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Linux Self Guided 

We run RHEL/CentOS 6 Linux on our high-performance systems.

If you have never used Linux before or have had very limited use, read this useful guide:


If you have learned Linux in the past but want a quick reference to the syntax of commands, then read this:

Bash Cheat Sheet






Intel® Modern Code Training

Intel brought a workshop to campus in 2014 and the material is covered here.  If you want to do any work on the Intel® Xeon Phi™ Coprocessors we have 40 of them installed in ElGato.  You can obtain "standard" queue access and can request access to the nodes with them installed. 

Created by Colfax International and Intel, and based on the book, Parallel Programming and Optimization with Intel® Xeon Phi™ Coprocessors, this short video series provides an overview of practical parallel programming and optimization with a focus on using the Intel® Many Integrated Core Architecture (Intel® MIC Architecture).

Length: 5 hours

Parallel Programming and Optimization with Intel Xeon Phi Coprocessors


Intel® Software Tools

Intel offers the Cluster Studio XE.  On Ocelote we have installed modules (module avail intel ) as:

  • intel-cluster-checker/2.2.2

  • intel-cluster-runtime/ia32/3.8

  • intel-cluster-runtime/intel64/3.8

  • intel-cluster-runtime/mic/3.8

We have installed the Intel high performance libraries (module avail intel ):

  • Intel® Threading Building Blocks
  • Intel® Integrated Performance Primitives
  • Intel® Math Kernel Library
  • Intel® Data Analytics Acceleration Library

The University is licensed and has access to this toolset separate from HPC.   Portions of it are FREE for use in teaching/instruction and to students.









Singularity containers let users run applications in a Linux environment of their choosing.  This is different from Docker which is not appropriate for HPC due to security concerns.  Singularity is like a container for Docker images, but is not just for Docker.

For an overview and more detailed information refer to:

Here are some of the use cases we support using Singularity:

  • You already use Docker and want to run your jobs on HPC
  • You want to preserve your environment so that a system change will not affect your work
  • You need newer or different libraries than are offered on HPC systems
  • Someone else developed the workflow using a different version of linux
  • You prefer to use something other than Red Hat / CentOS, like Ubuntu 

Centos with Tensorflow Example

This is an example of creating a singularity image to run code that is not supported on HPC.  This example uses Tensorflow but any application could be installed in its place.  It also uses CentOS but it could just as easily be Ubuntu.

  1. Install Singularity on linux workstation -

  2. Create the container using a size of 1500MB on a Centos workstation / VM with root privileges

    singularity create -s 1500 centosTFlow.img
    # Create an image file to host the content of the container.  
    # Think of it like creating the virtual hard drive for a VM.
    # In ext3, an actual file of specified size is created. 
  3. Create the definition file, in this example called centosTFlow.def

  4. Bootstrap process creates the installation following the definition file

    singularity bootstrap centosTFlow.img centosTFlow.def

  5. Copy the new image file to your space on HPC.  /extra might be a good location as the image might use up your remaining home.  There is a line in the definition file that will create the mount for /extra.  Any time you run from a location other than /home on ElGato you are likely to see a warning which you can ignore:

    WARNING: Not mounting current directory: user bind control is disabled by system administrator
  6. Test with a simple command

    $module load singularity
    $singularity exec centosTFlow.img python --version
    Python 2.7.5
  7. Or slightly more complex create a simple python script called

    $singularity exec centosTFlow.img python /extra/netid/
    Hello World: The Python version is 2.7.5 
    $singularity shell centosTFlow.img
    Hello World: The Python version is 2.7.5 
  8. And now test tensorflow with this example from their web site,

    $singularity exec centosTFlow.img python /extra/netid/
    (0, array([-0.08299404], dtype=float32), array([ 0.59591037], dtype=float32))
    (20, array([ 0.03721666], dtype=float32), array([ 0.3361423], dtype=float32))
    (40, array([ 0.08514741], dtype=float32), array([ 0.30855015], dtype=float32))
    (60, array([ 0.09648635], dtype=float32), array([ 0.3020227], dtype=float32))
    (80, array([ 0.0991688], dtype=float32), array([ 0.30047852], dtype=float32))
    (100, array([ 0.09980337], dtype=float32), array([ 0.3001132], dtype=float32))
    (120, array([ 0.09995351], dtype=float32), array([ 0.30002677], dtype=float32))
    (140, array([ 0.09998903], dtype=float32), array([ 0.30000633], dtype=float32))
    (160, array([ 0.0999974], dtype=float32), array([ 0.3000015], dtype=float32))
    (180, array([ 0.09999938], dtype=float32), array([ 0.30000037], dtype=float32))
    (200, array([ 0.09999986], dtype=float32), array([ 0.3000001], dtype=float32)) 

Docker Example


This example is taken from the Singularity documentation and modified for our HPC. The example taken is tensorflow again but it could be PHP or any other Docker image

  1. Create the Singularity container on the workstation or VM where you have root authority:

    $singularity create --size 4000 docker-tf.img
  2. Import the Docker Tensorflow workflow from the Docker hub:

    $singularity import docker-tf.img docker://tensorflow/tensorflow:latest
    Cache folder set to /root/.singularity/docker
    Downloading layer sha256:a3ed95caeb02ffe68cdd9fd84406680ae93d633cb16422d00e8a7c22955b46d4
    Extracting /root/.singularity/docker/sha256:a3ed95caeb02ffe68cdd9fd84406680ae93d633cb16422d00e8a7c22955b46d4.tar.gz
    Downloading layer sha256:65f3587f2637c17b30887fb0d5dbfad2f10e063a72239d840b015528fd5923cd
    Extracting /root/.singularity/docker/sha256:56eb14001cebec19f2255d95e125c9f5199c9e1d97dd708e1f3ebda3d32e5da7.tar.gz
    Bootstrap initialization
    No bootstrap definition passed, updating container
    Executing Prebootstrap module
    Executing Postbootstrap module
  3. Test the new image:

    [user@host]$ singularity shell docker-tf.img
    Singularity: Invoking an interactive shell within container...
    Singularity.docker-tf.img> python 
    Python 2.7.6 (default, Oct 26 2016, 20:30:19) 
    [GCC 4.8.4] on linux2
    Type "help", "copyright", "credits" or "license" for more information.
    >>> import tensorflow
    >>> exit()
    Singularity.docker-tf.img> exit
    user@host$ singularity exec docker-tf.img python /extra/netid/ 
    WARNING:tensorflow:From in <module>.: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
    Instructions for updating:
    Use `tf.global_variables_initializer` instead.
    (0, array([ 0.72233653], dtype=float32), array([-0.00956423], dtype=float32))
    (20, array([ 0.24949318], dtype=float32), array([ 0.22735602], dtype=float32))
    (40, array([ 0.13574874], dtype=float32), array([ 0.28262845], dtype=float32))
    (60, array([ 0.10854871], dtype=float32), array([ 0.2958459], dtype=float32))
    (80, array([ 0.1020443], dtype=float32), array([ 0.29900661], dtype=float32))
    (100, array([ 0.10048886], dtype=float32), array([ 0.29976246], dtype=float32))
    (120, array([ 0.10011692], dtype=float32), array([ 0.29994321], dtype=float32))
    (140, array([ 0.10002796], dtype=float32), array([ 0.29998642], dtype=float32))
    (160, array([ 0.10000668], dtype=float32), array([ 0.29999676], dtype=float32))
    (180, array([ 0.1000016], dtype=float32), array([ 0.29999924], dtype=float32))
    (200, array([ 0.10000039], dtype=float32), array([ 0.29999983], dtype=float32))





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