What is Beocat?
Beocat is the High-Performance Computing (HPC) cluster at Kansas State University. It is run by the Institute for Computational Research, which is a function of the Computer Science department. Beocat is available to any educational researcher in the state of Kansas (and his or her collaborators) without cost. Priority access is given to those researchers who have contributed resources.
Beocat is actually comprised of several different cluster computing systems
- "Beocat", as used by most people is a Beowulf cluster of CentOS Linux servers coordinated by the Slurm job submission and scheduling system. Our Compute Nodes (hardware) and installed software have separate pages on this wiki. The current status of this cluster can be monitored by visiting http://ganglia.beocat.ksu.edu/.
- A comparatively small Hadoop cluster
- A small Openstack cloud-computing infrastructure
How Do I Use Beocat?
First, you need to get an account by visiting https://account.beocat.ksu.edu/ and filling out the form. In most cases approval for the account will be granted in less than one business day, and sometimes much sooner. When your account has been approved, you will be added to our LISTSERV, where we announce any changes, maintenance periods, or other issues.
Once you have an account, you can access Beocat via SSH and can transfer files in or out via SCP or SFTP (or Globus Connect using the endpoint beocat#beocat). If you don't know what those are, please see our LinuxBasics page. If you are familiar with these, connect your client to headnode.beocat.ksu.edu and use your K-State eID credentials to login.
As mentioned above, we use Slurm for job submission and scheduling. If you've never worked with a batch-queueing system before, submitting a job is different than running on a standalone Linux machine. Please see our SlurmBasics page for an introduction on how to submit your first job. If you are already familiar with Slurm, we also have an AdvancedSlurm page where we can adjust the fine-tuning. If you're new to HPC, we highly recommend the Supercomputing in Plain English (SiPE) series by OU. In particular, the older course's streaming videos are an excellent resource, even if you do not complete the exercises.