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= How to get help =
= How to get help =
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You can e-mail research-computing-support@stanford.edu
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*You can e-mail research-computing-support@stanford.edu
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**If you're e-mailing about a barley job, please mention that it's on barley and the job number.
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You can come to office hours.  Wed 1-3PM in Huang basement in front of ICME door.
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*You can come to office hours.  Wed 1-3PM in Huang basement in front of ICME door.
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Revision as of 11:08, 13 May 2013

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

    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 /mnt/glusterfs on all corn and barley nodes.  Login to senpai1.stanford.edu and a directory will be created for you as /mnt/glusterfs/<your user name> (can take up to 5 minutes).  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

    • 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.
    • You can come to office hours. Wed 1-3PM 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 1-3 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.

    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.

    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.

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

    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.

    barley info

    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. Rmpi
    5. Gaussian
    6. Ipython
    7. ANSYS
    1. Gaussview: Automated Submission Script Creation & Submission

    FarmShare software

    Please note that we provide suppport on the installation and availability of software packages, but we generally don't provide support on the usage of the software. If you need help on usage, please e-mail the FarmShare user community or submit a support ticket with the appropriate vendor. Alternatively, the SSDS campus group can provide guidance for R, SAS and Stata. Please contact them directly if you have any questions about using those particular software packages.

    stock software

    The FarmShare machines are running Ubuntu, 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

    As of April 2012, we're transitioning the location of licensed software into /mnt/glusterfs/software/ and using modules. As of Feb 13, 2013, the software that is available is:

    ---------------------------------------------- /mnt/glusterfs/software/free/modules/tcl/modulefiles -----------------------------------------------
    AMPL                  GAMS-23.8.1-AFS       MATLAB-R2012a         R-2.15.0              abinit-6.12.3         
    ANSYS                 GAMS-23.8.2-GlusterFS MATLAB-R2012b         R-2.15.1-precise      abinit-7.0.5          
    ATK                   IMSL                  Mathematica-8.0.4     StatTransfer-11.2     abinit-7.0.5-barley   
    CMTK-2.2.4            MATLAB-R2010b         NAG-C-23              StataMP-12.1          gephi-0.8.1-beta      
    CPLEX_Studio-12.4     MATLAB-R2011b         NAG-gfortran-23       StataSE-12.1          opensim-3.0           
    

    Licensed software that hasn't been transitioned yet is still available in /usr/sweet/bin. Older versions of some of the same programs as above may also be available here.

    # ls /usr/sweet/bin/
    MathKernel@   cplexamp@      g03@        hlm2@      lmutil@       mex@        sicstus@  stata@
    Mathematica@  cplexconvert@  g09@        hlm3@      maple@        mint@       spdet@    stata-se@
    Splus@        dbmscopy@      gams@       hmlm@      math@         rats@       spld@     tracker@
    ampl@         dbmsnox@       gamsbatch@  hmlm2@     mathematica@  ratsgraph@  splfr@    xdisplay@
    anshelp@      eqs@           gamslib@    launcher@  matlab@       rcomp@      splm@     xmaple@
    ansys@        f95@           gview@      limdep@    mbuild@       rgf2pst@    splus@    xstata@
    cplex@        f95mcheck@     hcm2@       lmgrd@     mcc@          sas@        spxref@   xstata-se@
    

    Monitoring / Status

    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:


    GPUs! We don't have any GPUs as part of FarmShare, but there are other campus resources available:


    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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