similar to: speed of makeCluster (package parallel)

Dear R community, I do know, that an R function is constructing a copy of any object passed as argument into a function. I program on a larger S4 project for a package, and I arrived at a point where I have to think a little harder on implementation style (especially to spare users complex object handling). I have a function foo(), taking as input arguments two S4 objects of different class

(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

Dear R users, Since a long time now, I have the following error when I want to load the tcltk library in R 64 bit. Loading Tcl/Tk interface ...Error : .onLoad failed in loadNamespace() for 'tcltk', details: call: inDL(x, as.logical(local), as.logical(now), ...) error: unable to load shared object 'C:/Program Files/R/R-2.13.0/library/tcltk/libs/x64/tcltk.dll': LoadLibrary

Is there anyway with some experience in using armadillo in R C++ extensions? My problem is the following: I programmed a function in a header looking like #include <armadillo> inline arma::vec foo(input) { ... do something return an arma::vec object } compiling this via R CMD INSTALL packagename (PKG_CXXFLAGS = -I/folder/of/armadillo and armadillo_bits in my package) I get the

Dear UseRs, Is there a way to define the lower-upper bounds for parameters fitted by optim using the Nelder-Mead method ? Thanks, Arnaud [[alternative HTML version deleted]]

Hi everyone, I am using package knitr, FIRST TIME. I don't have access to RStudio. Read through Yihui's page, didn't find it helpful. Stuck on terms Rnw, GFM (GitHub Flavored Markdown). Never used Sweave, so the reference is not helping. Is there a simple step-by-step example WITHOUT RStudio? My question: What is the procedure? The documentation explains the functions, but does

Hi Inaki, > "Performant"... in terms of what. If the cost of copying the data > predominates over the computation time, maybe you didn't need > parallelization in the first place. Performant in terms of speed. There's no copying in that example using `mclapply` and so it is significantly faster than other alternatives. It is a very simple and contrived example, but

Good day, I'm trying the example cl <- makeCluster(2, type = "SOCK") from the makeCluster documentation and the R command prompt never returns. I am using a 32-bit Windows 10 computer. The problem doesn't happen on another 64-bit Windows 10 computer but does happen on another 32-bit Windows 7 computer. Can anyone reproduce it? Are there any other options that can be passed to

Hi, In order to find the best models I use AIC, more specifically I calculate Akaike weights then Evidence Ratio (ER) and consider that models with a ER < 2 are equally likely. But the same problem remain each time I do that. I selected the best models from a set of them, but I don't know if those models are efficient to predict (or at least represent) my data. I can have selected the best

Thanks Simon, I will take note of the sensible default for core usage. I?m trying to achieve small scale parallelism, where tasks take 1-5 seconds and make fuller use of consumer hardware. Its not a HPC-worthy computation but even laptops these days come with 4 cores and I don?t see a reason to not make use of it. The goal for the current piece of code I?m working on is to bootstrap many

The problem causing the stray worker processes when the master fails to open a server socket to listen to connections from workers is not related to timeout in socketConnection(), because socketConnection() will fail right away. It is caused by a bug in checking the setup timeout (PR 17391). Fixed in 76275. Best Tomas On 3/18/19 2:23 AM, Henrik Bengtsson wrote: > (Bcc: CRAN) > >

Dear all, Since I installed the x64 version of R (v2.12.1), I got a problem with tcltk that I did not achieve to resolve. When loading the library, it gives me the following error message: Loading Tcl/Tk interface ...Error : .onLoad failed in loadNamespace() for 'tcltk', details: call: inDL(x, as.logical(local), as.logical(now), ...) error: unable to load shared object

Hi all, Is it possible to use the 'renice' option together with parallel clusters of type 'PSOCK'? The help page for parallel::makeCluster is not specific about which options are supported on which types and I am getting the following message when passing renice = 19 : > cl <- parallel::makeCluster(2, renice = 19) nice: ?+19?: No such file or directory Kind regards,

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 2) Removing fork would break existing workflows Even if replaced with something using the same interface (e.g., a function that automatically detects variables to export as in the amazing `future` package), the lack of copy-on-write functionality would

When using multicore-forking of the parallel package, is it possible for a child process to know that it is a fork? Something like: parallel::mclapply(1:10, FUN = function(i) { test_if_running_in_a_fork() }) I'm looking into ways to protect against further parallel processes (including threads), which not necessarily are created via the parallel:mc* API, are being spawned off recursively.

Dear list, I fitted the same GAM model using directly the function gam(mgcv) ... then as a parameter of another function that capture the warnings messages (see below). In the first case, there is no warning message printed, but in the last one, the function find two warning messages stating "matrix not positive definite" So my question is: Do I have to worry about those warnings and

I'm exploring latency overhead of parallel PSOCK workers and noticed that serializing/unserializing data back to the main R session is significantly slower on Linux than it is on Windows/MacOS with similar hardware. Is there a reason for this difference and is there a way to avoid the apparent additional Linux overhead? I attempted to isolate the behavior with a test that simply returns

Dear useRs, In a thesis, I found a mention of the use of pairwise deletion in linear regression and GLM (binomial family). The author said that he has used R to do the statistics, but I did not find the option allowing pairwise deletion in both lm and glm functions. Is there somewhere a package allowing that ? Thanks, Arnaud [[alternative HTML version deleted]]

Dear R-Users, I need a very fast and reliable database solution so I try to serialize a data.frame (to binary data) and to store this data to an SQLite database. This is what I tried to do: library(RSQLite) con <- dbDriver("SQLite") db <- dbConnect(con, "test") dbSendQuery(db, 'CREATE TABLE frames("simID" INT, "data" BLOB)') data.bin <-

Dear all, I?m working as an administrator of a High-Performance Computing (HPC) Cluster which runs on Linux. A lot of people are using R on this Linux cluster and, of course, the *parallel* package to speed up their computations. It has been our collective experience, that using |makeForkCluster| yields an overall better experience /on Linux/ than the |makePSOCKcluster|, for whatever definition