similar to: Problem with mclapply in package parallel

Hello all, Using classical glm function with binomial family, I experienced a problem when using predict() over a glm object. I found that family objects built on R-2.14.0 contain elements like : .Call(C_logit_link,...) But on R-2.9.0, the object C_logit_link can not be found. Instead, this prior version used to call more simply : .Call("logit_link",...) which causes no problem. Is

Hello, I experienced a strange behaviour of callNextMethod when used in either initialize or any other S4 function method definition. Help says callNextMethod calls the next inherited method for the current function from where it is called with the same actual (non missing) arguments. This is OK. The problem appears when some formal arguments (in particular, S4 objects) of this function are

Dear expeRts, ./src/library/base/man/Random.Rd says that L'Ecuyer requires a seed of length 6. ./src/library/parallel/man/RngStream.Rd also mentions this, but only in the text part; In the "Arguments"-part, it says that "seed" has to be of length 7 Also: ,---- | > RNGkind("L'Ecuyer-CMRG") | > length(.Random.seed) | [1] 7 `---- Is the docu old? Some

In a vanilla R 2.14.0 GUI session (on Windows XP SP3): > library(parallel) > cl<-makePSOCKcluster(2) > RNGkind() [1] "Mersenne-Twister" "Inversion" > clusterSetRNGStream(cl) > RNGkind() [1] "L'Ecuyer-CMRG" "Inversion" > stopCluster(cl) Is it intentional that clusterSetRNGStream() changes the RNG kind in the master process?

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

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'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

This issue has already been addressed in 76462 (R-devel) and also ported to R-patched. In fact rmChild() is used in mccollect(wait=FALSE). Best Tomas On 5/19/19 11:39 AM, Sun Yijiang wrote: > 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"

Hello, Have been thinking that sometimes users may want each process to initialize its random seed with a specific value rather then the current seed. This could be keyed off depending whether mc.set.seed is logical, preserving the current behaviour, or numerical, using the value in a call to set.seed. Does this make sense? If you wonder how I came up with the idea: I spent a couple of hours

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

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

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__()

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

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

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.

Hello Giovanni, I don't know if my workflow would suit you but I tend to want the opposite when I launch a parallel process. I tend to want to keep the processes alive as long as they can. If the computation time is long I would not want to lose everything. lapply..8 <- function(X,FUN,...){ max..cores <- as.numeric(system("grep ^processor /proc/cpuinfo 2>/dev/null | wc

Thanks for the response, Jeremie. I wholeheartedly agree about testing. In my case, this feature would be used purely to reduce computation time. I?m calculating an expensive (and embarrassingly parallel) likelihood function, and for some parameter combinations my objective function diverges to infinity. In those instances, I?d like to throw an error that stops subsequent computations and that I

When I execute mclapply it creates the needed processes, but these processes never begin computing anything, they just wait indefinitely. I recently upgraded to version 2.9.1, which might have caused the problem. -- Med venlig hilsen Rune Schjellerup Philosof Ph.d.-studerende, Statistik, IST, SDU Telefon: 6550 3607 E-mail: rphilosof at health.sdu.dk Adresse: J.B. Winsl?wsvej 9, 5000 Odense

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

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