Cluster

General Information

Directory setup

home directory quota

There is a 10GB quota limit enforced on $HOME directory (/global/home/users/username) usage. Please keep your usage below this limit. There will NETAPP snapshots in place in this file system so we suggest you store only your source code and scripts in this area and store all your data under /clusterfs/cortex (see below).

In order to see your current quota and usage, use the following command:

 quota -s

data

For large amounts of data, please create a directory

 /clusterfs/cortex/scratch/username

and store the data inside that directory.

Connect

get a password

• Press the "PASSWORD" button to power on the CryptoCard. You will see "PIN?" request prompt
• Enter your PIN, and press the "ENT" key.
• You should see 7 digits presented like a phone number; this is your one-time password

ssh to the gateway computer (hadley)

note: please don't use the gateway for computations (e.g. matlab)!

 ssh -Y neuro-calhpc.berkeley.edu (or hadley.berkeley.edu) 

and use your crypto password

Setup environment

• put all your customizations into your .bashrc
• for login shells, .bash_profile is used, which in turn loads .bashrc

Useful commands

Start interactive session on compute node

• start interactive session:

 qsub -X -I

• start interactive session on particular node (nodes n0000.cortex and n0001.cortex have GPUs):

 qsub -X -I -l nodes=n0001.cortex

Perceus commands

The perceus manual is here

• listing available cluster nodes:

 wwstats

• list cluster usage

 wwtop

• to restrict the scope of these commands to cortex cluster, add the following line to your .bashrc

 export NODES='*cortex'

• module list
• module avail
• module help

• help pages are here

Resource Manager PBS

• Job Scheduler MOAB
• List running jobs:

 qstat -a

• List jobs of a given node:

 qstat -n 98

• sample script

 #!/bin/bash
 
 #PBS -q cortex
 #PBS -l nodes=1:ppn=2:cortex
 #PBS -l walltime=01:00:00
 #PBS -o path-to-output
 #PBS -e path-to-error
 cd /global/home/users/kilian/sample_executables
 cat $PBS_NODEFILE
 mpirun -np 8 /bin/hostname
 sleep 60

• submit script

 qsub scriptname

• interactive session

 qsub -I -q cortex -l nodes=1:ppn=2:cortex -l walltime=00:15:00

• flush STDOUT and STDERR to files in your home directory so you can tail the output of the job while it's running

 qsub -k oe scriptname

• remove a queued/running job (you can get the job_id from qstat)

 qdel job_id

• list nodes that your job is running on

 cat $PBS_NODEFILE

• run the program on several cores

 mpirun -np 4 -mca btl ^openib sample_executables/mpi_hello

Finding out the list of occupants on each cluster node

• One can find out the list of users using a particular node by ssh into the node, e.g.

 ssh n0000.cortex

• After logging into the node, type

 top

• This is useful if you believe someone is abusing the machine and would like to send him/her a friendly reminder.

Software

Matlab

note: remember to start an interactive session before starting matlab!

In order to use matlab, you have to load the matlab environment:

 module load matlab

Once the matlab environment is loaded, you can start a matlab session by running

 matlab -nodesktop

An example PBS script for running matlab code is

 #!/bin/bash
 #PBS -q cortex
 # request 1 nodes with 2 CPUs 
 #PBS -l nodes=1:ppn=2
 # reserve time on the selected cores
 #PBS -l walltime=01:00:00
 module load matlab
 matlab -nodisplay -nojvm << EOF
 test # here you should have whatever you would normally type in the Matlab prompt
 exit
 EOF

If you would like to see who is using matlab licenses, enter

 lmstat

Python

We have several Python Distributions installed: The Enthought Python Distribution (EPD), the Source Python Distribution (SPD) and Sage. The easiest way to get started is probably to use EPD (see below).

Enthought Python Distribution (EPD)

We have the Enthought Python Distribution 6.3.1 installed [EPD]. In order to use it, you have to follow the following steps:

• login to the gateway server using "ssh -Y" (see above)
• start an interactive session using "qsub -I -X" (see above)
• load the python environment module:

 module load python/epd

• start ipython:

 ipython -pylab

• run the following commands inside ipython to test the setup:

 from enthought.mayavi import mlab
 mlab.test_contour3d()

CUDA

CUDA is a library to use the graphics processing units (GPU) on the graphics card for general-purpose computing. We have a separate wiki page to collect information on how to do general-purpose computing on the GPU: GPGPU. We have installed the CUDA 3.0 driver and toolkit.

In order to use CUDA, you have to load the CUDA environment:

 module load cuda

Obtain GPU lock in python

If you would like to use one of the GPU cards on node n0000 or n0001, please optain a GPU lock to make sure the card is not in use and that no one else will be using the card.

If you are using Python, you can obtain a GPU lock by running

 import gpu_lock
 gpu_lock.obtain_lock_id()

The function either returns the number of the card you can use (0 or 1) or -1 if both cards are in use.

Obtain GPU lock for Jacket in Matlab

If you are using Matlab, you can obtain a GPU lock by running

 addpath('/clusterfs/cortex/software/gpu_lock');
 addpath('/clusterfs/cortex/software/jacket/engine');
 gpu_id = obtain_gpu_lock_id();
 gselect(gpu_id);

By default, obtain_gpu_lock() throws an error when all gpu cards are taken. There is another option: obtain_gpu_lock_id(true) will return -1 in case there is no card available and you can then write your own code to deal with that fact.

ginfo tells you which gpu card you are using.

The following lines should also be in your .bashrc

 ## jacket stuff!
 module load cuda
 export LD_LIBRARY_PATH=/clusterfs/cortex/software/jacket/engine/lib64:$LD_LIBRARY_PATH

CUDA SDK (Outdated since version change to 3.0)

You can install the CUDA SDK by running

 bash /clusterfs/cortex/software/cuda-2.3/src/cudasdk_2.3_linux.run

You can compile all the code examples by running

 module load X11
 module load Mesa/7.4.4
 cd ~/NVIDIA_GPU_Computing_SDK/C
 make

The compiled examples can be found in the directory

 ~/NVIDIA_GPU_Computing_SDK/C/bin/linux/release

note: The examples using graphics with OpenGL don't seem to run on a remote X server. In order to make them work, we probably need to install something like virtualgl.

Usage Tips

Here are some tips on how to effectively use the cluster.

Mounting Cluster File System

Mounting the cluster file system remotely allows you to easily access files on the cluster, and allows you to use local programs to edit code or examine simulation outputs locally (very useful). I often edit the remote code using a text editor running on my local machine. This allows you to take advantage of the niceties of a native editor without having to copy code back and forth before you run a simulation on the cluster.

On linux distributions you can mount your cluster home directory locally using sshfs [1]

 sshfs hadley.berkeley.edu: <mount-dir>

On Mac and Windows machines the program ExpanDrive works well (uses Fuse under the hood): [2]

Support Requests

• If you have a problem that is not covered on this page, you can send an email to our user list:

 redwood_cluster@lists.berkeley.edu

• If you need additional help from the LBL group, send an email to their email list. Please always cc our email list as well.

 scs@lbl.gov

• In urgent cases, you can also email Krishna Muriki (LBL User Services) directly.