Re: [R] Rmpi task-pull
Thanks for your help! Strangely this code (it's not mine), seems to cause deadlock over my cluster, even if every node of the cluster is tested working. Anyway I tried a task pull method and that seems to work. Thanks again, Daniel 2008/11/7 Markus Schmidberger <[EMAIL PROTECTED]> > Hi, > > there is a new mailing list for R and HPC: [EMAIL PROTECTED] > 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 > > rssr
Re: [R] Rmpi task-pull
Hi, there is a new mailing list for R and HPC: [EMAIL PROTECTED] 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 remo
[R] Rmpi task-pull
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]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.