HPC Cluster Basic Use Guide
The CHTC has partnered with the UW-Madison
Advanced Computing Initiative
(ACI) in order to provide a dedicated high-performance computing (HPC) cluster meant for large,
singular computations that use specialized software to achieve internal parallelization of work across
multiple servers of dozens to hundreds of cores. All other computational work, including that in the form or single
or multiple compute jobs that each fit on a single server or core are more appropriate for our larger high-throughput
computing (HTC) system, which also includes specialized hardware for extreme memory, GPUs, and other cases.
Before using the campus-shared HPC Cluster or any CHTC computing resource, you will need to
obtain access by
filling out the
Large-Scale Computing Request Form on our website so that our Research Computing Facilitators
can make sure to match you to the best computing resources (including non-CHTC services).
If you have been using the HPC Cluster already, and believe your research group would benefit from
purchasing prioritized hardware to add to the HPC Cluster, please see our
information regarding buy-in options for access to your own priority queue.
- Cluster Configuration and Policies
- Basic Use of the Cluster
- Checking Home Directory Usage
Cluster Configuration and Policies
A. Hardware and Partition Configuration
The HPC Cluster servers consist of two head nodes and many compute nodes
("servers") of memory and multiple CPU cores. There is one queue with access to separate "partitions"
of hardware, including the largest partitions that are available to
anyone on campus (for free).
Our first generation nodes ("univ" partition) each have 16 CPU cores of
2.2 GHz, and 64 GB of RAM (4 GB per CPU core). Our second generation nodes
(in the "univ2" partition) each have 20 CPU cores of 2.5 GHz, and 128 GB of
RAM. All users log in at a head node, and all user files on the shared file
sytem (Gluster) are accessible on all nodes. Additionally, all nodes are
tightly networked (56 Gbit/s Infiniband) so they can work together as a
single "supercomputer", depending on the number of CPUs you specify.
Because the HPC Cluster is specifically intended for singular compute jobs requiring multiple nodes of
cores to complete in a reasonable amount of time, users running work in the form of single-node (or smaller) computations will
be contacted to move such work to our larger and more appropriate HTC System. Otherwise, such numerous smaller jobs impede
the effectiveness of the cluster for running larger jobs.
B. Logging In
You may log in to the cluster, submit jobs, and transfer/move data through either head node
(aci-service-1.chtc.wisc.edu or aci-service-2.chtc.wisc.edu).
DO NOT RUN PROGRAMS ON THE HEAD NODES.
Simple commands (to compress data, create directories, etc.) that run within a
few minutes on the head node are okay, but any scripts, software, or other processes
that perform data manipulations/creation are VERY likely to kill the head nodes, creating
signficant issues for all active users and CHTC staff.
To ensure proper functioning of the cluster for ALL users, computational work should
always be run within an interactive session (see below) or
batch job. CHTC staff reserve the right to kill any long-running or problematic processes
on the head nodes and/or disable user accounts that violate this policy,
and users may not be notified of account deactivation. Processes that only occasionally
perform work (e.g. crontab, etc.) are still a violoation of this policy.
If you are not able to log into the cluster, please contact us at email@example.com.
Only ssh connections from an on-campus network are allowed, so
you will need to first connect to an on-campus server with ssh or Virtual Private Network
(VPN) before connecting to either HPC head node, when off-campus. (The Division of IT provides the
WiscVPN, which will work for users with a UW NetID.)
C. Data Storage
Data space in the HPC file system is not
backed-up and should be treated as temporary by users. Only files
necessary for actively-running jobs should be kept on the file system,
and files should be removed from the cluster when jobs complete. A copy of any
essential files should be kept in an alternate, non-CHTC storage location.
Each user is initially allocated 100 GB of data storage space and a file count quota
of 50,000 files/directories in their home directory
(/home/username/). To check how many files and directories you have
in your home directory (and subdirectories), see the instructions below.
Increased quotas are available upon email request to
firstname.lastname@example.org. In your request, please include both size (in GB) and file/directory
If you don't know how many files your installation creates, because it's more than 50,000,
simply indicate that in your request.
CHTC Staff reserve the right to remove any significant amounts of data
on the HPC Cluster in our efforts to maintain
filesystem performance for all users, though we will always first ask users
to remove excess data and minimize file counts before taking additional action.
Local scratch space of 500 GB is available on each execute node in
/scratch/local/ and is automatically cleaned out upon completion of scheduled
job sessions (interactive or non-interactive). Local scratch is available on
the compiling node, aci-service-2, in the same location and should be cleaned
out by the user upon completion of compiling activities. CHTC staff will otherwise
clean this location of the oldest files when it reaches 80% capacity.
- software packages installed in your home directory (if any)
- the input and output for a single job
D. Partition Configuration and Job Scheduling
The job scheduler on the HPC Cluster is SLURM. You can read more about
submitting jobs to the queue on
SLURM's website, but we have provided a simple guide below for getting
We have provisioned 3 freely-available submission partitions and
a small set of nodes prioritized for interactive testing. These
partitions can be thought of as different queues, and are
selected by the user at the time of job submission.
To promote fairness, there is a 600-core running limit per-user across
the entire cluster of partitions, with rare exceptions for researchers
who own more than this number of cores. Additionally, each user
may only have 10 jobs running at once. Users with many smaller (1-node or
2-node) jobs will find that they experience better throughput on CHTC's
high-throughput computing (HTC) system, and can email email@example.com to
|| # nodes (N)
|| max nodes/job
|| cores/node (n)
|| RAM/node (GB)
||16 or 20
||64 or 128
||16 or 20
||64 or 128
*note: jobs not requesting a run time will be alotted the default value
(t-default) for that partition; jobs without a partition indicated will be run
in the "univ" partition.
The University (univ) partition is available to all UW-Madison
researchers, and jobs are run without being pre-empted for the
duration of time requested. This partition is best for running longer (multi-day)
jobs on any number of CPUs and will always have at least 32 nodes (512 cores),
but usually much more.
The Owner partitions actually consist of multiple group-specific
partitions for research groups who have paid into the cluster for a set number
of nodes. Each owner partition will have unique settings, and owned
nodes are backfilled by jobs from the "pre" queue.
The Interactive (int) partition consists of a few nodes meant for short
and immediate interactive testing on a single node (up to 16 CPUs, 64 GB RAM).
There is a specific command to access the "int" partition:
srun -n16 -N1 -p int --pty bash
The Pre-emptable (pre) partition is under-layed on the entire cluster
and is meant for more immediate turn-around of shorter and somewhat smaller jobs,
or for interactive sessions requiring more than the 30-minute limit of the "int"
Pre-emptable jobs will run on any idle nodes (primarily Owner nodes, as the
University partition is likely to be full), but will be pre-empted by jobs
of other partitions with priority on those nodes. However, pre-empted jobs will
be re-queued if originally submitted with an sbatch script (see below).
Job Priority Determinations
A. User priority decreases as the user accumulates hours of CPU time over the
last 21 days, across all queues. This "fair-share" policy means that users who
have run many/larger jobs in the near-past will have a lower priority, and users
with little recent activity will see their waiting jobs start sooner. We do NOT
have a strict "first-in-first-out" queue policy.
B. Job priority increases with job wait time. After the history-based user
priority calculation in (A), the next most important factor for each job's priority
is the amount of time that each job has already waited in the queue. For all
the jobs of a single user, these jobs will most closely follow a
C. Job priority increases with job size, in cores. This least important factor
slightly favors larger jobs, as a means of somewhat countering the inherently
longer wait time necessary for allocating more cores to a single job.
Basic Use of the Cluster
1. Log in to the cluster head node
Create an ssh connection to aci-service-1.chtc.wisc.edu using your UW-Madison
username and associated password.
Checking partition availabilty
To see partitions that you can submit to, use the following command:
Using the "
-a" argument to "
sinfo" will show ALL
2. Software Capabilities
As part of our overall strategy for enabling users through computing, we
actually encourage users to install and compile their desired software (and
version), as they wish, within the /home/username location. Compiling, like any
other computational work is best-performed in an interactive session. If your compilation
will take more than the 30 minutes allowed on our interactive ("int") partition, alter the
interactive job command below to submit to "univ" or "univ2" to have more time. Please email
firstname.lastname@example.org if you can't find the compiler you need or have other issues.
For more specific details on compiling and running MPI code
Please see our HPC Cluster MPI Use Guide for information about the availability
of specific libraries and how to load modules for them.
3. Submitting jobs
A. Requesting an Interactive Job ("int" and "pre" partitions)
You may request up to a full node (16 CPUs, 64 GB RAM) when requesting an
interactive session in the "int" partition. Interactive sessions on the "int"
partition are allowed for 30 minutes, but you may request less time
(see the below example). Sessions in the "pre" partition are limited according to
the "Partition" table above, but are potentially subject to interruption.
[alice@service]$ srun -n16 -N1 -p int --pty bash
The above example indicates a request for 16 CPUs (
-n16) on a
single node (
-N1) in the "int" partition (
-p int), and
-t 15" would indicate a request for 15 minutes, if desired rather than the
30-minute default. After the interactive shell is created to a compute node with
the above command, you'll have access to files on the shared file system
and be able to execute code interactively as if you had
directly logged in to that node. It is important to exit the interactive shell
when you're done working by typing
B. Submitting a Job to the Queue (all partitions)
To submit jobs to the queue for a given partition such that a connection
to the jobs is not maintained, you should use
You will first want to create an
sbatch script, which is
is essentially just a shell script (sh, bash, etc.) with
The following example requests a job slot with 16 CPU cores
on each of 2 nodes (32 cores total) for 4 hours and 30 minutes:
#This file is called submit-script.sh
#SBATCH --partition=univ # default "univ", if not specified
#SBATCH --time=0-04:30:00 # run time in days-hh:mm:ss
#SBATCH --nodes=2 # require 2 nodes
#SBATCH --ntasks-per-node=16 # (by default, "ntasks"="cpus")
#SBATCH --mem-per-cpu=4000 # RAM per CPU core, in MB (default 4 GB/core)
#Make sure to change the above two lines to reflect your appropriate
# file locations for standard error and output
#Now list your executable command (or a string of them).
# Example for non-SLURM-compiled code:
module load mpi/gcc/openmpi-1.6.4
mpirun -n 32 /home/username/mpiprogram
You can then submit the script with the following command:
[alice@service]$ sbatch submit-script.sh
Other lines that you may wish to add to your script for specifying a number of total
tasks (equivalent to "cores" by default),
desired CPU cores per task (for multiple CPU cores per MPI task),
or total RAM per node are:
#SBATCH --mem=4000 # RAM per node, in MB (default 64000/node, max values in partition table)
#SBATCH --ntasks=32 # total number of "tasks" (cores) requested
#SBATCH --cpus-per-task=1 # default "1" if not specified
In any case, it is important to make sure that your request fits within the hardware
configuration of your chosen partition.
C. Using srun and salloc
In early tests with the cluster, we encouraged running non-interactive jobs
without an sbatch script; however, doing so
creates and requires a persistent connection to your job as it runs, and
interrupted jobs are not re-queued if submitted this way (even when using
the "pre" partition). You are welcome to submit jobs in
these modes according to
guide, which has some awesome advanced features for complex MPI configurations.
Please remember to indicate partition and run time with the -p and
-t flags, respectively (see the interactive job command, above, for an
example using these flags).
4. Viewing jobs in the queue
To view your jobs in the SLURM queue, enter the following:
[alice@service]$ squeue -u username
will show all user jobs in the queue. You can view all jobs for a particular
partition with "
squeue -p univ"
5. Removing jobs
squeue, you can kill and/or remove your job from
the queue with the following:
[alice@service]$ scancel job#
job# is the number shown for your job in the
Checking Home Directory Usage
In order to check how many files and directories are contained in your
home directory, as well as the total amount of space used, we recommend using
the Linux tool
To check data usage and file counts, run
ncdu from within
the directory you'd like to query. Example:
$ cd /home/alice
ncdu has finished running,
the output will give you a total file count and allow you to navigate between
subdirectories for even more details. Type
q when you're ready to
exit the output viewer. More info here:
du can also be used to examine the size of
$ df -h /home/alice