This page is intended as a quick introduction for new users submitting their first job to the HPRC Cluster. A few things that new users should be aware of:
- Typically, jobs are not run in an interactive manner, except when:
- users are running small one off jobs
- evaluating the resources required for bigger jobs
- using graphical applications like MATLAB
- Examples of interactive jobs:
Content by Label showLabels false showSpace false labels interactive-cluster-job
- HPRC Cluster software is not run in a window on their desktop, neither is it launched by clicking on it in a network drive (see HPRC Fileshares).
- Users need to log into the cluster and inform the job scheduler about their job and it will run it when it can.
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The first step in using the HPRC Cluster is to log in to the login node -
The HPRC Cluster uses environment modules to manage the available software packages. This allows multiple versions of the same software to be installed without interfearing with each other. To enable the
environment module systemthe following command needs to be executed on the command line:
-bash-4.1$ source /etc/profile.d/modules.sh
The software that is available on the HPRC clusted is listed here: HPRC User Software. Alternately you can query the software available on the cluster with the following commands:
A list of available software is displayed
Version number and brief synopsis is displayed for
To run a job on the cluster create a shell script containing the PBS Directives containing the information required by the scheduler to schedule the job, and the job commands.
Example: paup witth the ML_analysis.nex sample file
It is important to match resources requested with the PBS Directives in your script and the resource usage of your job. There can be consequences for incorrectly specifiying these resource requirements
- Walltime: your job can be killed if it exceeds the specified wall time.
- Memory: overusing memory can cause the compute node's memory to be pushed into swap space, slowing down all jobs on that node. This has also killed compute nodes in the past, destroying
- CPUs: using more cpus than requested can slow down all jobs running on that node.