• The sample R program is from ​here
  • Original R Program of 10 loops (without Rmpi)

    start_time <- Sys.time()
    # first make some data
    n <- 1000 # number of obs
    p <- 30 # number of variables
    
    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)
    
    # summary(lm(y~x))
    
    fold <- rep(1:10,length=n)
    fold <- sample(fold)
    
    rssresult <- matrix(0,p,10)
    
    for (j in 1:10) {
        for (i in 1:p) {
            templm <- lm(y~.,data=thedata[fold!=j,1:(i+1)])
            yhat <- predict(templm,newdata=thedata[fold==j,1:(i+1)])
            rssresult[i,j] <- sum((yhat-y[fold==j])^2)
        }
    }
    
    end_time <- Sys.time()
    runTime<-difftime(end_time,start_time,units="secs")
    write(runTime,file="single_10_runTime",append=TRUE)
    write(rssresult,file="single_10_result")
    q(save="no")
    
    

  • Parallel R Program of 10 loops (with Rmpi)

    # Create Start Timer
    start_time<-Sys.time()
    
    # Initialize MPI
    library("Rmpi")
    
    # Notice we just say "give us all the slaves you've got."
    mpi.spawn.Rslaves(nslaves=2)
    
    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))
    end_time<-Sys.time()
    runTime<-difftime(end_time,start_time,units="secs")
    write(runTime, file="parallel_10_runTime",append=TRUE)
    write(rssresult, file="parallel_10_result")
    mpi.close.Rslaves()
    mpi.quit(save="no")
    
    

• We could find that there double loops in the sample program, one is j, and the other is p.
  So we control one of the loop (either j or p) variable, and see how fast it is when the Rmpi apply in it.
  In the meanwhile, we compare how much does the performance improve when the number of slaves are 3 and 6.
  
• The running times(secs) for variable j of sample program (p is 30) are as below:

  j(times of loop)	single	        paralle with 3 slaves	parallel with 6 slaves
  10	                7.319691	3.738059	        2.707863
  50	                33.96304	11.76658	        6.84227
  100	                71.7085	        22.68779	        11.85747
  150	                111.417	        32.51246	        16.98184
  200	                142.6771	42.51736	        22.92909
  250	                176.2949	53.08824	        27.35953
  

• The running times(secs) for variable p of sample program (j is 10) are as below:

  p(times of loop)	single	        paralle with 3 slaves	parallel with 6 slaves
  30	                6.462582	3.91415	                2.550209
  60	                22.9513	        9.87096	                5.806265
  90	                56.07782	20.56819	        11.25752
  120	                90.7383	        35.26347	        18.84071
  150	                154.7837	54.48117	        28.72215
  

• The Curve is as below:
  The Y-aix represents runtime of both single mode and parallel mode.(units: seconds)
  The X-aix represents how many times of loop set by us in the sample program
  For variable j :
  

For variable p :

• We could proof that Rmpi will let you program run faster and faster when times of loop larer and larer.