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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

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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/6/6.0OpenACC

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title	GPU Nodes

Excerpt Include

	Getting Help
	Getting Help
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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.

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Cluster Information

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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.

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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.

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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:

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language	bash
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$ 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

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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

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
We support OpenACC in the GCC Compiler 6.1 which is automatically loaded as a module when you log

in

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:

is available from: https://gcc.gnu.org/wiki/OpenACC#Quick_Reference_Guide

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

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Applications

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

These include:

Application	Information	Access
NAMD

- installed

Installed as a module

;

	`$ module load namd`
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`
MATLAB	Installed 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 Fluent	Installed as a module and available as an application through Open OnDemand	`$ module load ansys`
RELION	Available as a Singularity container or as a module.	`$ module load relion`
ML and DL

frameworks -

Frameworks

See the

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.18.03-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.18.03-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.18.03.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.18.03-py3.simg

.

Python ML/DL including Nvidia RAPIDS

The minimum version of Python that is supported is 3.6:

Framework	Details
numba	RAPIDS: numba is for Cuda programming
cuml	RAPIDS: Cuda Machine Learning has many ML algorithms like K-means, PCA and SVM
cudf	RAPIDS: Cuda Dataframes supports loading and manipulating datasets
tensorflow	TensorFlow 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


torch	PyTorch supports tensor computation and deep neural networks

research.nvidia-theano.18.03.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

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:

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$ 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.


caffe2	A deep learning framework
tensorrt	Inference server for deep learning
tensorboard	Visualization tool for machine learning

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Versions Compared

Old Version 25

New Version 105

Key

Specifications

Cuda Modules

Overview

Compute Resources

Containers with GPU Support

Accessing GPUs

Training

Cluster Information

Puma

Ocelote

Cuda Modules

OpenACC

Applications

Machine Learning

Python ML/DL including Nvidia RAPIDS

Singularity

Training

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Versions Compared

Old Version 25

New Version 105

Key

Specifications

Cuda Modules

Overview

Compute Resources

Containers with GPU Support

Accessing GPUs

Training

Cluster Information

Puma

Ocelote

Cuda Modules

OpenACC

Applications

Machine Learning

Python ML/DL including Nvidia RAPIDS

Singularity

Training