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For map/reduce style computations, look at Rhipe or Segue. R at 12,000 Cores describes the “Programming with Big Data in R” project (pbdR). For batch jobs, Starcluster may be a better choice. Brian Holt (Unlicensed) wrote up a document on using R on starcluster called /wiki/spaces/IT/pages/7867417. Other documents give an overview of Distributed Computation Strategy and how to run Distributed Compute Jobs.

Starting a cluster with Bioconductor and Cloud Formation

The BioConductor group has put together a Cloud Formation stack for doing interactive parallel computing in R on Amazon AWS. Follow those instructions, selecting the number of workers and size of the EC2 instances. Once the stack comes up, which took about 10 minutes for me, you log into RStudio on the head node. You'll start R processes on the worker nodes and send commands to the workers.

stack name: StartBioCParallelClusterWithSSH
template url: https://s3.amazonaws.com/bioc-cloudformation-templates/parallel_cluster_ssh.json

Note that the parallel package is perfectly happy starting up several copies of R on a single machine, which can be helpful for testing.

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After starting the cloud formation script, look at the "Outputs" tab in the AWS console. You'll get a URL for a head-node running R-Studio and login information. Click on the URL, log into R-Studio and continue...

Connecting to workers

The IP addresses of the workers (and the head node) get stored on the head node in a file. We'll read that file and create an R process for each core on each worker.

# grab host IPs
lines <- readLines("/usr/local/Rmpi/hostfile.plain")

# we'll want to start a worker for each core on each
# machine in the cluster
hosts <- do.call(c, lapply(strsplit(lines, " "), function(host) { rep(host[1], as.integer(host[2])) }))

library(parallel)
help(package=parallel)

cl <- makePSOCKcluster(hosts)

Note that the parallel package is perfectly happy starting up several copies of R on a single machine, which can be helpful for testing.

Simple tests

Try a few simple tests to make sure we're able to evaluate code on the workers and that it buys us some speed.

# try something simple
ans <- unlist(clusterEvalQ(cl, { mean(rnorm(1000)) }), use.names=F)

# test a time-consuming job
system.time(ans <- clusterEvalQ(cl, { sapply(1:1000, function(i) {mean(rnorm(10000))}) }))

# do the same thing locally
system.time(ans2 <- sapply(1:(1000*length(hosts)), function(i) {mean(rnorm(10000))}))

# use load balancing parallel lapply
n <- length(cl)*1000
system.time(ans <- parLapplyLB(cl, 1:n, function(x) { mean(rnorm(10000)) }))

Head node vs. workers

Be aware of when you're running commands on the head node and when commands are running on the workers. Many commands will be better off running on the head node. When it's time to do something in parallel, you'll need to ship data objects to the workers, which is done with clusterExport, something like the following pattern:

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It might be necessary to modify the library path. If you try to install packages on the workers and get an error to the effect that the workers "cannot install packages", you need to do this.

# set cran mirror
clusterEvalQ(cl, { options(repos=structure(c(CRAN="http://cran.fhcrc.org/"))) })

# set lib path to install packages

clusterEvalQ(cl, { .libPaths( c('/home/ubuntu/R/library', .libPaths()) ) })

clusterEvalQ(cl, {
    install.packages("someUsefulPackage")
    require(someUsefulPackage)
})

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Sage packages

clusterEvalQ(cl, {
    options(repos=structure(c(CRAN="http://cran.fhcrc.org/")))
    source('http://depot.sagebase.org/CRAN.R')
 
    pkgInstall("synapseClient")
    pkgInstall("predictiveModeling")
     
    library(synapseClient)
    library(predictiveModeling)
})

Loading synapse entities

Logging in.Logging workers into synapse:

clusterEvalQ(cl, { synapseLogin('joe.user@mydomain.com','secret') })

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clusterEvalQ(cl, {
    if (!exists('expr')) {
        Sys.sleep(runif(1,0,5))
        expr_entity <- loadEntity('syn269056')
        expr <- expr_entity$objects$eSet_expr
    }
})

Attaching a shared EBS volume

It might be worth looking into attaching a shared EBS volume and adding that to R's .libPaths(). See Configuration of Cluster for Scientific Computing for an example of connecting a shared EBS volume in StarCluster. How to do this in the context of a cloud formation stack is yet to be figured out.

In general, attaching and using an EBS volume can be done like so (from StackOverflow Add EBS to ubuntu EC2 instance):

1. Create EBS volume in the EC2 section of the AWS console.
2. Attach EBS volume to `/dev/sdf` (EC2's external name for this particular device number).

3. Format file system `/dev/xvdf` (Ubuntu's internal name for this particular device number):

   Code Block
   sudo mkfs.ext4 /dev/xvdf

4. Mount file system (with update to /etc/fstab so it stays mounted on reboot):

   Code Block
   sudo mkdir -m 777 /vol echo "/dev/xvdf /vol auto noatime 0 0" | sudo tee -a /etc/fstab sudo mount /vol

To mount an existing EBS volume, attach the volume to your instance in the AWS Console, then mount it:

sudo mkdir -m 777 /vol
sudo mount /dev/xvdf /vol

Like a real hard-drive, EBS volumes can only be attached to a single instance. But, they can be shared by NFS. <<How to do this?>>

Accessing source code repos on worker nodes

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stopCluster(cl)

Don't forget to delete the stack in the AWS administration console to avoid continuing charges.

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• Spot instances? Is this worthwhile for interactive use?
• Create our own Cloud Formation template
• Attach a shared EBS volume
• Run a user-specified script on start-up

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