The teaching cluster is meant as a resource for students and instructors for computational computing. The teaching cluster is a full HPC cluster and students are allowed to run jobs on the head node. Students must contact instructors for course related questions and support.
Example Use Cases
As a student, I want to learn how to login and run a simple program on an HPC cluster with minimal setup time and effort.
As an instructor, I want a resource to teach my students about the different functions of high performance computing without needing to spend a lot of time to set up accounts and get the students logged in.
Service Policies
For Instructors:
The teaching cluster provided to all UM students, students must be official UM students with an PawPrint or UM SSO ID.
The environment is “research grade”. No backups or high availability. Students/TA’s are responsible for backing up any data throughout the semester.
Only infrastructure support is provided, there is no student/end-user support. All support requests should come through the instructor or TA’s via rcss-support@missouri.edu. Support is best effort and provided during regular business hours.
Software is limited to CentOS 7 packages installed via yum that require minimal configuration and a subset of Lewis scientific packages.
We do not support a development environment/IDE. Users need to use either sftp or emacs/vi/nano or other console based text editors. For windows users we have a site license for MobaXterm that can be downloaded from the software center.
We take security seriously. We upgrade the entire environment (including rebooting) on a regular basis and without notice. SELinux is enforced.
Students must be made aware of the “Teaching Cluster Policy” and the limitations of the environment.
For Students:
Use of this system is governed by the rules and regulations of the University of Missouri and the University of Missouri System.
Users must be familiar with and abide by the UM System acceptable use policy (CRR 110.005) and the UM System Data Classification System (DCL)[1]. Collected Rules and Regulations – Chapter 110, Data Classification System[2].
Only DCL 1 data is permitted on the cluster Data Classification System – Definitions[3].
This is a shared environment with limited storage, RAM, or CPU with no quotas. Please be nice.
Data is not backed up and all data deleted when students graduate. This policy may be revised.
Students must contact instructors for course related questions and support.