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FarmShare

This wiki is intended for the users of FarmShare, the Stanford shared research computing environment: the "cardinal", "corn", and "barley" machines. For a general description of this service, and Stanford's shared computing policies, see the main service catalog page.


Most useful pages: Special:AllPages and Special:RecentChanges and User Guide and FAQ and FarmShare tutorial



Last 10 messages on Farmshare-Discuss mail list (this month)

Contents

Jaeho Shin
Jaeho Shin: [farmshare-discuss] valgrind on corn [?]
Jason Bishop
Jason Bishop: [farmshare-discuss] valgrind on corn [?]
Jaeho Shin
Jaeho Shin: [farmshare-discuss] valgrind on corn [?]
Jason Bishop
Jason Bishop: [farmshare-discuss] valgrind on corn [?]
Jason Bishop
Jason Bishop: [farmshare-discuss] Message from syslogd at corn12 [?]
Forest Olaf Peterson
Forest Olaf Peterson: [farmshare-discuss] Message from syslogd at corn12 [?]
Amrita Ray
Amrita Ray: [farmshare-discuss] Message from syslogd at corn12 [?]
Jaeho Shin
Jaeho Shin: [farmshare-discuss] valgrind on corn [?]
Jason Bishop
Jason Bishop: [farmshare-discuss] corn server down [?]
Yunxiao Zhang
Yunxiao Zhang: [farmshare-discuss] corn server down [?]

FarmShare is in a rolling upgrade to Ubuntu 13.04

Details and status can be found here: Ubuntu13TransitionGuide



How to connect

The machines are available for anyone with a SUNetID. Simply "ssh corn.stanford.edu" with your SUNetID credentials. The DNS name "corn.stanford.edu" actually goes to a load balancer and it will connect you to a particular corn machine (e.g. corn21) that has relatively low load.

The "barley" machines are designed to be used for high performance computing (HPC) and only accessible via a resource manager (currently Open Grid Scheduler). You cannot log in directly, but you can submit jobs from any corn. Storage dedicated for jobs running on the barley cluster is available via /farmshare/user_data/ on all corn and barley nodes. Sign up and email the farmshare-discuss mailing list if you have any questions or would like any info not listed here.

corn SSH fingerprint is:

 RSA key fingerprint is 0b:e7:b4:95:03:c1:1e:07:df:04:ca:a2:3d:8e:e3:37.

How to get help

email support

Huangsmacc.png
  • You can e-mail research-computing-support@stanford.edu
    • If you're e-mailing about a barley job, please mention that it's on barley and the job number.


SMACC office hours

Smacctable.jpg
  • You can come to office hours. Every Wednesday from noon-2PM in Huang basement in front of ICME door:
    • Have a computational or statistical problem that you need help with? Or maybe you have an account on Farmshare or Proclus, and so now what? You have a boatload of data to make sense of, but how? Wonder where you can do your research project, and who can help you? You know what you want to do – but how best to do it, you just aren’t sure. Help is here in the form of SMACC – Stat, Math, Algorithmic and Computational Consulting! Technical consultants from ICME, Research Computing, Statistics and IRiSS will be available to work with you each Wednesday, from noon-2 pm, in the basement of Huang (in front of ICME). One stop shopping for your scientific computing needs. Rather than poke around web sites and send mail to multiple groups, drop by to catch all of us at once.
    • Details are at SMACC

Alternatively, the SSDS campus group can provide one one one guidance for R, SAS and Stata. Please contact them directly if you have any questions about using those particular software packages.

FAQ


Hardware Resources

cardinal info

The "cardinal" machines are small VMs intended for long-running processes (on the order of days) that are not resource intensive, e.g. mail/chat clients. You could log in to a cardinal and run a screen/tmux session there to do things on other machines.

Simply "ssh cardinal.stanford.edu" with your SUNetID credentials.

There are currently 3 cardinal machines: cardinal1, cardinal2 and cardinal3, load-balanced via cardinal.stanford.edu.

Things you can do on cardinal:

  • text based email clients
  • transfer files to and from AFS and your desktop
  • access command line utilities

corn info

The "corn" machines are general-purpose Ubuntu boxes and you can run whatever you want on them (so long as you don't negatively impact other users). Please read the policies and the motd first.

Simply "ssh corn.stanford.edu" with your SUNetID credentials.

Each of the 30 corn machines has 8 cores, 32GB RAM and ~70GB of local disk in /tmp.

Things you can do on corn:

  • email to stanford addresses only. for general text based email please use the cardinal
  • access command line utilities
  • access desktop environment via VNC
  • access developer toolchains (c/c++/java/python/go/julia/etc)
  • run licensed software (mathematica/stata/matlab/gaussian/sas/etc) as described here
  • submit jobs to the Barley cluster
  • basically anything you would do on a desktop Ubuntu system

other info:




rye info

Pymolgiffy.gif

The "rye" machines are general purpose Ubuntu (same as corn) but have 8 Nvidia GPU's each.

Things you can do on rye:

  • anything you can do on a corn
  • run CUDA 5.5/OpenCL programs (text based or GUI)
  • run 3D graphics programs such as PyMOL ------>




barley info

The "barley" machines are general-purpose newer Ubuntu boxes that can run jobs that you submit via the resource manager software. You should not log in to any barley directly, but can do so to troubleshoot your jobs.

Things you can do on barley:

  • cpu intensive jobs
  • large memory jobs (up to 192GB)
  • lots of cpu intensive jobs
  • thousands of cpu intensive jobs



Examples of using the barley cluster

  1. Introductory examples:
    1. Flac Like a Boss
    2. Cheap Flights
    3. San Francisco to Hong Kong in 5 minutes
    4. Monte Carlo Simulations in Matlab
  1. R
  2. MATLAB
  3. Access Mysql from Matlab
  4. MPI Abinit
  5. Rmpi
  6. Gaussian
  7. Ipython
  8. ANSYS
  9. Gaussview: Automated Submission Script Creation & Submission


FarmShare software

questions or requests about installed software

If you have a question or request about the installed software on FarmShare please email us: research-computing-support@stanford.edu

questions about how to use installed software

If you need help on usage we would suggest:

  • FarmShare mail list. FarmShare maillist has hundreds of other FarmShare users. It is quite likely somebody will be able to help. Please e-mail the FarmShare user community
  • SMACC. SMACC office hours is an excellent place to start if you would like to discuss or ask a question in-person. see SMACC

stock software

The FarmShare machines are running Ubuntu 13.04, and the software is from the Ubuntu repositories, e.g. run dpkg -l | grep ^i to see the list of installed packages.

If the package you're looking for isn't installed, search the Ubuntu Packages page and submit a HelpSU with the package name(s) you want.

licensed software

In addition to Ubuntu packages, the following packages are installed and are available via the "module" command:

See FarmShare_software for detailed examples.


$ module avail

---------------------------------- /farmshare/software/free/lmod-5.0-install/lmod/lmod/modulefiles/Core -----------------------------------
   lmod/lmod    settarg/settarg

------------------------------------------------------ /farmshare/software/mf/raring ------------------------------------------------------
   abinit/7.4.2       cudasamples/5.5                  julia/git11262013          povray/3.6.1                    statamp/12.1
   acml/5.3.1         farmvnc/0.1                      mathematica/9.0            sagemath/5.11                   statase/12.1
   ampl/20120629      farmvnc/0.2               (D)    matlab/r2012b              sas/9.2                         stattransfer/12
   atompaw/4.0.0.3    gams/24.1                        matlab/r2013a       (D)    sas/9.3
   cplex/12.4         gaussian/g09gview50       (D)    mzmine/2.10                sas/9.4                  (D)
   cuda/5.5           gaussian/g09sse4gview50          openmpi/1.6.5              sentarus/H_2013.03-SP1

-------------------------------------------------- /farmshare/software/mf/raring-compat ---------------------------------------------------
   ANSYS         GAMS-24.1        MATLAB-R2013a      SAS-v9.2            StataMP-12.1
   CPLEX-12.4    MATLAB-R2012b    Mathematica-9.0    StatTransfer-v12    StataSE-12.1

  Where:
   (D):  Default Module


See https://www.stanford.edu/group/farmshare/cgi-bin/wiki/index.php/FarmShare_software for description of how to use modules. 

A few commands to get you started: 

To load latest version of matlab: module load matlab 
You can load a specific version by: module load matlab/r2012b or module load MATLAB-R2012b

or to find out more information for a package: module spider matlab

Monitoring / Status

You probably want to try these commands:

 qstat -g c #cluster slots summary by queue
 qhost -F mem_free #show available memory on each host
 qstat -f -u \* #show all jobs in the system

For important announcements, we plan to:

  • add it to this wiki
  • modify /etc/motd on the corn machines
  • send a mail to farmshare-discuss

Mailing Lists

We have mailing lists, @lists.stanford.edu - https://itservices.stanford.edu/service/mailinglists/tools

Links

Want to learn HPC? Free education materials available:


FarmShare has GPUs, but if you need to scale up to a cluster ICME is an excellent resource:

Other GPU resources are:


Other similar wikis/clusters on campus (you might not have access to these):

Vision

The Farmshare resources are being made available to students, faculty and staff with fully sponsored SunetIDs to facilitate research at Stanford University.  This resource is designed so that those doing research will have a place to experiment and learn about technical solutions to assist in reaching their research goals without needing to write a grant for a cluster.  The Farmshare resources are focused on making it easier to learn how to parallelize research computing tasks and use research software including a "scheduler" or "distributed resource management system" to submit compute jobs.

By using Farmshare, new researchers can more easily adapt to using larger clusters when they have big projects that involve using federally funded resources, shared Stanford clusters, or on a small grant funded cluster.

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