Supported Resources and Technologies

Edit this Page via GitHub       Comment by Filing an Issue      Have Questions? Ask them here.

The Fred Hutch provides researchers on campus access to high performance computing using on-premise resources. The various technologies provided are outlined on here along with the basic information required for researchers to identify which resource might be best suited to their particular computing needs.

The Fred Hutch managed systems listed serve needs that rise above those that can be met using your desktop computer or web-based services. Often reasons to move to these high performance computing (HPC) resources include:

  • reproducible compute jobs
  • version controlled and/or specialized software
  • increased compute capability
  • rapid access to large data sets in central data storage locations

Overview of On-Premise Resources

Compute Resource Access Interface Resource Admin Connection to FH Data Storage
Gizmo Via Rhino or NoMachine hosts (CLI, FH credentials on campus/VPN off campus) Scientific Computing Direct to all local storage types
Beagle Via Rhino or NoMachine hosts (CLI, FH credentials on campus/VPN off campus) Center IT home, fast, economy, AWS-S3, and Beagle-specific scratch
Rhino CLI, FH credentials on campus/VPN off campus Scientific Computing Direct to all local storage types
NoMachine NX Client, FH credentials on campus/VPN off campus Scientific Computing Direct to all local storage types
Python/Jupyter Notebooks Via Rhino (CLI, FH credentials on campus/VPN off campus) Scientific Computing Direct to all local storage types
R/R Studio Via Rhino (CLI, FH credentials on campus/VPN off campus) Scientific Computing Direct to all local storage types

Gizmo and Beagle Cluster

While we generally don’t recommend interactive computing on the HPC clusters- interactive use can limit the amount of work you can do and introduce “fragility” into your computing- there are many scenarios where interactively using cluster nodes is a valid approach. For example, if you have a single task that is too much for a rhino, opening a session on a cluster node is the way to go.

If you need an interactive session with dedicated resources, you can start a job on the cluster using the command grabnode. The grabnode command will start an interactive login session on a cluster node. This command will prompt you for how many cores, how much memory, and how much time is required

This command can be run from any NoMachine or rhino host.

NOTE: at this time we aren’t running interactive jobs on Beagle nodes. If you have a need for this, please email scicomp.

Available Resources

VMs, shiny, rancher, data transfer

Community Resources (not specifically supported by IT)

Are there things people use that we don’t really support?

Proposal Preparation

A description of computational and storage resources from Scientific Computing for grant writers can be found here.

<!– ## Self Service Resources Jupyterhub, RStudio, db4sci, Galaxy, etc.

Gory Details on Node Classes

Resource Table

This table is auto-generated based on the yaml in _data/scicomp_resources.yaml, and is a work in progress.

Name Type Authentication Authorization Location
rstudio web web hutchnetID FHCRC
proxmox VM cluster web hutchnetID FHCRC

Cluster Node Table

This table is auto-generated based on the yaml in _data/cluster_nodes.yaml:

GIZMO

Location: FHCRC

Partition Node Name Node Count CPU Cores Memory
campus f 456 Intel E3-1270v3 4 32GB
largenode g 18 Intel E5-1234v666 6 256GB
largenode h 3 Intel E5-1234v666 14 768GB
none (interactive use) rhino 3 Intel E5-1234v666 14 384GB

Additional resources

Node Name Network Local Storage
f 1G (up to 100MB/s throughput) 800GB @ /loc (ca 100 MB/s throughput)
g 10G (upto 1GB/s throughput) 5TB @ /loc (300MB/s throughput / 1000 IOPS) and 200GB @ /loc/ssd (1GB/s throughput / 500k IOPS)
h 10G (upto 1GB/s throughput) 5TB @ /loc (300MB/s throughput / 1000 IOps) and 200GB @ /loc/ssd (1GB/s throughput / 500k IOPS)
rhino 10G (up to 1GB/s throughput) 5TB @ /loc (300MB/s throughput/ 1000 IOps)

BEAGLE

Location: AWS

Partition Node Name Node Count CPU Cores Memory
campus f 777 Intel c5 4 15GB
largenode g 103 Intel c5 18 60GB
largenode h 34 Intel r4 16 244GB

Additional resources

Node Name Network Local Storage
f EC2 EBS
g EC2 EBS
h EC2 EBS

Updated:

Edit this Page via GitHub       Comment by Filing an Issue      Have Questions? Ask them here.