similar to: convert apply to lappy

The following program is whittled down from a much larger program that always works on Intel, and always works on AMD's threadripper with lapply but not mclappy. With mclapply on AMD, all processes go into "suspend" mode and the program then hangs. This bug is replicable on an AMD Ryzen Threadripper 2950X 16-Core Processor (128GB RAM), running latest ubuntu 18.04. The R version

Hello, I spent a lot of a time on a weird bug, and I just managed to narrow it down. In parallel code (here with parallel::mclappy, but I got it doMC/multicore too), if the library(tcltk) is loaded, R hangs when trying to open a DB connection. I got the same behaviour on two different computers, one dual-core, and one 2 xeon quad-core. Here's the code: library(parallel) library(RSQLite)

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 quite large code (with lapply and mclapply) and I am getting the following error. Error: serialization is too large to store in a raw vector Is it possible to ask from R to extend the Error messages with more details? I would like to see where this problem exists. B.R Alex [[alternative HTML version deleted]]

Dear all I would like to ask you if an assignment can be done inside a lapply statement. For example I would like to covert a double nested for loop for (i in c(1:dimx)){ for (j in c(1:dimy)){ Powermap[i,j] <- Pr(c(i,j),c(PRX,PRY),f) } } to something like that: ij<-expand.grid(i=seq(1:dimx),j=(1:dimy)) unlist(lapply(1:nrow(ij),function(rowId) { return

Dear all, I am trying to multiply three different matrices and each matrice is of size 16384,16384 the normal %*% multiplciation operator has not finished one day now. As I am running a system with many cores (and it seems that R is using only one of those) I would like to write fast a brief function that converts the typical for loops of a matrix multiplication to a set of lapply sets (mclapply

Dear all I am using lapply (actually mclapply that share the same syntax). I want to call the same function that takes as input a vector. My initial data structure is a matrix that I want to cut it to multiple vectors (one vector for every row of the matrix) and then feed that to the function by using mclapply. Could you please help me converting the matrices to nrow times vectors. I would

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

Dear all, I have a function that recognizes the following format for timestamps "%Y-%m-%d %H:%M:%S" my function takes two input arguments the TimeStart and TimeEnd I would like to help me create the right list with pairs of TimeStart and TimeEnd which I can feed to lapply (I am using mclapply actually).  For every lapply I want two inputs to be fed to my function. I only know how to

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

Dear all, I am using my code the vgram.matrix of packets fields. I have around 500 matrices that I need to pass inside that function and then plot those results. Even though my system has 16 cores is quite clear that I am only using one of those. Would it be able to skip these 500 "tasks" to the 16 cores, with each processor having around 4 matrices to process? What would you suggest

I am trying to divide the columns of a matrix by the first row in the matrix. I have tried to get this using apply and I seem to be missing a concept regarding the apply w/o calling a function but rather command args %*% / etc. Would using apply be more efficient than this approach? I have observed examples in the archives using this type of approach. Does anybody have a snippet of a call

Dear all I have wrote the following line return(as.vector(lapply(as.data.frame(data),min,simplify=TRUE))); I want the lapply to return a vector as it returns a list with elements as shown below List of 30001 $ V1 : num -131 $ V2 : num -131 $ V3 : num -137 $ V4 : num -129 $ V5 : num -130 as you can see I have already tried the simplify=TRUE and also the as.vector() but both

Dear all, I have a piece of code that  I want to run in parallel (I am working in system of 16 cores) foreach (i=(seq(-93,-73,length.out=21))) %dopar%  {           threshold<-i            print(i)          do_analysis1(i,path)          do_analysis2(i,path)            do_something_else_analysis1(i,path)            something_else_now(i,path)  } as you can see I have already tried to make

On 4 April 2019 at 17:28, ivo welch wrote: | The following program is whittled down from a much larger program that | always works on Intel, and always works on AMD's threadripper with | lapply but not mclappy. With mclapply on AMD, all processes go into | "suspend" mode and the program then hangs. This bug is replicable on an | AMD Ryzen Threadripper 2950X 16-Core Processor (128GB

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, two days ago I was trying to run a bunch of adaptIntegrate() functions (double integrals) into my 4 core pc. As I was not satisfied about my pc's performance I tried also to run my code to another computer that has 8 or 16 cores. Unfortunately I didnt get any really decent improvement . In both computer only the first core was looking busy while the rest were close to idle levels.

Hello all I would like to ask your help use mapply. I have a function called findCell that takes two arguments(x,sr) where x is a vector of size two (e.g x<-c(2,3) and sr is a matrix. I would like to call many times the findCell function (thus I need mapply) for different x inputs but always for the same sr. as x is a vector of size two (two cells) I want to pass inside inside the following

Dear all, I would like to task you if you know a rle version that can work also in a non consecutive way too. B.R Alex [[alternative HTML version deleted]]

Dear all, I have a variable that I would like also to use it as a string. The reasons is that I want to collect results from different function to one table.. So when I use the  colnames(mymatrix) <-c(function1.function2,function3) the function1, function2, function3 to be "converted" to simple strings so as  colnames(mymatrix)