search for: parappli

I'm using /parApply/() function in "snow" package for parallel computing (boostrapping repeating calculation), as the follows: MyBoostrapping <- function( i ){ ..... } cl <- makeCluster( ncluster, type = "SOCK") i.circle <- as.matrix( 1:128, , 1) parApply( cl, i.circle, 1, FUN = MyBoostrapping) It works. However, I want to set more than one arguments to

I've started to use the parallel package and it works very well speeding things up. Thank you for making this easy to do. Should I have expected that parCapply would return a vector when parApply returns a matrix? library(parallel) x <- matrix(rnorm(8), nc = 2) apply(x, 2, function(y) y) [,1] [,2] [1,] -0.9649685 0.91339851 [2,] -1.4313140 0.13457671 [3,] 1.0499248

Hi I try parallelising some code using the snow package and the following lines: cl <- makeSOCKcluster(8) pfunc <- function (x) (if(x <= (-th)) 1 else 0) ###correlation coefficient clusterExport(cl,c("pfunc","th")) cor.c.f <- parApply(cl,tms,c(1,2),FUN=pfunc) The parApply results in the error message: > cor.c.f <- parApply(cl,tms,c(1,2),FUN=pfunc) Error

Hello All, I can run the "lower level" functions OK, but many of the higher level (eg. parSApply) functions are generating errors. When running the example (from the snow help docs) for parApply on MacOSX 10.5.5, I get the following error: cl <- makeSOCKcluster(c("localhost","localhost")) sum(parApply(cl, matrix(1:100,10), 1, sum)) Error in

I've been using parApply() in snow package for parallel computing with the following lines in R 2.8.1: library(snow) nNodes <- 4 cl <- makeCluster(nNodes, type = "SOCK") fm <- parApply(cl, myData, c(1,2), func1, ...) Since I have a Mac OS X (version 10.4.11) with two dual-core processors, I thought that I could run 4 simultaneous clusters. However with the 1st job

Hi Greg, I don't know if this is related to your problem, but I get the same error (on both ubuntu and fedora linux, R 2.9) and just found a very curious behaviour - snowfall apply functions don't like the variable name "c". E.g.: c<-1 sfLapply(1:10, exp) issues the same error you had posted, while subsequent rm("c") sfLapply(1:10, exp) runs fine. Rainer

invoking parLapply without a cluster fails to find a previously registered cluster > library(parallel) > setDefaultCluster(makePSOCKcluster(2)) > parLapply(X=1:2, fun=function(...) {}) Error in cut.default(i, breaks) : invalid number of intervals This is because in parLapply length(cl) is determined before defaultCluster(cl) is called. By inspection, this appears to be true of

Hi, Here are the issues I found: Typos ----- (a) Found: It a parallel version of ?evalq?, "is" missing. (b) Found: 'parLapplyLB', 'parSapplyLB' are load-balancing versions, intended for use when applying ?FUN? to 'parLapplyLB' has no 'FUN' arg (more on this below). (c) Found: 'clusterApply' calls 'fun' on the first

Dear all, I have used the Rmpi package many times before however this time I've installed it as I always do with openMPI tar.gz file direct from the website. I'm installing on my ubuntu 8.10. Linux 2.6.27-11-generic #1 SMP Thu Jan 29 19:28:32 UTC 2009 x86_64 GNU/Linux All i get is: > library(Rmpi) Segmentation fault :~$ Which dumps me back into the shell, and doens't give me much

(Posted also to debian-user) In my office network I have access to a debian powerpc server and 2 freebsd 6 servers (actually one of them is my notebook). Experienced user of the statistical software R, I have now a go at parallel computation via (r-)pvm and snow under R to enhance the performance of a heavy duty statistical problem involving many iteration on the calculation of models. I moved

Hello, The problem I'm working now requires to operate on big matrices. I've noticed that there are some packages that allows to run some commands in parallel. I've tried snow and NetWorkSpaces, without much success (they are far more slower that the normal functions) My problem is very simple, it doesn't require any communication between parallel tasks; only that it divides

Many thanks to Dr Yu for updating Rmpi for R 2.6.0, and for starting to make the changes to support Open MPI. I have just built the updated Debian package of Rmpi (i.e. r-cran-rmpi) under R 2.6.0 but I cannot convince myself yet whether it works or not. Simple tests work. E.g. on my Debian testing box, with Rmpi installed directly using Open Mpi 1.2.3-2 (from Debian) and using 'r' from

Dear R yet again! I finally got the Rmpi package to install! Yay!. However, when I go into R to use it, there is an error with the dyn.load file. This should be simple (allegedly). Please help. Thanks, Sincerely, Erin mailto: hodgess at gator.uhd.edu /home/faculty/hodgess/R-2.6.0/bin [hodgess at gator bin]$ ./R CMD INSTALL Rmpi_0.5-4.tar.gz

Dear R users, we are trying to do some parallel computing using library(snow). In particular we have a cluster with 3 nodes >cl <- makeCluster(3, type = "MPI") 3 slaves are spawned successfully. 0 failed. and we want to compute the function op_mat (see below) first with the master and then with the cluster using system.time for checking the computational performance.

V; Sent via BlackBerry from T-Mobile -----Original Message----- From: r-help-request at r-project.org Date: Tue, 30 Sep 2008 12:00:06 To: <r-help at r-project.org> Subject: R-help Digest, Vol 67, Issue 31 Send R-help mailing list submissions to r-help at r-project.org To subscribe or unsubscribe via the World Wide Web, visit https://stat.ethz.ch/mailman/listinfo/r-help or, via email,