search for: parallelli

Hi all, Henrik Bengtsson has done some fantastic work with {future} and, more importantly, greatly improved constructing and deconstructing a parallelized environment within R. It was with great joy that I saw Henrik slowly split off some functionality of {future} into {parallelly} package. Reading over the package?s README, he states: > The functions and features added to this package are

FWIW, there are indeed a few low hanging bug fixes in 'parallelly' that should be easy to incorporate into 'parallel' without adding extra maintenance. For example, in parallel::makePSOCKcluster(), it is not possible to disable SSH option '-l USER' so that it can be set in ~/.ssh/config. The remote user name will be the user name of your local machine and if you try to

I am looking for ways to add points to three different plots in parallel. I generate three scatter plots and name them as s3d1, s3d2 and s3d3 s3d1<-scatterplot3d(mtcars[,3],mtcars[,4],mtcars[,5],main="common",pch=20) s3d2<-scatterplot3d(mtcars[,3],mtcars[,4],mtcars[,5],main="common",pch=20)

If these are easy changes, maybe someone will incorporate them. You'll make the argument stronger for doing that if you can explain why it's better to do that than to keep them in parallely. Duncan Murdoch On 07/11/2020 1:39 p.m., Henrik Bengtsson wrote: > FWIW, there are indeed a few low hanging bug fixes in 'parallelly' > that should be easy to incorporate into

On 06/11/2020 4:47 p.m., Balamuta, James Joseph wrote: > Hi all, > > Henrik Bengtsson has done some fantastic work with {future} and, more importantly, greatly improved constructing and deconstructing a parallelized environment within R. It was with great joy that I saw Henrik slowly split off some functionality of {future} into {parallelly} package. Reading over the package?s README, he

Hi all, I am using R-3.0.1 under Linux platform to embed R into my C++ code. I am facing an error while executing more than 1 R-expressions parallelly. I am executing round(X) and abs(X) parallelly on a set of 50 input rows which resulted in segmentation fault after getting the following errors. Error: unprotect_ptr: pointer not found Error: argument to 'findVar' is not an environment

G'day Philipp, On Fri, 16 Feb 2024 17:33:13 +0100 Philipp Schneider <philipp-schneider at gmx.net> wrote: > Thanks for all the input. It's happening again. This time for the > packages "DBI", "parallelly", "segmented", "survival", "V8". So, > RStudio shows updates for those and updating them via RStudio leads > to this

Hey everyone, Thanks for all the input. It's happening again. This time for the packages "DBI", "parallelly", "segmented", "survival", "V8". So, RStudio shows updates for those and updating them via RStudio leads to this output: ``` > install.packages(c("DBI", "parallelly", "segmented", "survival",

Dear Sadam, ? Mon, 13 Jan 2025 14:34:20 +0000 "Huseni, Sadamhusen" <Sadamhusen.Huseni at genpact.com> ?????: > Issue Observed: The script hangs and does not complete execution > post-update. Previously, it took around two hours; now it runs for > over 17 hours without finishing. Do you still have the RHEL 7 machine? Could you please share the sessionInfo() output at

Hi R-developers In the package Parallel, the function parLapply(cl, x, f) seems to allow transmission of only one parameter (x) to the function f. Hence in order to compute f(x, y) parallelly, I had to define f(x, y) as f(x) and tried to access y within the function, whereas y was defined outside of f(x). Script: library(parallel) f <- function(x) { z <- 2 * x + .GlobalEnv$y # Try to

Hi Team, I am writing to bring to your attention an issue we have encountered following the recent update of our Linux server from RHEL 7 to RHEL 8. We have an R script designed to create demand forecasts utilizing parallel computing on the Linux server. Before the version update, the script executed successfully within approximately two hours. However, since updating to RHEL 8, the script has

Dear XAPIAN developers, I was using xapian to index large than 13 million document about Q & A(Quora similarly). I will share some performance data about indexing and searching, and I will seek some help for improving performance of searching. My computer has 8 i7 at 3.4G CPU and 16G memory, ubuntu 16.04. Dataset include about 13M document, each document will be cut into 35 term(Chinese

...iprocessing > (child processes, OpenMP threads, pthreads, forks, MPI, PVM, 'parallel' > clusters, <insert many more methods here>) in a single setting, but > there is currently no such setting, and it may be impossible to > implement. Particularly problematic may be nested parallellism: > sometimes desirable (e.g. 4-machine cluster, each machine in it using > OpenMP threads), sometimes undesired (e.g. your case). A single setting > is probably far from enough. > >> Does R even know whether a linked library is doing multi-processing? > > Unfortunately,...

Hi all, I recently found out that R does not utilize fully the Duo Core capability when you only run one instance of R. I did some number crunching today and it seems that if I only open 1 R console, it uses 50% of my CPU (either 50-50 or 100-0 on 2 cores). Then, I open the second instance and divide the work into two parts, and run them parallelly, they seems to utilize 100% of my CPU. So, my

Hi, Sorry for the hiatus, busy time at my university. :) After a false start and some (hopefully cogent) thought, I am now of the opinion that it will be better to have llvm natively support a somewhat different notion of parallel computation and have the frontend lower OpenMP directives (and possibly other such things) into the same. In short, I propose a intrinsic based approach which hinges

Hello, I am trying to see if the hypervisor can be used to detect deadlocks in the guest VMs. My goal is to detect if a guest OS is deadlocked, and if it is, then create a clone of the deadlocked OS without the locking condition, and letting the clone run. While the clone runs I am hoping to generate some hints that could tell me what caused the deadlock. I simulated a deadlock/hang

On Tue, 14 Aug 2012 10:22:35 +0300 Pekka Jääskeläinen <pekka.jaaskelainen at tut.fi> wrote: > On 08/13/2012 10:54 PM, Hal Finkel wrote: > > I had thought about uses for shared-memory OpenCL implementations, > > but I don't know enough about the use cases to make a specific > > proposal. Is your metadata documented anywhere? > > It is now a quick "brute

dear Ivan, THe present problem had been encountered by me also about 1 and a half years ago. You had solved the issue then. Can't we search this mail list according to some keywords? It helps people with problems already solved in the mailing list. THanking you, Yours sincerely, AKSHAY M KULKARNI ________________________________ From: R-help <r-help-bounces at

I met with the Propeller team today (we work for the same company but it was my first time meeting two members on the team:) ). One thing I have been reassured: * There is no general disassembly work. General disassembly work would assuredly frighten off developers. (Inherently unreliable, memory usage heavy and difficult to deal with CFI, debug information, etc) Minimal amount of plumbing work

Hi Ivan, I am sharing the session information as below. Also, I will be sharing the code which is utilized to do parallel computing. Note: we are using different version of R on Linux version 7 & 8. ###***Session information from Linux version 8 R version 4.4.1 (2024-06-14) Platform: x86_64-redhat-linux-gnu Running under: Red Hat Enterprise Linux 8.10 (Ootpa) Matrix products: default