Infolab cluster

It was recently decided that having a split personality is not the best thing to have in a cluster as two different resource managers start to compete while not being aware of each other. That is why we have separated our cluster into a Compute cluster and a Hadoop cluster. Read on for more about the two.

Mailing list

There is a mailing list for all those interested in what is currently happening with the cluster and the configuration of the cluster:

• List address: ilcluster@lists.stanford.edu

• Manage your subscription: https://mailman.stanford.edu/mailman/listinfo/ilcluster

Compute cluster

The compute cluster comes in handy whenever you need a lot of cores to get your job done. It is just like looking at the CPU and memory load of the other servers and then deciding which one to use for your job, only the job schedule will take care of looking at the CPU load for you and schedule the resources on a first come first serve basis (at least for the time being, queue priorities may change in the future).

Hardware

• 1 head node: ilhead1
• 27 compute nodes: iln1 - iln28 (iln25 currently offline)
  • 768 CPU cores
  • 1728 GB RAM

Software

• Torque resource manager
• MAUI job scheduler
• CentOS 6.3

Resources

• Using the compute cluster

Hadoop cluster

If you want to run map/reduce jobs then this is the cluster for you.

Hardware

• 1 head node: ilhadoop1
• 39 nodes: ilh01 - ilh40 (ilh19 currently offline)
  • 312 CPU cores
  • 2496 GB RAM
  • 312 TB raw storage

Software

• Cloudera CDH 5.4.2
• CentOS 6.3

Resources

• Hadoop Cluster Statistics

• Using the Hadoop cluster