Beagle Quick Start

2 minute read

Lately I have experienced traffic jam on our gizmo cluster and cannot acquired largenode without waiting for a couple days. So it is time for me to migrate to beagle - a bridge to AWS cloud computing. The complete beagle cluster user guide is here on SciComp’s wiki. Below is a just quick start. (click on the title to continue.)


From SciComp’s wiki:

Beagle can be seen as an extension of gizmo into AWS cloud infrastructure. For this, we intended to make transitioning as easy as possible, so we’ve retained the familiar Slurm workload manager as well as extending on-campus storage to this system. Thus, your files- shared fast directories, your home directory, the shared app tools directory- area all available at the same paths as with gizmo compute nodes.

Thus, all that is really necessary are small changes to the Slurm commands to enable your jobs to run on beagle nodes.

However: it needs to be noted that as access to data is much slower than your access here on campus. On IO intensive workloads you may see up to a 3x slowdown on overall time to complete a job.

Basic use

To summit a job, use sbatch in a similar manner as on gizmo and add -M beagle to the commend:

sbatch -M beagle -n1

This way you are partitioning one F class note (16GB RAM) and will share CPU and memory with other jobs.

Managing jobs

Similarly, use squeue, sacct and scancel and add -M beagle to the commend..

squeue -M beagle -u my_username

Note that srun and salloc do not support the -M option: for interactive use you need to first log into the host fitzroy where you can run any of the Slurm commands without -M.


There are three classes available: F, G and H, each of which has 16GB, 60GB and 244GB, respectively. The default partition is campus which contains only F class nodes. The other two classes of nodes are in the largenode partition.

Class CPUs RAM Partition
F 4 16GB campus, c5.2xlarge
G 18 60 GB largenode, c5.9xlarge
H 16 244GB largenode, r4.8xlarge

Use -p <partition name> to select the partition. When selecting the largenode partition you will get a G node unless you request more memory than available on the G class.


Limits on beagle are enforced in the same way as they are on gizmo: a core limit per PI. The limits are typically higher and can be increased upon request.


  • Partition on G class with one task and have the whole RAM:
    sbatch -M beagle --exclusive -n1 -p largenode

    With --exclusive you get the whole computer with 18 cores and 60 GB RAM (if partitioning on G class) and will not share with others.

  • Partition on G class with one task and a few cores:
    sbatch -M beagle -n1 -c4 -p largenode

    Without using --exclusive, you may share CPU and memory with other submitted jobs.

  • Get a larger allocation on an H node
    sbatch -M beagle --mem=200G -p largenode

Please edit this page is you have more useful information.

By Chao-Jen Wong