Hi,
there is a new mailing list for R and HPC: r-sig-hpc at r-project.org
This is probably a better list for your question. Do not forget, first
of all you have to register: https://stat.ethz.ch/mailman/listinfo/r-sig-hpc
I tried your code and it is working!
Please send us your sessionInfo() output. Probably you use some old
package versions?
Is this your first Rmpi code? Is other code working?
You can try something like this to get output from all nodes.
library("Rmpi")
mpi.spawn.Rslaves(nslaves=3)
mpi.remote.exec(paste("I am
node",mpi.comm.rank(),"of",mpi.comm.size(),"on",Sys.info()["nodename"]))
mpi.remote.exec(sessionInfo())
mpi.close.Rslaves()
mpi.quit()
For some more debugging you can start your cluster with log output:
mpi.spawn.Rslaves(nslaves=3, needlog=TRUE)
Then there should be logfiles for every node in your working directory.
Best
Markus
Daniel Ferrara wrote:> 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 all the slaves you've got."
> mpi.spawn.Rslaves()
>
> if (mpi.comm.size() < 2) {
> print("More slave processes are required.")
> mpi.quit()
> }
>
> .Last <- function(){
> if (is.loaded("mpi_initialize")){
> if (mpi.comm.size(1) > 0){
> print("Please use mpi.close.Rslaves() to close
slaves.")
> mpi.close.Rslaves()
> }
> print("Please use mpi.quit() to quit R")
> .Call("mpi_finalize")
> }
> }
>
> # Function the slaves will call to perform a validation on the
> # fold equal to their slave number.
> # Assumes: thedata,fold,foldNumber,p
> foldslave <- function() {
> # Note the use of the tag for sent messages:
> # 1=ready_for_task, 2=done_task, 3=exiting
> # Note the use of the tag for received messages:
> # 1=task, 2=done_tasks
> junk <- 0
>
> done <- 0
> while (done != 1) {
> # Signal being ready to receive a new task
> mpi.send.Robj(junk,0,1)
>
> # Receive a task
> task <- mpi.recv.Robj(mpi.any.source(),mpi.any.tag())
> task_info <- mpi.get.sourcetag()
> tag <- task_info[2]
>
> if (tag == 1) {
> foldNumber <- task$foldNumber
>
> rss <- double(p)
> for (i in 1:p) {
> # produce a linear model on the first i variables on
> # training data
> templm <- lm(y~.,data=thedata[fold!=foldNumber,1:(i+1)])
>
> # produce predicted data from test data
> yhat <-
> predict(templm,newdata=thedata[fold==foldNumber,1:(i+1)])
>
> # get rss of yhat-y
> localrssresult <-
sum((yhat-thedata[fold==foldNumber,1])^2)
> rss[i] <- localrssresult
> }
>
> # Send a results message back to the master
> results <- list(result=rss,foldNumber=foldNumber)
> mpi.send.Robj(results,0,2)
> }
> else if (tag == 2) {
> done <- 1
> }
> # We'll just ignore any unknown messages
> }
>
> mpi.send.Robj(junk,0,3)
> }
>
> # We're in the parent.
> # first make some data
> n <- 1000 # number of obs
> p <- 30 # number of variables
>
> # Create data as a set of n samples of p independent variables,
> # make a "random" beta with higher weights in the front.
> # Generate y's as y = beta*x + random
> x <- matrix(rnorm(n*p),n,p)
> beta <- c(rnorm(p/2,0,5),rnorm(p/2,0,.25))
> y <- x %*% beta + rnorm(n,0,20)
> thedata <- data.frame(y=y,x=x)
>
> fold <- rep(1:10,length=n)
> fold <- sample(fold)
>
> summary(lm(y~x))
>
> # Now, send the data to the slaves
> mpi.bcast.Robj2slave(thedata)
> mpi.bcast.Robj2slave(fold)
> mpi.bcast.Robj2slave(p)
>
> # Send the function to the slaves
> mpi.bcast.Robj2slave(foldslave)
>
> # Call the function in all the slaves to get them ready to
> # undertake tasks
> mpi.bcast.cmd(foldslave())
>
>
> # Create task list
> tasks <- vector('list')
> for (i in 1:10) {
> tasks[[i]] <- list(foldNumber=i)
> }
>
> # Create data structure to store the results
> rssresult = matrix(0,p,10)
>
> junk <- 0
> closed_slaves <- 0
> n_slaves <- mpi.comm.size()-1
>
> while (closed_slaves < n_slaves) {
> # Receive a message from a slave
> message <- mpi.recv.Robj(mpi.any.source(),mpi.any.tag())
> message_info <- mpi.get.sourcetag()
> slave_id <- message_info[1]
> tag <- message_info[2]
>
> if (tag == 1) {
> # slave is ready for a task. Give it the next task, or tell it
tasks
>
> # are done if there are none.
> if (length(tasks) > 0) {
> # Send a task, and then remove it from the task list
> mpi.send.Robj(tasks[[1]], slave_id, 1);
> tasks[[1]] <- NULL
> }
> else {
> mpi.send.Robj(junk, slave_id, 2)
> }
> }
> else if (tag == 2) {
> # The message contains results. Do something with the results.
> # Store them in the data structure
> foldNumber <- message$foldNumber
> rssresult[,foldNumber] <- message$result
> }
> else if (tag == 3) {
> # A slave has closed down.
> closed_slaves <- closed_slaves + 1
> }
> }
>
>
> # plot the results
> plot(apply(rssresult,1,mean))
>
> mpi.close.Rslaves()
> mpi.quit(save="no")
>
>
> Thanks for your help!!!
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
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--
Dipl.-Tech. Math. Markus Schmidberger
Ludwig-Maximilians-Universit?t M?nchen
IBE - Institut f?r medizinische Informationsverarbeitung,
Biometrie und Epidemiologie
Marchioninistr. 15, D-81377 Muenchen
URL: http://www.ibe.med.uni-muenchen.de
Mail: Markus.Schmidberger [at] ibe.med.uni-muenchen.de
Tel: +49 (089) 7095 - 4599