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Ocelote has 46 new compute nodes with Nvidia P100 GPU's.  These are available to researchers on campus.  There will be fairshare limitations but the intention is for them to be as widely available as possible. There are still compute nodes on El Gato with 70 nodes provisioned with Nvidia Tesla K20's. 

Specifications


Cuda Modules

Currently the following Cuda modules are available on Ocelote:

/cm/shared/modulefiles




cuda75/blas/7.5.18 cuda75/nsight/7.5.18cuda80/blas/8.0.61cuda80/nsight/8.0.61
cuda75/fft/7.5.18cuda75/profiler/7.5.18cuda80/fft/8.0.61 cuda80/profiler/8.0.61
cuda75/gdk/352.79cuda75/toolkit/7.5.18cuda80/gdk/352.79 cuda80/toolkit/8.0.61
/cm/shared/uamodulefiles


cuda75/neuralnet/5/5.1cuda75/neuralnet/6/6.0cuda80/neuralnet/5/5.1cuda80/neuralnet/6/6.0

Applications

Many applications have been optimized to run faster on GPU's.  These include:

  • NAMD
  • VASP
  • GROMACS
  • LAMMPS
  • GAUSSIAN
  • MATLAB
  • AMBER
  • ANSYS Fluent
  • ML and DL frameworks

Machine Learning 

*** Nvidia Provided GPU Codes ***


Nvidia builds the popular set of ML and DL frameworks which is not a trivial task. They have made them available to us and they will be updated regularly.  They are currently located at:
/unsupported/singularity/nvidia  

Each is provided in a Singularity container.
The file name has a tag at the end that represents when it was made, so 18.01 is January 2018

USAGE

Copy the file you wish to use to your directory.  Your home path as well as /extra and /xdisk have been bound to the image, so those are your choices.


For interactive use, start an interactive job on a GPU node modifying this command:

$ qsub -I -N jobname -m bea -W group_list=GROUP-NAME -q windfall -l select=1:ncpus=28:mem=168gb:ngpus=1 -l cput=1:0:0 -l walltime=1:0:0

You must change the group_list and you should change the other attributes as desired.

On the compute node assigned to you, as an example you can run:

module load singularity
singularity exec --nv nvidia-tensorflow.18.01-py3.simg python tensorflow_example.py

You need to include the --nv and note it has two dashes.  This will bind the Cuda libraries.
The example file is included in this directory. "tensorflow_example.py"

For batch use, you will include these two lines in your submission script

module load singularity
singularity exec --nv nvidia-tensorflow.18.01-py3.simg python tensorflow_example.py


SINGULARITY

For more information on Singularity, see their web site at:

http://singularity.lbl.gov/user-guide

Training

We host workshops from the Pittsburgh Supercomputer Center which is a NSF funded location.  We are working with Nvidia to offer a workshop in the April 2018 timeframe.

Watch for announcements from the hpc-info list.

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