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Cambridge pstack Deployment

If you're a member of the POETS project, you can access the POETS Cloud VMs hosted at Cambridge University. pstack has been deployed on few of these and is ready for use without requiring anything to be installed locally on your computer.

Deployment Information

The deployment consists of a Redis instance running on

  • coleridge.cl.cam.ac.uk

and three pd instances running on

  • byron.cl.cam.ac.uk (28 cores)
  • aesop.cl.cam.ac.uk (12 cores)
  • coleridge.cl.cam.ac.uk (12 cores).

Each one of the three machines has local installations of the entire pstack tool suite, but application tools (psim and pcli) are maintained only on Coleridge.

Getting Started

The easiest way to use pstack is to SSH into Coleridge (follow the instructions here to request access).

The tools psim and pcli are available to all users on Coleridge and do not require any setup. You can get started immediately after login using the respective user guides:

Running pcli Locally

Instead of running pcli on Coleridge, you may want to run it on your own computer, so you can load local XML files, run Python scripts, write simulation/benchmark results to disk or else. Luckily, this is very easy:

  1. Install pstack locally
  2. Open an SSH tunnel to forward the local Redis port (6379) to Coleridge
  3. Run and use pcli 🎉

Using pcli in this manner is possible because Redis is the central ingress point of a pstack deployment (see Organization). Forwarding local Redis traffic to a remote host will therefore make that deployment accessible to local instances of pcli.

Running pd Locally

Forwarding the local Redis port (6379) also makes it possible to add your own computer as an engine to the pstack deployment on Coleridge. After opening the SSH tunnel, simply run pd and your computer will be immediately available as an engine for all users. You can confirm this by looking at the engines section in the process viewer.

pd will use your computer's hostname as an engine name by default. It will also spawn n worker threads where n is the number of cores you have. These settings can be changed by passing command line arguments to pd, here's the usage string for reference:

POETS Daemon (PD) v0.1

Usage:
  pd.py [options]

Options:
  -w --workers=<n>   Specify number of workers (core count by default).
  -n --name=<name>   Specify engine name (hostname by default)
  -h --host=<host>   Specify Redis host [default: localhost].
  -p --port=<port>   Specify Redis port [default: 6379].
  -q --quiet         Suppress all outputs.

As you can see, it's also possible to specify the Redis host and port. You may be wondering why not use --host to point pd (and pcli) to coleridge.cl.cam.ac.uk instead of forwarding ports over SSH. There are two reasons for this:

  1. The Redis port on Coleridge is not exposed publicly to the Internet and so external connections will be blocked.

  2. Redis traffic is not encrypted so forwarding it through an SSH tunnel provides an encryption layer to secure exchanged data. This is good practice to adhere to when using remote deployments even if the Redis port is accessible publicly.

One thing to keep in mind when running pd locally is that traffic between your computer and the Cambridge machines will be significantly slower so simulations will take longer. If you care about performance you can use region constraints to make sure that multi-region simulations are constrained to individual machines.