HTC User Guide

HTC stands for High Throughput Computing and is characterized by using many computing resources to solve time intensive problems efficiently.

This cluster is designed to remotely run high throughput computing jobs, with particular emphasis on supporting bioinformatics and health science research. 

With a CRC user account and basic familiarity towards Linux, you can access the cluster's login node. From here, you will be able to view your files and dispatch jobs to one or several other nodes across the cluster called compute nodes. The tool we use to submit these jobs is called a job scheduler. All compute nodes on a cluster mount a shared filesystem; a file server or set of servers that keeps track of all files on a large array of disks, so that you can access and edit your data from any compute node.

The first step in gaining access to our clusters is to request an account.

• If you are a university faculty member you can request an allocation for yourself and colleagues in your lab.
• If you are lab staff or a student, you can have your PI sponsor your user account on their allocation.

There is no charge for using these clusters.

HTC cluster is accessed via a protocol called secure shell (SSH). Once you have an account, you can logon to the cluster using your Pitt credentials.

If you want to access the clusters from outside of PittNet, you must use the VPN.

To best serve the diverse needs of all our researchers, we use a module system to manage the most commonly used software. This allows you to swap between different applications and versions of those applications with relative ease and focus on getting your work done.

On our clusters, you control your jobs using a job scheduling system called Slurm that dedicates and manages compute resources for you. Schedulers are usually used in one of two ways. For testing and small jobs you may want to run a job interactively. This way you can directly interact with the compute node(s) in real time to make sure your jobs will behave as expected. The other way, which is the preferred way for long-running jobs, involves writing your job commands in a script and submitting that to the job scheduler.