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!!!
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