search for: mclappi

Dear all I want to convert a apply to lapply. The reason for that is that there is a function mclappy that uses exact the same format as the lapply function. My code looks like that mean_power_per_tip <- function(data) {         return((apply(data[,],2,MeanTip))); } where data is a [m,n] matrix. I would like to thank you in advance for your help B.R Alex [[alternative HTML 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)

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

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

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