similar to: problem with parLapply from snow

Hello everybody, I've got a bit of a problem with parLapply that's left me scratching my head today. I've tried this in R 2.11 and the 23 bit Revolution R Enterprise and gotten the same result, OS in question is Windows XP, the package involved is the snow package. I've got a list of 20 rain/no rain (1/0) situations for these two stations i and j, all the items in this list look

Dear R gurus, I have a very embarrassingly parallelizable job that I am trying to speed up with snow on our local cluster. Basically, I am doing ~50,000 t.test for a series of micro-array experiments, one gene at a time. Thus, I can easily spread the load across multiple processors and nodes. So, I have a master list object that tells me what rows to pick up for each genes to do the t.test from

From: Rock <zimilo@code-trick.com> --- fs/btrfs/backref.c | 10 ++-- fs/btrfs/qgroup.c | 16 +++--- fs/btrfs/send.c | 2 +- fs/btrfs/ulist.c | 154 +++++++++++++++++++++++++++++++++++++--------------- fs/btrfs/ulist.h | 45 ++++++++++++--- 5 files changed, 161 insertions(+), 66 deletions(-) diff --git a/fs/btrfs/backref.c b/fs/btrfs/backref.c index ff6475f..a5bebc8

this is the command i made for a normal distribution, but when i try to plot the histograms, i dont know why the bars don't stick on the line... nsamples<-1000 sampsize<-15 Samples<-matrix(rnorm(nsamples*sampsize,0,1),nrow=nsamples) a<-apply(Samples,1,var) NC14<-a*14 x<-0:40 plot(x,dchisq(x,14),type='h') hist(NC14,freq=F,add=T) -- View this message in context:

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.

Hi, I am working on a 64-bit rocks cluster and am relatively new to the R package. I am trying to get Snow working with R and Rmpi and have run into the following issue. R is able to load the Rmpi and snow libraries and is able to run simple commands both interactively and batch as follows: -------------------------------------------------------------------------------------------------------

Hi, I am currently studying Decision Trees by using rpart package in R. I artificially created a data set which includes the dependant variable (y) and a few independent variables (x1, x2...). The dependant variable y only comprises 0 and 1. 90% of y are 1 and 10% of y are 0. When I apply rpart to it, there is no splitting at all. I am wondering whether this is because of the

I am trying to run a monte carlo process using snow with a MPI cluster. I have ~thirty processors to run the algorithm on and I want to run it 5000 times and take the average of the output. A very simple way to do this is to divide 5000 by the number of processors to get a number n and tell each processor to run the algorithm n times. I realize there are more efficient ways to manage the

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

Dear R users, I have some problems with the parLapply function from the "parallel" package: I use parLapply on a pretty big R object without changing the object within the called function. If I execute parLapply alone, everything works fine. It seems that the object resides only once in the memory. But if I use the same call within another function, the object seems to be multiplied to

The runtime of parallel::parLapply depends on variables unrelated to the parLapply call. However, this is not clearly documented. Therefore I would like to suggest expanding the relevant documentation to explain this behaviour. Consider this example: parallel_demo <- function(random_values_count) { some_data <- runif(random_values_count) dummy_function <- function(x) { x }

Dear R-user, I am trying to use the function 'parLapply' from the 'snow' package which is supposed to work the same wys as 'lapply' but for a parallelized cluster of computers. The function I am trying to call in parallel is 'dudi.pca' (from the 'ade4' package) which performs principal component analyses. When I call this function on a list of

I am slightly confused by the way the standard output is redirected in a R snow cluster environment. I am using parLapply from the snow package to execute a function on my MPI/LAM cluster. How can I redirect standard output (produced using "cat") from this function back to the terminal where I invoked it? I intend to transmit some status information in advance to the final result of the

Dear R-users I was running into problems with my R code trying to run clh sampling (clhs package) in parallel mode (=on various data sets simultaneously). Here is the code (which I developed with some help:)): ****************************************** library("clhs") library("snow") a <- as.data.frame(replicate(1000, rnorm(20))) b <- as.data.frame(replicate(1000,

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

R Version : 2.14.1 x64 Running on Windows 7 Connecting to a database on a remote Microsoft SQL Server 2012 The short form of my problem is the following. I have an unordered vectors of names, say: names<-c("A", "B", "A", "C","C") each of which have an id in a table in my db. I need to convert the names to their corresponding ids. I

Hi, I want to generate 10000 samples from normal distribution with replacement case and every sample size is 50. What should I do ? -- View this message in context: http://r.789695.n4.nabble.com/IMPORTANT-PLEASE-HELP-ME-tp4650676.html Sent from the R help mailing list archive at Nabble.com.

At office, using the internal LAN at my disposal, I'm having a go at parallel computing - to begin with - with pvm, rpvm & snow. The two boxes are as follows Remote machine uffbsd: CPU: Intel(R) Pentium(R) 4 CPU 2.00GHz (1994.13-MHz 686-class CPU) Origin = "GenuineIntel" Id = 0xf24 Stepping = 4 real memory = 260046848 (248 MB) This machine NbBSD: CPU: Mobile Intel(R)

Dear R users, Can anyone please tell me how to generate a large number of samples in R, given certain distribution and size. For example, if I want to generate 1000 samples of size n=100, with a N(0,1) distribution, how should I proceed? (Since I dont want to do "rnorm(100,0,1)" in R for 1000 times) Thanks for help Debbie

Hi all, I am trying to use snow package to do a parallel MCMC. I have read a few guides and articles, the following is that I came up with. When I run it I got the error message: Error in checkForRemoteErrors(lapply(cl, recvResult)) : 4 nodes produced errors; first error: could not find function "ui.Next" The data is a longitudinal data with few repeated readings on a number of