How do I install software packages on the RCE?

As a RCE user, you are only allowed to install certain packages and modules to your own home directory. We have documentation for install popular packages on the follow pages:

If you were redirected here from the RCE, you may have been trying to install a package using apt-get. This is a package manager for Debian-based systems, and isn't compatible with the RCE. If you need assistance with installing additional software that isn't available on the RCE, please contact us (or by phone: 617-495-4734) for support!

How to exit a job from a shell OR cannot start RCE job -- too many CPUs

The commands below will help you exit an RCE job from a terminal if the application window has disappeared, or you need to exit it and do not have access to your NX session. This can be accomplished by using an ssh client and connecting to rce.hmdc.harvard.edu, or opening a terminal window in the RCE. 

The same commands can be used If you are running into an error starting a new RCE job due to too many CPU's in use, you need to terminate your jobs that were tied to an old nx session. The commands that are highlighted below should enable you to remove these old jobs.

  1. Run this command to see your jobs:
    condor_q -pool cod6-head.priv your_username
    job_id  username  date_&_time   run_time   R  0   0.0  application_name

  2. Remove your job, using the job_id:
    condor_rm -pool cod6-head.priv -name HMDC.interactive@cod6-head.hmdc.harvard.edu job_id

What is the cost to use the RCE?

The RCE is free to use for qualified individuals in the pursuit of social science research, within certain limits.

If your needs exceed the capacities of the shared RCE environment, we can work with you to establish dedicated RCE resources for your exclusive use. If you are planning a large project, please contact us prior to your grant application so that we can assist in budgetary estimation.

The standard limits and costs of allowances in excess are outlined in the RCE Service Level Agreement (SLA).

How do I get an account, or get help?

For all inquiries related to the HMDC RCE, please contact our support helpdesk.

For fastest service, please email directly support@help.hmdc.harvard.edu. Or, you may also call 617-495-4734 (x54734) from 9AM-5PM weekdays.

If your question is not answered here on our RCE support website, you may also find the information you need on our User Services website, which covers topics relating to the HMDC RCE and other services provided by the IQSS Technology Services group.

For a list of all IQSS services, including statistical Research Technology Consulting and Public Computer Labs, please visit http://www.iq.harvard.edu/services.

Will MPlus be available on the RCE?

Unfortunately, licensing for MPlus is complex, and places an undue financial burden on HMDC. From the MPlus developers:

Mplus was not developed for more than one analysis to be run on the same computer at the same time.

This is the exact opposite behavior of what the RCE is designed for, so at this time we cannot offer MPlus on our research cluster.

Is there temp or scratch storage available?

Yes, the top-level /scratch directory on our batch nodes is the same as using /tmp. On our interactive nodes, /scratch is a separate 1TB shared storage space.

Top level scratch space is world-writeable and -readable (Unix 1777 permissions). User created directories are only owner writeable/readable (1700) or owner/group (2770) if you are a member of a research group.

In either case, if you need several gigabytes of storage, please request a project directory.

Please note:

  • Do not store any Confidential Information in scratch/temp.
  • Any files written to scratch/temp will be deleted after 2-4 weeks.
  • Do not use the scratch space for permanent storage.
  • Each server has a separate scratch/temp. If you need shared space for a distributed batch job, please request a project directory.

How do I use compressed data with R?

R supports two primary ways of accessing compressed data. This allows you to keep your data files on disk compressed saving space, and often time (since the file I/O saved by compression is often more expensive than the cpu cycles it uses).

If you are storing your data in native format, simply use the compress option of save:

tst.df=as.data.frame(cbind(1:10,2:11)) # just some testing data save(tst.df,file="test.Rbin", compress=T) # save a compressed R file

You can use load as normal, to read the compressed files:


To access any other kind of file with compression, simply use gzfile("") around the file name:

write.table(tst.df,gzfile("test.dat.gz")) # write a compressed file read.table(gzfile("test.dat.gz"),row.names=1)# read it back in

Files compressed using the gzfile method can also be compressed and uncompressed using the UNIX gzip and gunzip commands (respectively).