapply(1:2, stop) :
# all scheduled cores encountered errors in user code
is(res[[1]], 'try-error')
#[1] FALSE
2)
library(parallel)
res <- mclapply(1:2, stop, mc.preschedule=FALSE)
#Warning message:
#In mclapply(1:2, stop, mc.preschedule = FALSE) :
# 2 function calls resulted in an
2011 Apr 11
1
Mclapply and print statement
Dear all.
I am using the mclapply function to split my code to the many cores my system has. It seems that is working fine. This is the parallel version of lcapply.
The only problem that I seem to have is that the printf cannot print messages.
The ideal to me is to have fro my function an output of the form
Shadowlist<-mclapply(1:dimz, function(i) {
print(sprintf('Creating the
2020 Oct 08
2
exiting mclapply early on error
Hey folks,
Is there any way to exit an mclapply early on error?
For example, in the following mclapply loop, I have to wait for all the processes to finish before the error is returned.
```
mclapply(X = 1:12, FUN = function(x) {Sys.sleep(0.1); if(x == 4) stop()}, mc.cores = 4, mc.preschedule = F)
```
When there are many calculations in FUN, it takes a long time before the error is returned.
2012 Nov 16
0
Bug in parallel / mclapply
Hi,
there seem to be some (small) bugs in the mclapply function in parallel.
I discovered this in the current R release version, and I checked that it is
still present in R-devel.
I think it only occurs in the part of the code corresponding to argument option
mc.preschedule = FALSE.
Here are two examples:
a)
library(parallel)
mclapply(list(), identity, mc.preschedule=FALSE)
Error in
2012 Dec 11
1
Bug in mclapply?
I've been using mclapply and have encountered situations where it gives
errors or returns incorrect results. Here's a minimal example, which gives
the error on R 2.15.2 on Mac and Linux:
library(parallel)
f <- function(x) NULL
mclapply(1, f, mc.preschedule = FALSE, mc.cores = 1)
# Error in sum(sapply(res, inherits, "try-error")) :
# invalid 'type' (list) of argument
2018 Oct 18
0
Random behavior of mclapply
Hi Thibault,
mclapply has been designed to signal an error in two ways. User code
errors are returned as special objects (of class "try-error") in the
respective element of the result list. All other errors (including a
process killed) are returned as NULL in the respective elements of the
result list. To detect these errors reliably, one needs to implement FUN
so that it never
2010 Aug 12
1
multicore mclapply error
I'm running r 2. on a mac running 10.6.4 and a dual-core macbook pro. I'm having a funny time with multicore. When I run it with 2 cores, mclapply, R borks with the following error.
The process has forked and you cannot use this CoreFoundation functionality safely. You MUST exec().
Break on __THE_PROCESS_HAS_FORKED_AND_YOU_CANNOT_USE_THIS_COREFOUNDATION_FUNCTIONALITY___YOU_MUST_EXEC__()
2015 Sep 02
0
mclapply memory leak?
Well it's only a leak if you don't get the memory back after it returns,
right?
Anyway, one (untested by me) possibility is the copying of memory pages
when the garbage collector touches objects, as pointed out by Radford Neal
here:
http://r.789695.n4.nabble.com/Re-R-devel-Digest-Vol-149-Issue-22-td4710367.html
If so, I don't think this would be easily avoidable, but there may be
2019 Apr 12
0
SUGGESTION: Settings to disable forked processing in R, e.g. parallel::mclapply()
On Fri, 12 Apr 2019 at 21:32, Travers Ching <traversc at gmail.com> wrote:
>
> Just throwing my two cents in:
>
> I think removing/deprecating fork would be a bad idea for two reasons:
>
> 1) There are no performant alternatives
"Performant"... in terms of what. If the cost of copying the data
predominates over the computation time, maybe you didn't need
2019 Apr 13
1
SUGGESTION: Settings to disable forked processing in R, e.g. parallel::mclapply()
On Sat, 13 Apr 2019 at 18:41, Simon Urbanek <simon.urbanek at r-project.org> wrote:
>
> Sure, but that a completely bogus argument because in that case it would fail even more spectacularly with any other method like PSOCK because you would *have to* allocate n times as much memory so unlike mclapply it is guaranteed to fail. With mclapply it is simply much more efficient as it will