HPC system: Teton

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The Teton HPC cluster is the successor to Mount Moran. Teton contains several new compute nodes. All Mount Moran nodes have been reprovisioned within the Teton HPC Cluster. The system is available by SSH using hostname teton.arcc.uwyo.edu or teton.uwyo.edu. We ask that everybody who uses ARCC resources cite the resources accordingly. See Citing Teton. Newcomers to research computing should also consider reading the Research Computing Quick Reference.


Teton is a Intel x86_64 cluster interconnected with Mellanox InfiniBand and has a 1.3 PB IBM Spectrum Scale global parallel filesystem available across all nodes. The system requires UWYO two-factor authentication (2FA) for login via SSH. The default shell is BASH with Lmod modules system is leveraged for dynamic user environments to help switch software stacks rapidly and easily. The Slurm workload manager is employed to schedule jobs, provide submission limits, and implement fairshare as well as provide the Quality of Service (QoS) levels for research groups who have invested in the cluster. Teton has a Digital Object Identifier (DOI) (https://doi.org/10.15786/M2FY47) and we request that all use of Teton appropriately acknowledges the system. Please see Citing Teton for more information.

Available Nodes

Type Series Arch Count Sockets Cores Threads / Core Clock (GHz) RAM (GB) GPU Type GPU Count Local Disk Type Local Disk Capacity (GB) IB Network Operating System
Teton Regular Intel Broadwell x86_64 180 2 32 1 2.1 128 N/A N/A SSD 240 EDR RHEL 7.4
Teton BigMem GPU Intel Broadwell x86_64 8 2 32 1 2.1 512 NVIDIA P100 16G 2 SSD 240 EDR RHEL 7.4
Teton HugeMem Intel Broadwell x86_64 10 2 32 1 2.1 1024 N/A N/A SSD 240 EDR RHEL 7.4
Teton KNL Intel Knights Landing x86_64 12 1 72 4 1.5 384 + 16 N/A N/A SSD 240 EDR RHEL 7.4
Teton DGX Intel Broadwell x86_64 1 2 40 2 2.2 512 NVIDIA V100 32G 8 SSD 7 TB EDR Ubuntu 16.04 LTS
Moran Regular Intel Sandbridge/Ivybridge x86_64 283 2 16 1 2.6 64 or 128 k20 on some 2 HD 1T FDR RHEL 7.4
Moran BigMem Intel Sandbridge/Ivybridge x86_64 2 2 16 1 2.6 512 K80 8 HD 1T FDR RHEL 7.4
Moran Debug Intel Sandbridge/Ivybridge x86_64 2 2 16 1 2.6 64 k20m 2 HD 1T FDR RHEL 7.4
Moran HugeMem Intel Sandbridge/Ivybridge x86_64 2 2 16 1 2.6 1024 k20 2 HD 1T FDR RHEL 7.4
Moran DGX Intel Broadwell x86_64 1 2 40 2 2.2 512 NVIDIA V100 16G 8 SSD 7 TB EDR Ubuntu 16.04 LTS
TOTAL Nodes 501

See Partitions for information regarding Slurm Partitions on Teton.

Global Filesystems

The Teton global filesystem is configured with ~160 TB SSD tier for active data and 1.2 PB HDD capacity tier. The system policy engine moves data automatically between pools. The system will automatically migrate data to HDD when the SSD tier reaches 70% used capacity. Teton has several spaces that are available for users to access described in the table below.

  • home - /home/username ($HOME)
- Space for configuration files and software installations. This file space is intended to be small and always resides on SSDs. The /home file space is snapshotted to recover from accidental deletions.
  • project - /project/project_name/[username]
- Space to collaborate among project members. Data here is persistent and is exempt from purge policy.
  • gscratch - /gscratch/username ($SCRATCH)
- Space to perform computing for individual users. Data here is subject to a purge policy defined below. Warning emails will be sent when possible deletions may start to occur. No snapshots.
Global Filesystems
Filesystem Quota (GB) Snapshots Backups Purge Policy Additional Info
home 25 Yes No No Always on SSD
project 1024 No No No Aging data will move to HDD
gscratch 5120 No No Yes Aging data will move to HDD

Purge Policy - File spaces within the Teton cluster filesystem may be subject to a purge policy. The policy has not yet been defined. However, ARCC reserves the right to purge data in this area after 30 to 90 days of no access or from creation time. Before performing an actual purge event, the owner of the file(s) will be notified by email several times for files which are subject to being purged.

Storage Increases

  • Project PIs can purchase additional scratch and/or project space at a cost of $100 / TB / year.
  • Additionally, researchers can request allocation increases at no cost for scratch and/or project space by submitting proposals that must be renewed every 6 months and include the following information:
    • the scientific gain and insights that will be or have been obtained by using the system,
    • how data is organized and accessed in efforts to maximize performance and usage.
  • To request more information, please contact ARCC.

Special Filesystems

Certain filesystems exist on different nodes of the cluster where specialized requirements exist. The table below summarizes these specialized filesystems.

Specialty Filesystems
Filesystem Mount Location Notes
petaLibary /petalibrary/homes Only on login nodes
/petalibrary/Commons Only on login nodes
Bighorn /bighorn/home Only on login nodes, read-only
/bighorn/project Only on login nodes, read-only
/bighorn/gscratch Only on login nodes, read-only
node local scratch /lscratch Only on compute nodes; Moran is 1 TB HDD; Teton is 240 GB SSD
memory filesystem /dev/shm RAM based tmpfs available as part of RAM for very rapid I/O operations; small capacity

The node local scratch or lscratch filesystem is purged at the end of each job.

The memory filesystems can really enhance performance of small I/O operations. If you have localized single node I/O jobs that have very intensive random access patterns, this filesystem may improve performance of your compute job.

The petaLibrary filesystems are only available from the login nodes, not on the compute nodes. A storage space on the Teton global filesystems does not imply storage space on the ARCC petaLibrary or vice versa. For more information about the petaLibrary please see the following link petaLibrary

The Bighorn filesystems will be provided for a limited amount of time in order for researchers to move data to either the petaLibrary, Teton storage or to some other storage media. The actual date that these mounts will be removed is still TBD.

Project and Account Requests

For research projects, UWYO faculty members (Principal Investigators) can request a Project be created on Teton. PIs can then add access to the project for UWYO students, faculty and external collaborators. User Accounts on Teton require a valid UWYO e-mail address and an UWYO-Affiliated PI sponsor. UWYO faculty members can sponsor their own accounts, while students, post-doctoral researchers, or research associates must use their PI as their sponsor. Non-UWYO external collaborators must be sponsored by a current UWYO faculty member.

Follow this link Account_Policy for addition information and policy statements on accout usage. Use the link under "Account Requests" to request that either a project or user(s) be created or added. From this same page you can request that users be added to an existing project.

Note, that for external collaborators a special UWYO account must be created by the ASO office before access can be granted to Teton. There is a one time $10 fee for having these account created. Please allow extra time for the ASO office to create the account.

Please go to this web page to request a project be setup, ARCC Access Request Form.

Once the form is submitted, and the information verified, the project and user account(s) will be created. Users will receive email notification once a project has been created and/or when they are added to a project.

To request access for instructional use, send email to arcc-info@uwyo.edu with the course number, section and student list. If the PI prefers generic accounts can be created instead of providing a student list. Instructional accounts are usually valid for a single semester and access to the project is terminated at the beginning of the next semester.

System Access

SSH Access

Teton has login nodes for users to access the cluster. Login nodes are available publicly using the hostname teton.arcc.uwyo.edu or teton.uwyo.edu. SSH can be done natively on MacOS or Linux based operating systems using the terminal and the ssh command. Although X11 forwarding is supported, and if you need graphical support, we recommend using FastX if at all possible. Additionally, you may want to configure your OpenSSH client to support connection multiplexing if you require multiple terminal sessions. For those instances where you have unreliable network connectivity, you may want to use either tmux or screen once you login to keep sessions alive during disconnects. This will allow you to later reconnect to these sessions.

ssh USERNAME@teton.arcc.uwyo.edu
ssh -l USERNAME teton.arcc.uwyo.edu
ssh -Y -l USERNAME teton.arcc.uwyo.edu                          # For secure forwarding of X11 displays
ssh -X -l USERNAME teton.arcc.uwyo.edu                          # For forwarding of X11 displays

OpenSSH Configuration File (BSD,Linux,MacOS)

By default, the OpenSSH user configuration file is $HOME/.ssh/config which can be edited to enhance workflow. Since Teton uses round-robin DNS to provide access to two login nodes and requires two-factor authentication, it can be advantageous to add SSH multiplexing to your local environment to make sure subsequent connections are made to the same login node. This also provides a way to shorten up the hostname and access methods for SCP/SFTP/Rsync capabilities. An example entry looks like where USERNAME would be replaced by your actual UWYO username:

Host teton
  Hostname teton.arcc.uwyo.edu
  controlmaster auto
  controlpath ~/.ss/ssh-%r@%h:%p

WARNING: While ARCC allows SSH multiplexing, other research computing sites may not. Do not assume this will always work on systems not administered by ARCC.

Access from Microsoft Windows

ARCC currently recommends that users install MobaXterm to access the Teton cluster. It provides appropriate access to the system with SSH and SFTP capability, allowing X11 if required. The home version of MobaXterm should be sufficient. There is also PuTTY if a more minimal application is desired.

Addtional options include, a cygwin installation with SSH installed or the Windows Subsystem for Linux with an OpenSSH client installed on very recent versions of windows, enabling the OpenSSH client. Finally, a great alternative is to use our FastX capability.

FastX Access

If your currently on the UW campus, you can also leverage FastX to provide you with a more robust remote graphics capability via a installable client for Windows, Mac, or Linux or through a web browser. Navigate to https://fastx.arcc.uwyo.edu and log in with your 2FA credentials. There are also native clients for FastX for Windows, MacOS, and Linux which can be downloaded here. For more information, see the documentation on using FastX.

Available Shells

Teton has several shells available for use. The default is bash]. To change your default shell, please submit the request through standard ARCC request methods.

Shell Path Version Notes
bash /bin/bash 4.2.46 Recommended
zsh /bin/zsh 5.0.2
csh /bin/csh 6.18.01 Implemented by TCSH
tcsh /bin/tcsh 6.18.01

Data Transfer & Access

  1. Teton Cluster Filesystem
    1. SMB / CIFS Access
    2. NFS Access
  2. ARCC Bighorn (Mt Moran) Filesystem
  3. ARCC petaLibrary Filesystem

Job Scheduling Slurm

  1. Required Inputs and Default Values and Limits
    1. Default Values
    2. Default Limits
  2. Partitions

Running jobs on the Teton cluster require the user to specify a list of partitions a job may run in. The user should use "moran,teton" or "teton,moran" most of the time. The order specifies the order SLURM searches for nodes to allocate.

Speciality partitions are used to specify particular resources, i.e. GPU nodes that require them.

  1. General Partitions
  2. Investor Partitions
  3. Special Partitions

Purchasing Investor Nodes for Teton

Quick Links

Here are some quick links to some additional documentation on using the system.

Base Operations

Access to software

Schedule jobs and query system

Workflow Software

  • SSH Connection Multiplexing
  • Software Multiplexers - Keep your sessions alive

Programming on HPC cluster

GPUs and Accelerators

The ARCC Teton cluster has a number of compute nodes that contain GPUs. This section describes the hardware, as well as access and usage of the GPU nodes.

Teton GPU Hardware

The following tables list each node that has GPUs and the type of GPU installed.

Table #1

Node GPU Type Number of Devices GPU Memory Size (GB) Compute Capability GRES Flag Teton Partition Notes
m025 K20m 2 4 3.5 gpu:k20m:{1-2} Yes
m026 K20m 2 4 3.5 gpu:k20m:{1-2} Yes
m027 K20m 2 4 3.5 gpu:m20m:{1-2} Yes
m028 K20m 2 4 3.5 gpu:k20m:{1-2} Yes
m029 K20m 2 4 3.5 gpu:k20m:{1-2} Yes
m030 K20m 2 4 3.5 gpu:k20m:{1-2} Yes
m031 K20m 2 4 3.5 gpu:k20m:{1-2} Yes
m032 K20m 2 4 3.5 gpu:k20m:{1-2} Yes
m075 K20m 2 4 3.5 gpu:k20m:{1-2} Yes
m076 K20m 2 4 3.5 gpu:k20m:{1-2} Yes
m077 K20m 2 4 3.5 gpu:k20m:{1-2} Yes
m078 K20m 2 4 3.5 gpu:k20m:{1-2} Yes
m079 K20m 2 4 3.5 gpu:k20m:{1-2} Yes
m080 K20m 2 4 3.5 gpu:k20m:{1-2} Yes
m081 K20m 2 4 3.5 gpu:k20m:{1-2} Yes
m082 K20m 2 4 3.5 gpu:k20m:{1-2} Yes
m083 K20m 2 4 3.5 gpu:k20m:{1-2} Yes Disabled due to memory ECC errors
m084 K20m 2 4 3.5 gpu:k20m:{1-2} Yes Disabled due to memory ECC errors
m085 K20m 2 4 3.5 gpu:k20m:{1-2} Yes Disabled due to memory ECC errors
m086 K20m 2 4 3.5 gpu:k20m:{1-2} Yes
m219 K20Xm 2 5 3.5 gpu:k20xm:{1-2} Yes
m220 K20Xm 2 5 3.5 gpu:k20xm:{1-2} Yes
m227 K20Xm 2 5 3.5 gpu:k20xm:{1-2} Yes
m228 K20Xm 2 5 3.5 gpu:k20xm:{1-2} Yes
m235 K20Xm 2 5 3.5 gpu:k20xm:{1-2} Yes
m236 K20Xm 2 5 3.5 gpu:k20xm:{1-2} Yes
m243 K20Xm 2 5 3.5 gpu:k20xm:{1-2} Yes
m244 K20Xm 2 5 3.5 gpu:k20xm:{1-2} Yes
m251 K20Xm 2 5 3.5 gpu:k20xm:{1-2} Yes
m252 K20Xm 2 5 3.5 gpu:k20xm:{1-2} Yes
m259 K20Xm 2 5 3.5 gpu:k20xm:{1-2} Yes
m260 K20Xm 2 5 3.5 gpu:k20xm:{1-2} Yes
m267 K20Xm 2 5 3.5 gpu:k20xm:{1-2} Yes
m268 K20Xm 2 5 3.5 gpu:k20xm:{1-2} Yes
mdbg01 GTX Titan X 1 12 5.2 gpu:TitanX:{1-1} Yes
GTX Titan 2 6 6.0 gpu:Titan:{1-2} Yes
mdbg02 K40c 2 11 3.5 gpu:k40c:{1-2} Yes
GTX Titan X 2 12 5.2 gpu:TitanX:{1-2} Yes
mbm01 K80 8 11 3.7 gpu:k80:{1-8} No
mbm02 K80 8 11 3.7 gpu:k80:{1-8} No
tbm03 Tesla P100 2 16 6.0 gpu:P100:{1-2} No
tbm04 Tesla P100 2 16 6.0 gpu:P100:{1-2} No
tbm05 Tesla P100 2 16 6.0 gpu:P100:{1-2} No
tbm06 Tesla P100 2 16 6.0 gpu:P100:{1-2} No
tbm07 Tesla P100 2 16 6.0 gpu:P100:{1-2} No
tbm08 Tesla P100 2 16 6.0 gpu:P100:{1-2} No
tbm09 Tesla P100 2 16 6.0 gpu:P100:{1-2} No
tbm10 Tesla P100 2 16 6.0 gpu:P100:{1-2} No

The following two GPU nodes are reserved for AI use.

Table #2

Node GPU Type Number of Devices GPU Memory Size (GB) Compute Capability GRES Flag Teton Partition Notes
mdgx01 Tesla V100 8 16 7.0 gpu:V100-16g:{1-8} No
tdgx01 Tesla V100 8 32 7.0 gpu:V100-32g:{1-8} No

For more information on GPU capabilities please visit Nvidia's web page: [1]

For additional information about CUDA programming visit Nivida's CUDA C Programming Guide: [2]

Access and running jobs

There are three different types of GPU nodes in the Teton cluster and they are requested in somewhat different ways.

  • Public Nodes: Public nodes are available to the general user and are in the "teton" partition. These nodes are identified in table #1 last column with a "yes". Use the following partition request to access these nodes.
  • Reserved Nodes: Reserved nodes are available to the general user and must be specifically requested via a partition request, i.e. "teton-gpu". These nodes are identified in table #1 last column with a "no". Use the following partition request to access these nodes.
  • Speciality Nodes: These are speciality nodes that are available to special users and are requested via a partition request, i.e. "dgx", see table #2 above. Use the following partition request to access these nodes.

If one wants to access the GPU devices on a node one MUST explicitly specify the generic consumable resources flag ("gres" flag). The "gres" flag has the following syntax:



  • resource_type is always equal to gpu string for the GPU devices.
  • resource_name is a string which describes the type of the requested gpu(s) e.g. k80, titanx, k20m, ....
  • resource_count is the number of gpu devices that are requested of the type resource_name. Its value is an integer in the closed interval: {1,max. number of devices on a node}

The "gres" flag attached to each type of node can be found in the second-to-last column of Table 1. For example, the flag --gres=gpu:titanx:1 must be used to request one (1) GTX Titan X device that can only be satisfied by the nodes with the GTX Titan X in them.

If you run a job that requires GPUs and you fail to specify the "gres" flag, your job will be assigned any node in the requested partition. This means your job will possible not have access to GPUs as part of your job.

One way to verify that your job has access to GPUs within a node you can execute the following command:


An empty output string implies NO access to the node's GPU devices.

Some programs are serial, or able to run only on a single GPU; other jobs perform better on a single or small number of GPUs and therefore cannot efficiently make use of all of the GPUs on a single node. In order to better utilize our GPU nodes, node sharing has been enabled.. This allows multiple jobs to run on the same node, each job being assigned specific resources (number of cores, amount of memory, number of accelerators). The node resources are managed by the SLURM scheduler up to the maximum available on each node. It should be noted that while efforts are made to isolate jobs running on the same node, there are still many shared components in the system. Therefore a job's performance can be affected by other job(s) running on the node at the same time.

Node sharing can be accessed by requesting less than the full number of GPUs, CPUs or memory. Note that node sharing can also be done on the basis of the number of CPU's and/or memory, or all three. By default, each job gets 3.5 GB of memory per core requested (the lowest common denominator among our cluster nodes), therefore to request a different amount than the default amount of memory, you must use the "--mem" flag . To request exclusive use of the node, use "--mem=0".

Example #1

An example script that would request two Teton nodes with 2xK20m GPU's, including all cores and all memory, running one GPU per MPI task, would look like this:

#SBATCH --nodes=2
#SBATCH --mem=0
#SBATCH --partition=teton
#SBATCH --account=<account>
#SBATCH --gres=gpu:k20m:2
#SBATCH --time=1:00:00
... Other job prep
srun myprogram.exe

Example #2

To request all 8 K80 GPUs on a Teton node, again using one GPU per MPI task, we would do:

#SBATCH --nodes=1
#SBATCH --mem=0
#SBATCH --partition=teton
#SBATCH --account=<account>
#SBATCH --gres=gpu:k80:8
#SBATCH --time=1:00:00
... Other job prep
srun myprogram.exe

Example #3

Another example, using the job script below will get four GPUs, four CPU cores, and 8GB of memory. The remaining GPUs, CPUs, and memory will then be accessible for other jobs.

#SBATCH --ntasks=4
#SBATCH --nodes=1    
#SBATCH --mem=8
#SBATCH --partition=teton
#SBATCH --account=<account>
#SBATCH --gres=gpu:k80:4
#SBATCH --time=00:30:00 
... Other job prep
srun myprogram.exe

Example #4

To run a parallel interactive job with MPI, do not use the usual "srun" command, as this does not work properly with the "gres" request. Instead, use the "salloc" command, e.g.

salloc -n 1 -N 1 -t 1:00:00 -A <account> -p teton-gpu  --gres=gpu:p100:1

This will allocate the resources to the job, but keeps the prompt on the login node. You can then use "srun" or "mpirun" commands to launch the calculation on the allocated compute node resources.

For serial jobs, utilizing one or more GPUs, "srun" works properly, e.g.

srun -n 1 -N 1 -t 1:00:00 -A <account> -p teton-gpu --gres=gpu:p100:1 --pty /bin/bash -l

GPU programming environment

On Teton Nvidia CUDA, PGI CUDA Fortran and the OpenACC compilers are installed. The default CUDA is 9.2.88, which at the time of writing is the most recent. You can access by simply loading the CUDA module, "module load cuda". PGI compilers come with their own CUDA which is quite recent, and can be set access by loading the PGI module, using "module load pgi".

Any login node should work to compile your CUDA code as the CUDA tools are avaiable from the login nodes. PGI compilers come with their own CUDA so compiling anywhere from where you can load the PGI module should work.

To compile CUDA code using the CUDA compiler "nvcc" so that it runs on all types of GPUs that ARCC has, use the following compiler flags:

 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_70,code=sm_70

For more info on the CUDA compilation and linking flags, please have a look at [3].

The PGI compilers specify the GPU architecture with the -tp=tesla flag. If no further option is specified, the flag will generate code for all available computing capabilities (at the time of writing cc35,cc37, cc50, cc60 and cc70). To be specific for each GPU:

GPU Type Compiler Flag
K20m -tp=tesla:cc35
K20Xm -tp=tesla:cc35
Titan -tp=tesla:cc60
Titan X -tp=tesla:cc50
K40c -tp=tesla:cc35
K80 -tp=tesla:cc35
P100 -tp=tesla:cc60
V100 -tp=tesla:cc70

To invoke OpenACC, use the "-acc" flag. More information on OpenACC can be obtained at http://www.openacc.org.

A good tutorial on GPU programming is available at the CUDA Education and Training site from Nvidia.

When running the GPU code, it is worth checking the resources that the program is using, to ensure that the GPU is well utilized. For that, one can run the nvidia-smi command, and watch for the memory and CPU utilization. nvidia-smi is also useful to query and set various features of the GPU, see "nvidia-smi --help" for all the options that the command accepts.

For example, "nvidia-smi -L" lists the GPU card properties. On Teton node m025 you should see:

userX@m025:~# nvidia-smi -L
GPU 0: Tesla K20m (UUID: GPU-2e23ddef-1d96-7894-102a-0458da3faaa4)
GPU 1: Tesla K20m (UUID: GPU-458a86ec-09cd-64d1-475a-d36dc0a73b4f)


Nvidia's CUDA distribution includes a terminal debugger named cuda-gdb. Its operation is similar to the GNU gdbdebugger. For details, see the cuda-gdb documentation.

For out of bounds and misaligned memory access errors, there is the cuda-memcheck tool. For details, see the cuda-memcheck documentation.

The Allinea DDT debugger that we currently license also support CUDA and OpenACC debugging. Due to its user friendly graphical interface we recommend them for GPU debugging. For information on how to use DDT or Totalview, see our debugging page.


Profiling can be very useful in finding GPU code performance problems, for example inefficient GPU utilization, use of shared memory, etc. Nvidia CUDA provides both command line (nprof) and visual profiler (nvvp). More information is in the CUDA profilers documentation.

Installed GPU codes

Trouble Shooting



Hardware errors are a natural issue when dealing with bare metal servers and equipment. The following are some general hardware trouble shooting issues and what can be done to resolve the issues.

Issue: Node is taken offline for an error by the check hardware script.

Reason: The BMC is showing a hardware fault on the node.

hardware error at ... dimm #8

Resolution process:

1. Reseat the node to clear the error. 2. run "memtester" to see if you can artificially generate the issue. 3. If the problem returns, swap dimms to see if problem follows the dimm or is slot related. 4. If it follows the dimm, replace the dimm. 5. If the error stay with the dimm slot, if available call hardware repair from vendor.

Extra Help

  • Requesting software builds
  • Requesting project accounts
  • Requesting user accounts
  • Requesting class accounts
  • Requesting increased storage allocation