similar to: Problem with mpi.close.Rslaves()

Hi, I'm testing the efficiency of the Rmpi package regarding parallelization using a cluster. I've found and tried the task pull programming method, but even if it is described as the best method, it seems to cause deadlock, anyone could help me in using this method? here is the code I've found and tried: # Initialize MPI library("Rmpi") # Notice we just say "give us

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 I've written a function that uses Rmpi to perform a calculation in parallel. It works fine, but I'm trying to improve efficiency in terms of memory usage and the amount of data being passed back and forth between mater and slaves. Calculations are performed on a symmetrical matrix in order to zero-out some of the cells. In the parallel

Hi everybody! I have the next code which makes a reduction of the *a *variable in two slaves, using the Rmpi package. library(Rmpi) mpi.spawn.Rslaves(nslaves=2) reduc<-function(){ a<-mpi.comm.rank()+2 mpi.reduce(a,type=2, op="prod") return(paste("a=",a)) } mpi.bcast.Robj2slave(reduc) mpi.remote.exec(reduc()) cat("Product: ")

Hi everybody! I have the next code which makes a reduction of the *a *variable in two slaves, using the Rmpi package. library(Rmpi) mpi.spawn.Rslaves(nslaves=2) reduc<-function(){ a<-mpi.comm.rank()+2 mpi.reduce(a,type=2, op="prod") return(paste("a=",a)) } mpi.bcast.Robj2slave(reduc) mpi.remote.exec(reduc()) cat("Product: ")

Hi I am trying to utilize my dual core processor (and later a High-performance clusters (HPC) ) by using the Rmpi, snow, snowfall, ... packages, but I am struggling at the beginning, i.e. to initialise the "cluster" on my dual core computer. Whenever I try to initialize it (via sfInit(parallel=TRUE, cpus=2) or mpi.spawn.Rslaves(nslaves=2) ), I get an error message: >

Hello, I'm using R on a 10 blade dual quad core Rocks Cluster, and trying to use Rpmi and snow. I basically wondered if at the moment I ought to install Rmpi against another form of mpi (not openmpi) and wondered whether anyone could pass on any experience. I'm mainly worried about (a) the R server taking up 100% cpu time (I think this is a known issue with Rmpi and openmpi) and (b)

Dear R list, In the course of learning to work with Rmpi, we are confused about a few points. The following simple program is based on some examples we retrieved from the web. Each slave is writing the same output line multiple times (a multiple equal to the number of slaves). In other words, the write statements are being executed a number of times equal to the number of slaves. I am

I'm invoking R from withing a shell script like this R --no-save --no-restore --gui=none > `hostname` 2>&1 <<BYE # various commands here BYE I would like to regain control from the invoking terminal at some point. I tried source(stdin()) but got a syntax error, presumably stdin is the little shell here snippet (the part between <<BYE and BYE). Is there some way to

I'm aiming to compare the workings of Rmpi and multicore on a duel processor quad core machine with 64 bit R-2.11.1 Kubuntu 10.4. It's impossible for me to get a small reproducable code segment to show what I mean, but if I show what works for mclapply, I'd hope it's possible to be shown what would be the equivalent way with mpi.apply. The function lr.gbm has variables trees,

Hi there, In multiple regression one way to view R^2 is as (the square of) the correlation between original y's and the estimated y's. Suppose you fit a multilevel model with random intercept for each cluster. Would it be valid to compute an R^2 by using fixed effects plus the group intercepts to reduce the residuals? I suspect this has been done and, given its absence from the lmer

I have hundreds of megabytes of price data time series, and perl scripts that extract it to tab delimited files (I have C++ programs that must analyse this data too, so I get Perl to extract it rather than have multiple connections to the DB). I can read the data into an R object without any problems. thedata = read.csv("K:\\Work\\SignalTest\\BP.csv", sep = "\t", header =

Dear all, I would like to use the residuals in a general linear mixed effect model to diagnose model fit. I know that the resid function has been implemented for linear mixed models but not yet for general linear mixed effects. Is there a way to get them out of lmer fit objects? I tried searching the r-help archive and found nothing. I tried and failed to replicate what (I guessed would be

R-users, My question is related to earlier posts about benefits of quadcore over dualcore computers; I am trying to setup a cluster of windows xp computers so that eventually I could make use of 10-20 cpu:s, but for learning how to do this, I am playing around with two laptops. I thought that the package snow would come handy in this situation, but to use snow, I would probably need to install

Hello, I try to write parts of my code in C to accelerate the for-loops. But basic operations I want to do in R (e.g. start cluster). My R code looks something like this: library(Rmpi) mpi.spawn.Rslaves() mpi.remote.exec(....) dyn.load("test.so") erg <- .Call("test", ....) .... mpi.close.Rslaves() mpi.quit() And my C function looks something like this: #include

Hi, A simple question I hope. I wish to add a single vertical line to a plot with several density plots. Here is a simplified example. ############ thedata <- data.frame(x1=rnorm(100,1,1),x2=rnorm(100,3,1)) #create data thedata.m<-melt(thedata) densityplot(~value, thedata.m, groups=variable,auto.key=list(columns=2)) #this gives the two density plots ######### what I wish now is to add a

The problem: I would like to translate the Octave algorithm in griddata.m to R. Within the griddata algorithm calls are made to the Delaunay function. For the R translation I have found delaunayn within the "geometry" package and also the deldir package. Both do similar things but give slightly different results depending on the input. The question is, what is making the results for the

Hello r-experts, I sure could us a little help. I have an ever updating text file with timestamped data in it. I can reformat in anyway I want if need be but currently I have chosen to make columns of date, time and measuresed value (comma delimeted and with the dates and times in quotes to interpret them as strings). Here is a small section of my text data file:

Hello, # Full disclosure. I am not sure if my problem is a bug(s) in the code, or a fundamental misunderstanding on my part about what I am trying to do with these statistics. I am not familiar with maximum likelihood tests. # I currently have two vectors Aequipecten<-c(0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,

Hello, On Mac OS X, certain Aqua/Quartz UI functionality requires an application to be launched from within an app bundle, or (alternatively) requires a Carbon application with a resource fork. Playing with the wxWidgets distribution, I discovered that it is quite easy and transparent to make such a Carbon app from (I guess) any command line application. When applied to the R executable called

Hello, On Mac OS X, certain Aqua/Quartz UI functionality requires an application to be launched from within an app bundle, or (alternatively) requires a Carbon application with a resource fork. Playing with the wxWidgets distribution, I discovered that it is quite easy and transparent to make such a Carbon app from (I guess) any command line application. When applied to the R executable called