similar to: Mclapply and print statement

Dear all, In my 'simple' computer I was running some experiments to help me understand how faster a multicore lapply will be. I thought it might be interesting for some people to look at the results. Even though are not accurate, still might be a good indicator how much improvement there can be. A.Case. The classic: for 1:100 for (i in c(1:dimz)){ print(sprintf('Creating the %d

Dear all, I would like to ask your help understand the subsequent steps for making my program faster. The following code: Gauslist<-array(data=NA,dim=c(dimx,dimy,dimz)) for (i in c(1:dimz)){ print(sprintf('Creating the %d map',i)); Gauslist[,,i]<-f <- GaussRF(x=x, y=y, model=model, grid=TRUE,param=c(mean,variance,nugget,scale,Whit.alpha)) } creates 100 GaussMaps (each

Dear all I have the following code that works in paraller (and is pretty fast actually) The only problem I still have is that every core just prints only once. dimz<-1000 Shadowlist<-mclapply(1:dimz, function(i) { ????????????????????????????? print(sprintf('Creating the %d map',i)); ????????????????????????????? createField(x=x, y=y, model=model,

Dear all I am looking for a shorter way and more elegant to write the following for (i in c(1:length(Shadowlist))){ filename<-paste('/home/apa/maps/',model,i,'.mat',sep="") varname<-paste(model,'_shadow',i,sep="") eval(parse(text=paste('writeMat(filename,',varname,'=Shadowlist[[i]])',sep=""))) } actually I do not

Hi, Please see the email exchanges below. I am having trouble generating output that is large enough for our needs, specifically when using the GaussRF function. However, when I wrote Dr. Schlather (the author of the GaussRF function), he indicated that there is also a limit imposed by R itself. Is this something that we can overcome? Thank you very much for any assistance you may provde.

Dear all, I am trying to learn lapply. I would like, as a test case, to try the lapply alternative for the Shadowlist<-array(data=NA,dim=c(dimx,dimy,dimmaps)) for (i in c(1:dimx)){ Shadowlist[,,i]<-i } ---so I wrote the following--- returni <-function(i,ShadowMatrix) {ShadowMatrix<-i} lapply(seq(1:dimx),Shadowlist[,,seq(1:dimx)],returni) So far I do not get same results

Hi, I'm using RF to simulate a correlated variable with GaussRF set.seed=1 GaussRF(sim.kfinegrid, grid=F, model="exponential", param=c(0,0.5,0,0.2)) However when I simulate again using the same random seed I get different results. > set.seed=1 > summary(GaussRF(sim.kfinegrid, grid=F, model="exponential", param=c(0,0.5,0,0.2))) Min. 1st Qu. Median Mean 3rd

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

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

I have asked the same question on stackoverflow but did not get a satisfying answer. I am trying to simulate a lognormal spatial random field but I need the simulated value in a certain range. So I need some easy to use functions to generate a truncated Gaussian field to start with. To be specific, I need a function like GaussRF from the RandomFields package or grf from the geoR package to

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

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 R-devel friends: I'm back to bother you again about the conflict between mclapply and tcltk. I've been monitoring several packages that want to use mclapply to parallelize computations and need to figure out what should be done. It appears tcltk cannot be safely unloaded, so the best we can do is check for the presence of tcltk and stop if it is found before mclapply() is used. I

On Sat, 13 Apr 2019 at 03:51, Kevin Ushey <kevinushey at gmail.com> wrote: > > I think it's worth saying that mclapply() works as documented Mostly, yes. But it says nothing about fork's copy-on-write and memory overcommitment, and that this means that it may work nicely or fail spectacularly depending on whether, e.g., you operate on a long vector. -- I?aki ?car

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

Hello, I am running large simulations, which unfortunately I can't really replicate here because the code is so extensive. I rely heavily on mclapply, but I realize that I'm losing data somewhere. There are two worrisome symptoms: 1) I am getting 'NULL' as a return value for some (but not all) elements of the output when I use mclapply, but not if I use lapply > tmp2[1:3]

dear r experts---Is there a multicore equivalent of by(), just like mclapply() is the multicore equivalent of lapply()? if not, is there a fast way to convert a data.table into a list based on a column that lapply and mclapply can consume? advice appreciated...as always. regards, /iaw ---- Ivo Welch (ivo.welch at gmail.com)

Dear all, I got an issue trying to parse excel files in parallel using XLConnect, the process hangs forever. Martin Studer, the maintainer of XLConnect kindly investigated the issue, identified rJava as a possible cause of the problem: This does not work (hangs): library(parallel) require(rJava) .jinit() res <- mclapply(1:2, function(i) {

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

Dear R-devel, I am experiencing issues with running GAM models using mclapply, it fails to return any values if the data input becomes large. For example here the code runs fine with a df of 100 rows, but fails at 1000. library(mgcv) library(parallel) > df <- data.frame( + x = 1:100, + y = 1:100 + ) > > mclapply(1:2, function(i, df) { + fit <- gam(y ~ s(x, bs =