The University of Arizona
    For questions, please open a UAService ticket and assign to the Tools Team.
Page tree

Versions Compared

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.



Column
width
50%
30%
toc
Image Added



Column
width
50%

Image Removed

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

Image Removed

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/8.0.61cuda75/fft/7.5.18cuda75/profiler/7.5.18cuda80/fft/8.0.61 cuda80/profiler/8.0.61cuda75/gdk/352.79cuda75/toolkit/7.5.18cuda80/gdk/352.79 cuda80/toolkit/8.0.61/cm/shared/uamodulefilescuda75/neuralnet/5/5.1cuda75/neuralnet/6/6.0cuda80/neuralnet/5/5.1cuda80/neuralnet/6/6.0

OpenACC

70%

Table of Contents


Overview

Compute Resources

More detailed information on system resources can be found on our Compute Resources page.

Containers with GPU Support

Singularity containers are available as modules on HPC for GPU-supported workflows. For more information, see our documentation on Containers.

Accessing GPUs

Information on how to request GPUs using SLURM can be found in our SLURM Documentation.

Training

For a list of training resources related to GPU workflows, see our Training documentation.



Cluster Information

Puma

Puma has a different arrangement for GPU nodes than Ocelote and ElGato. Whereas the older clusters have one GPU per node, Puma has four. This has a financial advantage for providing GPU's with lower overall cost, and a technical advantage of allowing jobs that can use multiple GPU's to run faster than spanning multiple nodes.  This capability comes from using a newer operating system.  
Each node has four Nvidia V100S model GPUs. They are provisioned with 32GB memory compared to 16GB on the P100's.
Image Added


Ocelote

Ocelote has 45 compute nodes with Nvidia P100 GPUs that are available to researchers on campus. The limitation is a maximum of 10 concurrent jobs. One node with a V100 is also available. Since there is only one, you can feel free to use it for testing and comparisons to the P100, but production work should be run on the P100's. There is also one node with two P100's for testing jobs that use two GPU's. This one should be used to compare with running a job on two nodes.  

Image Added



Cuda Modules

Warning

Nvidia Nsight Compute (the interactive kernel profiler) is not available. In response to a security alert (CVE-2018-6260) this capability is only available with root authority which users do not have. 


The latest Cuda module available on the system is 11.0 and is the only version until newer ones come along. The Cuda driver version can be queried with the nvidia-smi command. To see the modules available, in an interactive session simply run:

Code Block
languagebash
themeMidnight
$ module avail cuda

-------------------- /opt/ohpc/pub/moduledeps/gnu8-openmpi3 --------------------
   cp2k-cuda/7.1.0

-------------------------- /opt/ohpc/pub/modulefiles ---------------------------
   cuda11-dnn/8.0.2    cuda11-sdk/20.7    cuda11/11.0





OpenACC

The OpenACC API is a collection of compiler directives and runtime routines that allow you to specify loops and regions of code in standard C, C++, and Fortran that you can offload from a host CPU to the GPU.

We provide two methods of support for OpenACC

  1. We support OpenACC in the PGI Compiler.  The PGI implementation of OpenACC is considered the best implementation.  
    "module load pgi" on Ocelote. If you are on a GPU node from an interactive session you can run "pgaccelinfo" to test functionality.  Remember that the login nodes do not have GPUs or software installed.  
    A useful getting-started guide written by Nvidia is available here: https://www.pgroup.com/doc/openacc17_gs.pdf 

  2. We support OpenACC in the GCC Compiler 6.1 which is automatically loaded as a module when you log
in
  1. into Ocelote.  Verify with "module list".
    The GCC 6 release includes a much improved implementation of the OpenACC 2.0a specification.
    A useful quick reference guide
can be  found at:
 

About two times a year we host the Xsede Workshop on Programming GPU's with OpenACC.  Watch for announcements to the HPC-Info list.

Nvidia has available free online OpenACC courses:
https://developer.nvidia.com/openacc/overview
https://developer.nvidia.com/openacc-courses 




Applications

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

 

These include:

ApplicationInformationAccess
NAMD
- installed
Installed as a module
;
$ module load namd
_cuda
VASP
-
A restricted license version is installed
on Ocelote
; only available to
the
licensed users$ module load vasp
GROMACS
-
Installed as a module
on Ocelote;
$ module load gromacs
LAMMPS
-
Installed as a module
on Ocelote;
$ module load lammps
/gcc/16Mar18
ABAQUS
- Installed
Installed as a module
on Ocelote;
and available as an application through Open OnDemand$ module load abaqus
GAUSSIAN
MATLAB -
Installed as a module. See these notes.$ module load gaussian/g16
MATLABInstalled as a module and available as an application through Open OnDemand. Review the GPU Coder
at their web siteAMBER
on their website$ module load matlab
ANSYS FluentInstalled as a module and available as an application through Open OnDemand$ module load ansys
RELIONAvailable as a Singularity container or as a module.$ module load relion
ML and DL
frameworks -
FrameworksSee the
next
section below

*** Nvidia Provided GPU Codes ***

Some of these codes are provided as Singularity containers from Nvidia.

NAMD and GROMACS can be found at /unsupported/singularity/nvidia along with a README file for each.

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  

Current list:

nvidia-caffe.18.06-py2.simgCaffe is a deep learning framework made with expression, speed, and modularity in mind. It was originally developed by the Berkeley Vision and Learning Center (BVLC) nvidia-pytorch.18.06-py3.simg

PyTorch is a Python package that provides two high-level features:

  • Tensor computation (like numpy) with strong GPU acceleration
  • Deep Neural Networks built on a tape-based autograd system

nvidia-mxnet.18.06.simg

MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix the flavors of symbolic programming and imperative programming to maximize efficiency and productivity.nvidia-tensorflow.18.06-py3.simg
.


Python ML/DL including Nvidia RAPIDS 

The minimum version of Python that is supported is 3.6:

FrameworkDetails
numbaRAPIDS: numba is for Cuda programming
cumlRAPIDS: Cuda Machine Learning has many ML algorithms like K-means, PCA and SVM
cudfRAPIDS: Cuda Dataframes supports loading and manipulating datasets
tensorflowTensorFlow is an open source software library for numerical computation using data flow graphs.
TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and
torchPyTorch supports tensor computation and deep neural networks
research.nvidia-theano.18.06.simgTheano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.

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

Either: 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.
Or: run the singularity file from where it is.  Since you cannot modify it you will not interfere with anyone else. 

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

Code Block
$ 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:

Code Block
$ 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

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

There are more detailed examples here

Singularity

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

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

There are tutorials for Singularity on HPC here

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.
caffe2A deep learning framework
tensorrtInference server for deep learning
tensorboardVisualization tool for machine learning